At age 16 and 18 all young people will make decisions that have lifelong consequences

Future Frontiers

In our digital world, continually menace the safety of individuals and corporations alike. Phishing, an insidious tactic whereby cybercriminals manipulate unsuspecting users into divulging sensitive information, is especially prevalent. This deceptive practice continually evolves, growing more sophisticated with each passing day. To counter this escalating threat, the field of cybersecurity has witnessed remarkable technological progress, the aim of which is to keep pace with these malicious advances and offer effective mitigation strategies. cyber threats Artificial Intelligence and machine learning in the Cyber Arms Race The advent of the internet marked the beginning of an ongoing cyber arms race. In the early days, basic defense mechanisms such as password protection and firewall usage were employed to secure systems. However, as the cyber threat landscape expanded and grew more complex, these measures evolved into more sophisticated techniques. Notably, the integration of artificial intelligence (AI) and machine learning (ML) has revolutionized the cybersecurity domain. AI and ML technologies have proven invaluable in the battle against cyber threats. These advanced systems can identify and recognize patterns, learn from them, and then apply this acquired knowledge to detect future threats. By implementing these cutting-edge technologies, our capabilities to safeguard sensitive data have improved substantially. Today, we have smarter, more agile defenses that continuously learn from each interaction, making us better prepared to confront and neutralize threats. The fight against phishing provides an excellent example of how technological evolution has been instrumental in bolstering cybersecurity. , which involve the deployment of deceptive emails or counterfeit websites to trick users into disclosing personal information, have grown alarmingly complex. Cybersecurity technologies have had to adapt and evolve equally rapidly to counteract these deceptive strategies. Phishing tactics Machine learning algorithms, specifically, have emerged as a potent tool in our defense arsenal against phishing. These sophisticated algorithms can scrutinize emails and websites for potential red flags indicative of phishing attempts, thereby offering a more proactive and effective line of defense. Through continuous learning and adaptation, these algorithms improve over time, identifying threats with greater precision and consistency. Criminal IP's Strategic Advance: Pioneering the Bug Bounty Program for Enhanced Cybersecurity In the context of this ongoing cyber war, companies such as are pioneering new strategies to enhance user safety and security. Criminal IP is a search engine for cyber threats. Using artificial intelligence (AI) the company can identify malicious IP addresses. The recent launch of their bug bounty program serves as a prime example. This program encourages security researchers worldwide to identify and report potential system vulnerabilities. By doing so, Criminal IP aims to rectify any security flaws before they can be exploited, proactively protecting their users. Criminal IP not only offers an additional layer of protection but also fosters a collaborative relationship with the security research community. By engaging with these experts, the company can continuously improve its defenses and stay ahead of emerging threats. This initiative exemplifies the innovative approaches being employed in the fight against cyber threats and reinforces the need for active and ongoing defense strategies. Criminal IP's bug bounty program Future Frontiers in Cybersecurity Looking ahead, the future of cybersecurity is likely to be shaped by further technological advancements. Technologies such as blockchain, with its decentralized and transparent nature, offer promising solutions for secure data storage and transaction verification. , though still in its infancy, holds potential for the development of virtually unbreakable encryption methods. Quantum computing Conclusion As we continue to ride the waves of cyber threats, technological advances in cybersecurity and phishing mitigation are our most reliable lifeline. As demonstrated by initiatives like Criminal IP's bug bounty program, strategic innovation coupled with robust technological solutions are essential for navigating these turbulent digital waters. By staying vigilant and embracing technological progress, we can mitigate the impact of cyber threats and create a safer digital landscape for all. This story was distributed as a release by Ascend under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

Staying Ahead of Cyber Threats: Innovations in Cybersecurity and Phishing Mitigation

Adelantarse a las ciberamenazas: innovaciones en ciberseguridad y mitigación del phishing

Garder une longueur d'avance sur les cybermenaces : innovations en matière de cybersécurité et d'atténuation du phishing

साइबर खतरों से आगे रहना: साइबर सुरक्षा और फ़िशिंग शमन में नवाचार

サイバー脅威の先を行く: サイバーセキュリティとフィッシング軽減のイノベーション

Fique à frente das ameaças cibernéticas: inovações em segurança cibernética e mitigação de phishing

Đứng trước các mối đe dọa mạng: Những đổi mới trong an ninh mạng và giảm thiểu lừa đảo

保持领先网络威胁：网络安全和网络钓鱼缓解方面的创新

Want to know how big data could be a crucial part of your business? Keep reading to see how big data analytics services has helped the tech industry become successful in 2019. What is Big Data? Big data is the rapid extension of unstructured, semi-structured, and structured data generated from . internet connected devices The insights that are delivered from big data analytics services will help marketers to target campaigns more strategically, help healthcare professionals notice epidemics, and help environmentalists understand future sustainability. 3v’s of Big Data Big data analytics can be a difficult concept to grasp onto, especially with the vast varieties and amounts of data today. To make sense of the concept, experts broken it down into 3 simple segments. These three segments are the three big V’s of data: variety, velocity, and volume. Velocity Initially, the acceleration of big data has to lead to more opportunities. There’s a lot of data at hand, and once we have access to this data, you can use it to discover new realities. Sadly, the rate of data growth is passing our ability to decipher it. A study conducted by Digital Universe by IDC reveals that data around the world doubles in size every two years. What’s more important is that 3% of the data is organized, with only 0.5% is ready for analysis. Big data isn’t just big; it’s growing fast. On example of this the daily Facebook statistics. Based on Social Skinny’s insight, 293,000 statuses are updated, 136,000 photos uploaded, and 500,000 comments posted on Facebook every minute. Metadata technology and Big data technology paired with machine learning and AI will have to be used to their highest potential to give a snapshot of future frontiers. Variety Data is fast, data is big, but data is extremely diverse. A few decades ago, data would’ve been in a structured database in a simple text file. There was not a lot of options on how to use the data besides finding a trend and simple classification. Obviously, big data has changed the data landscape. While there’s still a place for text data, there are other forms of data presentation that are more convenient. For instance, video, audio, geospatial, images, and many others come into play. Each data form has its own type of uniqueness in terms of how it’s classified and stored on a cloud. What makes the format unique is how we can analyze them to create valuable solutions. Volume The world has a lot of data behind it, possibly at an incomprehensible amount. With over being generated in the past 2 years, that estimates to 2.5 quintillion data bytes daily. 90% of today’s data Let’s think about volume from a social media standpoint, since social media has a huge impact on data. Since 2016, there are over 2 trillion posts and 250 billion photos uploaded. Facebook has a wealth of personal data and its 2.2 billion users share data every second of the data. This would be impossible if it weren’t for the development of big data. Conclusion With big data analytics services, businesses will find it easier to present information, make predictions, and create innovative solutions. When using it, make sure that the data is accurate so that it runs properly. Thus, big data analytics is a necessary skill that will become more demanded in the future.

The 3 V’s of Big Data Analytics

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 2 AI COPILOTS Copilots[6] are applications of modern AI (foundation models, etc.) to help people with complex cognitive tasks. Copilots have conversational user interfaces and their users engage with them via natural language, they are powered by foundation models such as GPT-4, they are extensible with skills/tools/plugins, and they are scoped to specialized domains or applications (including search). Copilots are designed to to keep humans at the center of the task completion process and augment human capabilities to help them complete a broader range of tasks in less time. The Microsoft copilot stack (Figure 2) contains four main layers: (1) The copilot frontend, covering the user experience and extensibility with plugins, enabling developers to provide additional visible tools to the copilot; (2) The orchestration layer that handles the internal information flows, prompting, grounding, executing the plugins and processing their responses, among other things; (3) Copilots leverage the power of large foundation models that can be provided to the developer as is, specialized to specific tasks, domains, or applications, or developers can bring their own models to use to power copilot functionality, and; (4) This all runs on top of massive scale AI infrastructure hosted in the cloud on Azure. Underpinning all of this is Microsoft’s strong commitment to responsible AI, which ensures that copilots are safe, secure, and transparent, via an iterative, layered approach with mitigations spanning the model, prompts, grounding, and the user experience. Examples of copilots include GitHub Copilot,[7] an AI pair programmer that has been shown to reduce developer effort, enable more task success, and significantly expedite task completion [5]. Copilots are also emerging in search. Popular Web search engines such as Bing and Google are adding copilot functionalities to their search engines in the form of conversational agents such as Bing Chat and Google BARD. In search, copilots can help searchers to tackle a broader range of tasks than information finding and go deeper than surface (SERP-level) interactions with content by synthesizing answers on the searcher’s behalf. They also enable searchers to communicate their intents and goals more directly. Returning to the task tree (Figure 1), the focus on engaging copilots via natural language interactions allows both searchers and systems to consider higher-level task representations (macrotasks, subtasks) in addition to the more granular actions (queries, result clicks, pagination, and so on) that searchers already perform when engaging with traditional search engines. 2.1 Copilots in Search Copilots and chat experiences are a complement not a replacement to traditional search engines. Search engines have been around for decades and serve a valuable purpose: providing near instantaneous access to answers and resources for a broad range of search requests. These existing and emerging modalities can and should work well together to help searchers tackle a wider range of tasks. The capabilities of copilots to better understand intentions and provide assistance beyond fact finding and basic learning/investigation will expand the task frontier, broadening the range of complex search tasks that can be completed, e.g., direct support for tasks requiring creative inspiration (Figure 3). This all moves us a step closer toward intelligent search systems that can help with all-task completion, covering the full universe of tasks for which people might need search support. One way to define the range of tasks that copilots can support is though Bloom’s taxonomy of learning objectives [27]. Creation is at the pinnacle of that taxonomy and we have only scratched the surface in creativity support with next-word prediction through transformer models [16]. We are already seeing expansions into modalities beyond text (images, video, audio, and so on) and could consider support for other creative tasks including planning, analysis, and invention. There are also many other layers in Bloom’s taxonomy (e.g., evaluation - help searchers make judgments and decisions, application - help searchers complete new tasks, understanding - explain ideas and concepts to accelerate learning) that could form future task frontiers. Beyond greater capabilities, the introduction of copilots into search will also change how people will engage with search systems. In copilots, the mode of interaction is primarily natural language input, with some recent expansion toward multimodal inputs and outputs via the introduction of diffusion models such as DALL·E. Copilots can generate/synthesize direct answers, with source attribution for provenance, to build trust with users, and to drive traffic back to content creators, which is important to incentivize further content creation that fuels future foundation models. The overall search interaction flow is also different between search engines and copilots. When using copilots, searchers do not need to decompose their goal into sub-goals or sub-queries, examine SERPs and landing pages, and aggregate/synthesize relevant knowledge from retrieved information. Figure 4 has a comparison of the information seeking processes in the two modalities. In copilots, the responsibility for generating answers is delegated by the searcher to the system, which is not without its challenges in terms of human agency and human learning, as we discuss later in this article. 2.2 Adding Copilots to Search Engines It is not practical nor necessary to deploy copilots for all search tasks. Foundation model inference is expensive at massive scale and search engine algorithms have been honed over decades to provide relevant results for a broad range of tasks (e.g., navigation, factfinding). Conversational interfaces are less familiar for searchers and it will take time for searchers to adapt to this way of searching. Traditional search engines are sufficient when searchers know exactly what they want. Copilots are helpful for more complex search tasks or in situations where searchers may be struggling to find relevant information. Task complexity can be estimated using aggregate metrics such as the amount of engagement with the search engine (e.g., number of query reformulations) for similar tasks historically. As generative AI appears in more applications and searchers better understand search copilot capabilities, the tasks that searchers bring to search copilots will likely evolve. We will also see a growth in search experiences that unify traditional search and copilots. In a step towards this, search engines such as Bing and Google are already integrating dynamic answers from foundation models into their SERPs for some informational queries. Also, in the Google copilot (BARD), search results are displayed immediately below the copilot responses, allowing searchers to easily engage with them as desired. In the Bing copilot (Bing Chat), the search and copilot experiences are more separated and searchers must select a specific modality based on their task and personal preferences. Bing also provides searchers with control over other aspects, such as conversation style and tone, although it is not clear that searchers are sufficiently familiar with AI copilots at this time to use these nuanced controls effectively. Search copilots such as Bing Chat use retrieval augmented generation (RAG) [28] to ground copilot responses via timely and relevant results. This has many advantages, including: (1) There is no need to retrain the massive foundation model over time; (2) Search results provide relevant and fresh information to foundation models, and; (3) It provides a provenance signal linking generated content with online sources. In response to a searcher prompt, the foundation model generates internal queries iteratively that are used to retrieve the results that form context for the copilot answers created using generative AI. Displaying these queries to searchers inline in the dialog (as in Bing Chat) creates greater transparency and helps build trust with searchers that the system is understanding their tasks and goals. The Bing orchestrator can also pull in relevant instant answers from the search engine such as weather, stock, sports, and so on, and display those in copilot responses instead of or in addition to the answers generated by the foundation model. Figure 5 shows the high-level search process from query (+ conversation context) to the answer, and the role of various key system components. Copilots also enable search engines to support more complex search tasks. Using search alone would require more searcher effort to examine search results and manually generate answers or insights (see recent work on the Delphic costs and benefits of search [9]). Of course, there are different perspectives on task complexity, e.g., the copilot perspective (denoting the amount of computation, requests, etc. required for the system to complete the task) and the searcher perspective (denoting the amount of manual effort required for the human searcher to generate an answer and complete a task). Table 1 considers the task complexity from these two different perspectives and (again drawing from Bloom’s taxonomy) provides some current, anecdotal examples of the types of tasks that both searcher and systems may find to be more or less complex. Assuming that foundational model costs will drop and sophistication will increase, we focus here on the task complexity for searchers. [6] Copilots is the Microsoft terminology for an AI-powered digital assistant designed to  work alongside humans to augment their cognitive capabilities. [7] https://github.com/features/copilot

Navigating Complex Search Tasks with AI Copilots: AI Copilots

Blockchain is the technology behind cryptocurrencies. The rise of Bitcoin and Ethereum helped this distributed ledger technology amass huge popularity all around the world. While the so-called “crypto winter” has raised plenty of questions about the future of cryptocurrencies, blockchain technology proved itself extremely useful in other fields, such as logistics, healthcare, government, trade finance and cross-border payments. Increased capacity, enhanced security, immutability, faster settlement and decentralization are just some of the key features of this innovative technology. Companies and governments are already levering it to reduce operational costs, improve transparency and simplify complex business processes. Earlier this year, consulting giant KPMG released its “2019 Technology Industry Innovation” report, which examined over 740 world leaders from the tech industry. According to the , 41% of top managers said it was “very likely/likely” that blockchain will be adopted in their organizations within the next three years. Furthermore, nearly half of responders believed “very likely/likely” this technology will impact their business approach in that same timeframe. survey A prior study by PwC outlined similar , showing that 84 percent of executives confirmed their companies were “actively involved” with this technology. results When it comes to figures, blockchain solutions seems to be on the right track. A recent analysis by the forecasts a robust growth pace in spending for the 2018–2022 period, with an estimated 5-year compound annual growth rate of 76.0%. In 2019, spending on blockchain technology is forecasted at $2.9 billion, which translates into a 88.7% increase from the previous year. International Data Corporation As institutional adoption gains momentum, understanding blockchain and the opportunities that lie ahead of this technology could benefit your professional development. Ready to get started? Here are the best blockchain courses for 2019: #1 | Blockchain and Bitcoin Fundamentals Starting from scratch? Look no further. Blockchain and Bitcoin Fundamentals is the most popular blockchain-related course on Udemy for a good reason: it offers a comprehensive introduction to this technology and cryptocurrencies in just 2.5 hours. This course addresses basic definitions and concepts around blockchain, bitcoin, smart contracts, tokens, and all potential business applications. It will also show you how to get started with Bitcoin and other digital currencies. There are 37 lectures in total, averaging from 3 to 8 minutes each. Additionally, the author has included two downloadable PDFs on blockchain vocabulary and Bitcoin Hard Forks. The official price of this course is $94.99. #2 | Blockchain A-Z™: Learn How To Build Your First Blockchain If you’re one of those who is crazy about bestsellers, then this online course is for you. Nearly 30,000 students from all over the world have taken Blockchain A-Z™. This distance course was developed by AI entrepreneur Hadelin de Ponteves and data scientist Kirill Eremenko with one goal in mind: teaching you how to build your own blockchain, create a cryptocurrency and a smart contract. While there are no strict requirements to take this course, it is highly advisable to have a basic Python knowledge before jumping into it. It is available on Udemy. Blockchain A-Z™ costs $199.99, but there is a 95% discount in place right now. #3 | MIT Blockchain Technologies Is your organization planning to adopt blockchain technology? The MIT Blockchain Technologies course is meant for professionals seeking a deeper understanding of the impact and applications of the distributed ledger technology in an economic environment. This course is a go-to for entrepreneurs or executives wanting to integrate blockchain into their business plans, or figuring out how blockchain better fits the organization’s goals. This program will turn you into a blockchain specialist in six weeks. The course is based on the work of the leading MIT faculty and cryptoeconomics expert, Professor Christian Catalini. From a foundational overview of blockchain technology, its multiple use cases and limitations, the syllabus has it all covered. As you could expect from a world leading university, the price tag for this course is way above the average: $3,500. According to the MIT, this program requires you to study from 5 to 8 hours per week. The good news is that this is a fully self-paced learning course, which means you don’t need to follow a strict schedule. #4 | Oxford Blockchain Strategy Programme If you’re looking for something a bit more intense within a serious academic environment, then you might consider the Oxford Blockchain Strategy Programme — a six-week online course developed by Saïd Business School at the University of Oxford in collaboration with online education provider GetSmarter. Surprisingly, this program is a bit cheaper than the one offered by the MIT: £2,350. In this particular case, the organizers recommend studying 12–15 hours a week to complete it. Long story short, this course guides you through the multiple applications of blockchain technology, helping you identify opportunities to incorporate it to both short and long business strategies. This distance course is divided in six modules: Understanding blockchain Blockchain and business Transfer of value and storage of value Decentralised apps and smart contracts Transformative business models Blockchain frontiers: identify future trends in the industry #5 | IBM Blockchain Foundation for Developers As its name suggests, this online course with certificate of completion is mainly designed for developers wanting to create blockchain applications. However, the first part of this course offers a basic foundation on this DLT technology with no programming skills required. The other three parts are focused on concepts, components, and strategies to build blockchain networks. This blockchain developer course is available on Coursera — a leading online learning platform. The program was created by IBM, one of the strongest players in the Blockchain as a Service industry. In fact, this tech giants holds the second the second place in terms of the highest number of blockchain patents worldwide as of 2019. In order to earn the Coursera course certificate and IBM digital badge, you would have to pass a series of end-of-section quizzes and a final exam. The entire program is just 9 hours long and costs only $50. #6 | UC Berkeley Blockchain Fundamentals Last but not least, the UC Berkeley Blockchain Fundamentals Professional Certificate program. Unlike other courses offered by top universities, this one is available for just $198 on EdX — an well-known open online course provider. This learning path is accessible by anyone, without technical skills needed to get started. This program is designed to clarify essential concepts surrounding blockchain technology, the main ideas behind it and its benefits for a vast number of industries. According to the course organizers, you can complete it in under one and a half months if you dedicate between 3 and 5 hours per week. Overall, this program is a perfect choice for professionals to get up to speed on blockchain. Define blockchain solutions Understand the mechanics behind Bitcoin Discover real-life cryptocurrency applications, smart contracts and Dapps Gain in-depth knowledge on corporate-level blockchain implementations Explore cryptocurrency and blockchain regulatory environments About Crypterium Crypterium is one of the most promising fintech companies, according to KPMG and H2Ventures. We are building a mobile app that meets the banking needs of the digital assets era. The team is led by former General Manager of Visa Central & Eastern Europe , and C-level executives from global financial institutions, like Renaissance Insurance, London Derivatives Exchange, American Express etc. Steven Parker ๏ ๏ ๏ ๏ ๏ ๏ Website Telegram Facebook Twitter Reddit YouTube LinkedIn <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

6 Online Courses To Become A Blockchain Expert In 2019

In the face of mounting risks, enterprises and institutions are progressively leveraging IP geolocation as an efficacious instrument for detecting and alleviating internet-based menaces. IP geolocation involves the identification of a device or user's geographical location through their IP address. This advanced technology empowers organizations to track and oversee online activities, recognize looming threats, and proactively thwart potential cyberattacks. cybercrime Understanding IP Geolocation in Cybersecurity IP geolocation data unveils the whereabouts of network traffic and devices, affording organizations the ability to promptly detect potential threats and take suitable actions. Through meticulous analysis of IP geolocation data, organizations can effectively detect dubious activity such as connections from unexpected locations, and swiftly impede or isolate them from causing any damage. Additionally, IP geolocation serves as a valuable tool in detecting and responding to intricate threats like and . By scrutinizing IP geolocation data over an extended period, organizations can determine recurrent behavior patterns and consequently pinpoint potential malevolent activity. advanced persistent threats (APTs) botnets It is crucial to bear in mind that IP geolocation should not be viewed as the panacea for threat intelligence, but rather as a single instrument within a larger array of technologies, such as intrusion detection systems and security information and event management (SIEM) systems. Benefits of IP Geolocation in Cybersecurity It has been observed that of these establishments shut down their operations within half a year of experiencing a cyber assault. However, utilizing IP geolocation can aid in curbing such incidents, thereby reducing the rate of business closure. So, how can IP geolocation be useful in cybersecurity? Here are some of the key benefits: approximately 60% Identifying potential threats One of the key applications of in the realm of cybersecurity is the identification of possible threats. The analysis of the geographical origin of incoming traffic permits a rapid determination of suspicious locations. For instance, suppose that you are a business headquartered in the United States and you observe traffic emanating from an IP address located in Russia. In that case, it might be prudent to undertake a thorough investigation to guarantee the legitimacy of such traffic. IP geolocation Blocking malicious traffic The implementation of IP geolocation affords the advantage of intercepting harmful traffic. By leveraging the power of IP geolocation, one can effectively obstruct traffic originating from specific regions, thereby mitigating the probability of malicious attacks. To illustrate, a company that solely conducts business within the borders of the United States could utilize IP geolocation to bar access from foreign nations, thereby curbing the incidence of potential cyber threats. Improved fraud detection IP geolocation can aid in detecting fraud by cross-referencing a user's location with their billing details. For instance, a mismatch between a billing address in the United States and an IP address in Europe could signal fraudulent activity. Compliance with regulations Ultimately, IP geolocation can prove to be a valuable tool for adhering to regulatory requirements. Numerous countries like , , , , and more have implemented stringent data privacy regulations mandating businesses to store data within specific geographical locations. By leveraging IP geolocation, enterprises can verify that they are storing their data in adherence with these laws and avoid potential legal and financial consequences. China Canada Australia Brazil Use Cases of IP Geolocation in Cybersecurity is a powerful tool in the fight against cyber threats. Here are some of the use cases of IP geolocation in cybersecurity: IP geolocation : By harnessing the power of IP geolocation, one can accurately pinpoint the geographic coordinates of network-connected devices, thereby enabling the detection of unwarranted access and uncovering potential cyber threats that loom on the horizon. Network Security : The technique of IP geolocation can enable the tracking of endpoint locations, encompassing laptops, smartphones, and tablets. This valuable information facilitates vigilant surveillance by security teams and enables the timely detection of possible security incidents. Endpoint Security : By leveraging IP geolocation, security teams can effectively monitor cloud-based resources, including servers and applications, by identifying their geographical location. This enables them to verify that the resources are being accessed solely from authorized locations, bolstering the security posture of the organization's cloud infrastructure. Cloud Security : The discerning employment of IP geolocation empowers the acquisition of critical intelligence on potential cyber threats. By methodically scrutinizing the precise locations of IP addresses linked with malevolent activities, security teams can effectively unearth the origins of cyber attacks and undertake pre-emptive measures to safeguard their systems. Threat Intelligence Future of IP Geolocation and Cybersecurity As we bear witness to the ever-advancing frontiers of technology, the future of IP geolocation and cybersecurity stands to be equally impacted. Revolutionary technologies, including the Internet of Things (IoT), blockchain, and machine learning, are positioned to bring sweeping transformations to how we handle these crucial facets of online security. Particularly, offer immense potential in the realm of cybersecurity. These cutting-edge innovations can process and scrutinize colossal data sets at lightning-fast speeds, identifying abnormalities and warning signs of possible security breaches with unmatched efficiency. In doing so, they empower organizations to stay a step ahead of cybercriminals, safeguarding their most sensitive information from malicious actors. machine learning and artificial intelligence (AI) The intricately woven network of interconnected devices that is the Internet of Things (IoT) has brought forth a multitude of opportunities and challenges for IP geolocation and cybersecurity. While it enables more comprehensive monitoring of network traffic and possible threats, it also expands the attack surface and necessitates the implementation of robust security measures. New technologies bring great potential for IP geolocation and cybersecurity improvement but also entail challenges. Compliance with privacy regulations and ensuring reliable data are crucial. To stay protected from increasingly sophisticated cyber threats, organizations must keep up with technology and implement strong security measures. Final Thoughts According to the , 14 out of the 16 critical infrastructure sectors in the United States were subject to ransomware incidents. The use of IP geolocation in threat intelligence and cybersecurity is a vital tool in identifying potential cyber threats and improving overall security. Cybersecurity and Infrastructure Security Agency As technology continues to evolve and cybercrime becomes more sophisticated, the accuracy and reliability of IP geolocation data will become increasingly important. Organizations should continue to prioritize cybersecurity best practices and consider incorporating IP geolocation data into their security frameworks and tools. As we look to the future, it is clear that IP geolocation will play a significant role in protecting against cyber threats and maintaining a secure online environment.

Enhancing Threat Intelligence and Cybersecurity with IP Geolocation

Hey Hackers! is generating palpable excitement within the blockchain and crypto communities, with only two weeks remaining until the event. Scheduled for October 4-5, 2023, at the Le Meridien Dubai Hotel & Conference Center, this prestigious event gathers over 3,000 blockchain entrepreneurs, crypto enthusiasts, and industry leaders from 85 countries. The Blockchain Economy Dubai Summit The Summit is bolstered by its high-profile sponsors, including OKX as the Title Sponsor and other significant partners like Uniqan Capital, Foxify, Saitama, B2rbroker, B2BinPay, UZX, Bitunix, Tencent Cloud, Expand My Business, Avrora Metaverse, P2Pbet, Areon Network and many others who are building the future of web3 solidifying their positions in the industry. Attendees can anticipate insightful dialogues from an illustrious panel of experts, including: Lennix Lai, Global Chief Commercial Officer at OKX Dr. Ebrahim Al Alkeem, Director at the Government of Abu Dhabi and an authority on Digital Transformation, Cyber Security, and Artificial Intelligence Martin Hanzl, Head of New Technologies at EY Law Michaël van de Poppe, CEO & Founder of MN Trading Akshay Chopra, VP, Head of Innovation & Design at Visa CEMEA Vladimir Nikolenko, CBDC & Digital Assets Associate Director at Accenture Amnah Ajmal, Executive Vice President, Market Development, EEMEA, Mastercard Sam Blatteis, CEO of The MENA Catalysts Phillip Liu, General Manager of Tencent Cloud International in the MENA Region Dr. Salim Al-Shuaili, Senior Executive at the Ministry of Transport, Communications and Information Technology of Oman Adrian Zduńczyk, Founder of The Birb Nest George Gvazava, Chief Crypto Officer at Bank of Georgia A distinguished Forbes personality, Senior Producer, and Presenter, Ramia Farrage, will serve as the Master of Ceremony for the two-day duration of this anticipated Summit. The Summit promises engaging and comprehensive discussions on groundbreaking topics, framed in their most sophisticated formats: Investment and Trading Strategies: The Future Landscape; Decentralized Finance (DeFi): New Frontiers; Transitioning from Web2 to Web3: A Roadmap; Regulatory Frameworks for Cryptocurrency Usage; Seamless Payment Applications in Blockchain; The Intersection of AI and Web3: A Harmonious Convergence, among many others. The event is uniquely positioned to attract a diverse cross-section of stakeholders, including C-level decision-makers, venture capitalists, blockchain entrepreneurs more than 100 global media organizations, institutional and private investors, influencers, and many others. The Happy Bird tickets for the Blockchain Economy Dubai Summit 2023 are nearing a sold-out status, and soon only Lazy Bird tickets will be available. For individuals aiming to dive deep into groundbreaking innovations, eager to network with global decision-makers from all around the world, or interested in exploring the Dubai’s web3 market, the time to act is now. Exceptional Perks for Attendees Emirates Airlines, as the Official Airline partner, is offering exclusive travel discounts for global attendees of Blockchain Economy Dubai Summit. To further enhance attendee convenience, the organizers also provide specially discounted room rates at the Le Meridien Dubai Hotel & Conference Center. Event Info Event name: Blockchain Economy Dubai Summit Dates: October 4-5, 2023 Event website: https://beconomydubai.com Last Happy - Bird tickets: https://beconomydubai.com/tickets Event hashtag: #BESUMMIT Dubai 2023

Blockchain Economy Dubai Summit 2023: Just Two Weeks Away and Buzzing with Anticipation

“To be truly effective, today’s software development companies need to understand their customers’ business needs and industry specific issues before embracing new technologies”, says Olga Azimbaeva, Business Analyst and usability expert at DataArt. As a global company, carefully tracks industry developments at its Research and Development Centre, which is an important part of our Healthcare and Life Sciences Practice. The R&D Center generates ideas for promising new areas in the industry and develops these ideas at the concept level. Several years ago, (Vice President, Healthcare & Life Sciences), worked with a client whose electronic system for monitoring clinical trial outcomes was ineffective. The system was intended to collect data during clinical trials — for example, the reaction to drugs administered, what side effects were felt, how the patient estimates the effectiveness of the treatment, etc. At that time, ePRO (Electronic Patient-Reported Outcome) systems included digitised questionnaires and other documents to be completed by test subjects during the trials. Because of its poor usability, the system was hard to use, especially for children and adolescents. DataArt Daniel Piekarz Given the broad interest in organising clinical trials and improving the quantity and quality of data obtained, the R&D centre decided to create a concept application to address the specific clinical trial needs for children. Initially, the idea was to use gamification elements to attract attention and motivate children to use the solution. We used a concept resembling that of the once popular , with children being asked to take care of a virtual pet. Tamagotchi Learning while developing While analysing the target audience, we concluded that the Tamagotchi concept would not work in our case. If not cared for properly, the virtual pet could die and since our target audience is chronically ill children (those with diabetes, asthma, cancer) who were stressed and depressed, we felt that a positive reinforcement would yield better results. Several character concepts were created and, after collecting feedback from children, a squirrel was chosen. In our application, the child and the squirrel are treated together: the squirrel takes medication and becomes sad when a child feels unwell. In other words, we established an empathic connection: the child need not feel alone because he or she has a virtual friend who is always nearby. Moreover, the child receives bonus stars for completing tasks like taking medicine on time, and these stars can later be used to dress their character in different costumes or to buy new toys, making the virtual friend happy. At the start, the application was planned for inpatient clinical trials, but later we realised that it could also be used at home, for diseases like diabetes or cancer that require constant monitoring. We created another application, this time for parents — Care Companion –which took into consideration the fact that children at home (unsupervised by medical staff) might not be able to comply with prescriptions properly and would need adult supervision for safe and effective treatment. The Care Companion allows the adults to configure the children’s portion of the application with the frequency and dosage of medication to be taken, complete a diary, and answer questions about the child’s wellbeing. In order to collect feedback, the Care Companion analyses all data obtained from the child’s application and displays it in clear graphics. In addition, a separate interface for the child’s physician provides access to all the data from the child’s application and sets the daily schedule. Further discoveries Our team realised that one of the factors of medication non-compliance is the fear of side effects and the belief that the treatment can be stopped after the symptoms have disappeared. In light of this, an education section was added to the application, in which both the child and the parents can learn more about the disease, side effects, and recommendations. Another section was added to contact a doctor with questions. We believe that a system works well when it meets the needs of the target audience, so we tried to focus on a specific chronic disease — diabetes. For this purpose, we added a food tracking function (calories/carbs) and the ability to scan the glucometer, so that the application could recognise the numbers on the screen of the device and display the blood sugar level. Two years ago, the application won third place at the . Since then, we have developed the idea further, adding the Care Companion, and won . InnovateNYP event: Pediatric App Challenge the Pfizer Healthcare Challenge 2017 The team was also invited to speak at the prestigious to present our achievements to the participants, including both start-ups and leaders in the pharmaceutical industry. Frontiers Health conference Building for the future The team has planned several additional improvements for the application. These include additional customisation. While the current application revolves around diabetes, it can help with any clinical state. Yet, to make it more appealing to different categories of young patients (different age groups, e.g. teenagers) we are going to completely revamp the UI of the application. KidPRO New gamification elements will also be added. We are going to introduce new characters and types of rewards, customise the environment (like ‘the room’), and embed mini-games and other ways to better engage the child. There will be improved FHIR integration, which means the app will pull more details about the treatment plan and prescriptions so that the doctor can use that information to prepare an individual daily schedule for the patient to give better results. The analytics capabilities of the application make it possible to analyse the treatment process and engagement aspects (eg how many times the child has watched a certain video) and recommend personalised actions accordingly. For instance, if the child is not following the diet properly, he or she can be prompted to watch a video on the importance of diet. Notifications will be developed, too. If the child misses certain procedures or routine steps, the parent will be notified. Important events, like doctor check-ups, can be scheduled directly in the parent’s device calendar. Finally, inpatient and outpatient modes will be added so that w the patient visits a hospital, the application provides additional information and instructions. Originally published at medtechengine.com .

Holding Their Hand: What We Learned about Gamifying for Children in Medical Trials

The cryptocurrency world is a rapidly evolving landscape, with new projects, technologies, and investment opportunities emerging what feels like every single day. In this fast-paced environment, having access to comprehensive and up-to-date data is crucial for everyone from developers to traders to analysts. That's where crypto data APIs come into play. These powerful tools provide programmatic access to aggregated market data, on-chain metrics, price feeds, asset metadata, and more from thousands of blockchain networks and crypto exchanges. By tapping into these APIs, builders can create cutting-edge apps that put a wealth of cryptocurrency intelligence at users' fingertips. Apps You Can Build When You Have Access to Robust Crypto Data Streams Trading Platforms and Tools For traders, having access to historical price data alongside live market tickers is like giving a fighter pilot their instrument panel. By integrating a crypto data API like CoinGecko, developers can build advanced trading terminals that display candlestick charts, depth charts, order books and more for thousands of trading pairs across centralized and decentralized exchanges. But it goes beyond just visualizing the data. With flexible API access, you can calculate complex technical indicators on the fly, backtest automated trading strategies against years of historical data, and execute trades directly through the API. Essentially, you have all the building blocks to create a comprehensive trading platform or analysis toolkit. Perhaps most powerfully, machine learning can be layered on top to create self-teaching algorithms that identify patterns in the market data and execute trades based on those patterns. The possibilities are endless when you combine crypto data with AI/ML capabilities. Portfolio Tracking and Crypto Finance Apps For anyone holding a diversified basket of crypto assets, whether they're a day trader or long-term investor, keeping track of your portfolio across dozens of wallets and exchanges is a headache. But it doesn't have to be. By integrating coin metadata like ticker symbols, logos, descriptions and links to blockchain explorers from crypto data APIs, developers can build intuitive portfolio tracking apps that automatically sync all of a user's asset balances and provide a clean interface to monitor performance over time. The metadata also enables more advanced functionality like pricing in fiat terms, profit/loss calculations, and tax reporting features. We're just scratching the surface of ways to improve crypto finance management UX thanks to centralized access to this data. Analytics for DeFi, NFTs, DAOs and Web3 The decentralized finance (DeFi) space is booming, with on-chain trading volumes and total value locked hitting new highs seemingly every month. Projects in the NFT, DAO, and broader Web3 sectors are also seeing an explosion of activity and engagement. But analyzing the trends and opportunities in these areas requires robust on-chain data - information about liquidity pools, orders, trades, mints, token distributions, and more flowing directly from the blockchains where this activity is happening. This is exactly the type of data that crypto APIs like CoinGecko are focused on aggregating and expo sing through clean APIs. Whether it's a dashboard monitoring major DeFi lending activity, an analytical tool that identifies the hottest new NFT collections, or a data viz that maps the connections between the thousands of DAOs, on-chain crypto data is the key ingredient enabling the next wave of blockchain analytics and intelligence apps. Dig into the capabilities of a platform like CoinGecko and you'll find API endpoints that let you track stats on over 500 different decentralized exchanges, pull granular order book data for DEX trading pairs, analyze token transfers and smart contract interactions, and much more. In a space that is inherently decentralized with no single source of truth, having an aggregator that seamlessly combines on-chain data across networks into unified APIs is a game-changer for those of us building Web3 apps, services, and tooling. The Future: How Crypto Data + AI Will Transform Industries As transformative as crypto data APIs have already been for the development of blockchain and cryptocurrency applications, we're really just seeing the tip of the iceberg in terms of their potential impact. I'm most excited about the future intersection of crypto data and artificial intelligence. Being able to combine the wealth of data accessible through API streams with large language models, computer vision, simulation environments, and other cutting-edge AI technologies is going to open up entirely new frontiers of innovation. We're already starting to see examples of this convergence in the form of crypto trading bots that use machine learning to identify patterns and automatically execute trades. But I expect we'll see AI-powered crypto apps and services start to proliferate across domains like: Financial services: Intelligent crypto robo-advisors that provide personalized portfolio management, lending, trading and financial planning guidance tailored to each individual's risk profile and situation


Regulatory compliance: AI models that analyze on-chain activity and transaction patterns to detect fraud, money laundering and other illicit activity in real-time.


Economic simulation: Virtual environments for simulating and testing new blockchain-based economic, financial and governance models before deploying them in the real world.


Asset discovery: AI assistants that can analyze millions of data points to identify up-and-coming crypto assets, DeFi protocols and NFT collections before they explode in popularity.


Gaming and virtual worlds: Open-world blockchain games and metaverse experiences that leverage AI for creating infinite environments, stories, characters and experiences that respond intelligently to players' actions. The possibilities are truly mind-boggling when you have a torrent of live crypto data streaming in that can be consumed and learned from by artificial intelligence models with each passing second. At this pivotal moment when blockchain data is proliferating rapidly but is largely siloed across different networks and platforms, crypto data aggregators and their standardized APIs will act as critical connective layers. They'll provide the canonical pipelines for getting all of that on-chain information into the machines that will shape our decentralized digital future. This grand vision may still be a few years away, but the foundations are being built today by platforms working to comprehensively organize and expose the full scope of crypto asset data. For developers who want to start putting these powerful building blocks to use, there's never been a better time to start plugging into crypto's open data streams through robust APIs. Integrate once, and you'll be able to build apps and services that can interface with the entire blockchain ecosystem as it continues its exponential expansion. Just like the internet opened up entirely new worlds of innovation by democratizing information flows, crypto data APIs are the on-ramp to uncovering value in the vast frontier of decentralized data and assets. Put on your builder helmet and start plugging in. The cryptocurrency world is a rapidly evolving landscape, with new projects, technologies, and investment opportunities emerging what feels like every single day. In this fast-paced environment, having access to comprehensive and up-to-date data is crucial for everyone from developers to traders to analysts. That's where crypto data APIs come into play. These powerful tools provide programmatic access to aggregated market data, on-chain metrics, price feeds, asset metadata, and more from thousands of blockchain networks and crypto exchanges. By tapping into these APIs, builders can create cutting-edge apps that put a wealth of cryptocurrency intelligence at users' fingertips. Apps You Can Build When You Have Access to Robust Crypto Data Streams Trading Platforms and Tools For traders, having access to historical price data alongside live market tickers is like giving a fighter pilot their instrument panel. By integrating a crypto data API like CoinGecko, developers can build advanced trading terminals that display candlestick charts, depth charts, order books and more for thousands of trading pairs across centralized and decentralized exchanges. But it goes beyond just visualizing the data. With flexible API access, you can calculate complex technical indicators on the fly, backtest automated trading strategies against years of historical data, and execute trades directly through the API. Essentially, you have all the building blocks to create a comprehensive trading platform or analysis toolkit. Perhaps most powerfully, machine learning can be layered on top to create self-teaching algorithms that identify patterns in the market data and execute trades based on those patterns. The possibilities are endless when you combine crypto data with AI/ML capabilities. Portfolio Tracking and Crypto Finance Apps For anyone holding a diversified basket of crypto assets, whether they're a day trader or long-term investor, keeping track of your portfolio across dozens of wallets and exchanges is a headache. But it doesn't have to be. By integrating coin metadata like ticker symbols, logos, descriptions and links to blockchain explorers from crypto data APIs, developers can build intuitive portfolio tracking apps that automatically sync all of a user's asset balances and provide a clean interface to monitor performance over time. The metadata also enables more advanced functionality like pricing in fiat terms, profit/loss calculations, and tax reporting features. We're just scratching the surface of ways to improve crypto finance management UX thanks to centralized access to this data. Analytics for DeFi, NFTs, DAOs and Web3 The decentralized finance (DeFi) space is booming, with on-chain trading volumes and total value locked hitting new highs seemingly every month. Projects in the NFT, DAO, and broader Web3 sectors are also seeing an explosion of activity and engagement. But analyzing the trends and opportunities in these areas requires robust on-chain data - information about liquidity pools, orders, trades, mints, token distributions, and more flowing directly from the blockchains where this activity is happening. This is exactly the type of data that crypto APIs like CoinGecko are focused on aggregating and expo sing through clean APIs. Whether it's a dashboard monitoring major DeFi lending activity, an analytical tool that identifies the hottest new NFT collections, or a data viz that maps the connections between the thousands of DAOs, on-chain crypto data is the key ingredient enabling the next wave of blockchain analytics and intelligence apps. Dig into the capabilities of a platform like CoinGecko and you'll find API endpoints that let you track stats on over 500 different decentralized exchanges, pull granular order book data for DEX trading pairs, analyze token transfers and smart contract interactions, and much more. In a space that is inherently decentralized with no single source of truth, having an aggregator that seamlessly combines on-chain data across networks into unified APIs is a game-changer for those of us building Web3 apps, services, and tooling. The Future: How Crypto Data + AI Will Transform Industries As transformative as crypto data APIs have already been for the development of blockchain and cryptocurrency applications, we're really just seeing the tip of the iceberg in terms of their potential impact. I'm most excited about the future intersection of crypto data and artificial intelligence. Being able to combine the wealth of data accessible through API streams with large language models, computer vision, simulation environments, and other cutting-edge AI technologies is going to open up entirely new frontiers of innovation. We're already starting to see examples of this convergence in the form of crypto trading bots that use machine learning to identify patterns and automatically execute trades. But I expect we'll see AI-powered crypto apps and services start to proliferate across domains like: But I expect we'll see AI-powered crypto apps and services start to proliferate across domains like: Financial services: Intelligent crypto robo-advisors that provide personalized portfolio management, lending, trading and financial planning guidance tailored to each individual's risk profile and situation Regulatory compliance: AI models that analyze on-chain activity and transaction patterns to detect fraud, money laundering and other illicit activity in real-time. Economic simulation: Virtual environments for simulating and testing new blockchain-based economic, financial and governance models before deploying them in the real world. Asset discovery: AI assistants that can analyze millions of data points to identify up-and-coming crypto assets, DeFi protocols and NFT collections before they explode in popularity. Gaming and virtual worlds: Open-world blockchain games and metaverse experiences that leverage AI for creating infinite environments, stories, characters and experiences that respond intelligently to players' actions. Financial services: Intelligent crypto robo-advisors that provide personalized portfolio management, lending, trading and financial planning guidance tailored to each individual's risk profile and situation Financial services: Intelligent crypto robo-advisors that provide personalized portfolio management, lending, trading and financial planning guidance tailored to each individual's risk profile and situation Financial services: Regulatory compliance: AI models that analyze on-chain activity and transaction patterns to detect fraud, money laundering and other illicit activity in real-time. Regulatory compliance: AI models that analyze on-chain activity and transaction patterns to detect fraud, money laundering and other illicit activity in real-time. Regulatory compliance: Economic simulation: Virtual environments for simulating and testing new blockchain-based economic, financial and governance models before deploying them in the real world. Economic simulation: Virtual environments for simulating and testing new blockchain-based economic, financial and governance models before deploying them in the real world. Economic simulation: Asset discovery: AI assistants that can analyze millions of data points to identify up-and-coming crypto assets, DeFi protocols and NFT collections before they explode in popularity. Asset discovery: AI assistants that can analyze millions of data points to identify up-and-coming crypto assets, DeFi protocols and NFT collections before they explode in popularity. Asset discovery: Gaming and virtual worlds: Open-world blockchain games and metaverse experiences that leverage AI for creating infinite environments, stories, characters and experiences that respond intelligently to players' actions. Gaming and virtual worlds: Open-world blockchain games and metaverse experiences that leverage AI for creating infinite environments, stories, characters and experiences that respond intelligently to players' actions. Gaming and virtual worlds: The possibilities are truly mind-boggling when you have a torrent of live crypto data streaming in that can be consumed and learned from by artificial intelligence models with each passing second. At this pivotal moment when blockchain data is proliferating rapidly but is largely siloed across different networks and platforms, crypto data aggregators and their standardized APIs will act as critical connective layers. They'll provide the canonical pipelines for getting all of that on-chain information into the machines that will shape our decentralized digital future. This grand vision may still be a few years away, but the foundations are being built today by platforms working to comprehensively organize and expose the full scope of crypto asset data. For developers who want to start putting these powerful building blocks to use, there's never been a better time to start plugging into crypto's open data streams through robust APIs. Integrate once, and you'll be able to build apps and services that can interface with the entire blockchain ecosystem as it continues its exponential expansion. Just like the internet opened up entirely new worlds of innovation by democratizing information flows, crypto data APIs are the on-ramp to uncovering value in the vast frontier of decentralized data and assets. Put on your builder helmet and start plugging in.

FUTURE FRONTIERS

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FUTURE FRONTIERS

futurefrontiers.org.uk

-- employees

Since n.d.

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