Alibaba earmarked $1.1 billion for South Korea to build a new logistics center. This expansion comes amid fierce competition from domestic e-retailer <strong>Coupang</strong>, Inc (NYSE:CPNG), which is aggressively investing in advanced technologies and logistics to maintain its market share, taking a cue fro

Alibaba and Coupang Intensify E-Commerce Battle with Major Investments in South Korea

With this perspective, let's look at five stocks that I am confident are magnificent $500 investments for my daughter's custodial account.

5 Stocks You Can Confidently Invest $500 in Right Now

Coupang Inc’s recently made public that its insider Kim Tae Jung unloaded Company’s shares for reported $79207.0 on Oct 02. In the deal valued at $16.95 per share,4,673 shares were sold. As a result of this transaction,

Deutsche Bank analysts downgrades a Hold rating for Coupang Inc (CPNG)

What trends should we look for it we want to identify stocks that can multiply in value over the long term? One

The Return Trends At Coupang (NYSE:CPNG) Look Promising

CPNG stock has gone down by -3.63%, with a monthly gain of 0.41% and a quarterly plunge of -6.28%. The volatility ratio for the week is 3.56%, and the volatility levels for the last 30 days are 2.77% for Coupang Inc.

Coupang Inc (CPNG) Stock: A Review of the Recent Movement

Scottish Mortgage shares have fallen by 35 per cent since 2020 and stand at an embarrassingly wide 19 per cent discount to its net asset value.

Can Scottish Mortgage get back on track? We speak to manager Lawrence Burns

Microsoft and other titans of tech announced bumper profits driven by the generative artificial intelligence (AI) industrial revolution.But Scottish Mortgage, the £9.18billion FTSE 100 member investment trust that backs innovation in AI and every other form,

Can Scottish Mortgage get back on track? We speak to the fund manager

North Korean hackers are hiding malware in “copycat applications” that look like they are South Korean to steal personal information of users and other sensitive data, the national spy agency said Friday.

North Korean hackers tricking users with ‘copycat apps’ disguised as South Korean: NIS

Hanryu Holdings, a content platform company, celebrated its listing on the Nasdaq by ringing the opening bell at the Nasdaq MarketSite in Times Square, New York, Thursday, U.S. time.

Hanryu Holdings rings Nasdaq bell to celebrate its listing

Coupang

coupang

With the boom in technology, tech startups are booming all over the world, especially in the Asian region. In recent years, it has been noticed that startups are growing the track record of success stories in the business world. In fact, more than ever before, more talents are showing an interest in being a part of this culture. For the ones who are quite ambitious to build their careers in the business environment, then the fast-paced lifestyle and booming technology are giving them a good way to kick off. Asia – The Hub for Startups By taking leaps and strides, the startup scene in Asia has been growing like a fire in the forest. Several people are giving their attention and investment to start a tech startup and also receiving a great amount of success from it. What's causing Asia's tech startup scene to thrive? Well, the answer to this questions is quite simple and straightforward! Fortunately, the tech startup scene in Asia is vigorous despite being young enough to deal with the new type of innovations and technologies. As per the in April 2019,  Asia was home for approximately more than one-third (119) of the world's 331 'unicorns' (startups valued at more than US$1b). You will be pleased to know that an estimated 91 of these companies are based in China, followed by 13 in India, six in South Korea, and four in Indonesia. report presented by the McKinsey Global Institute Market conditions are just right for tech startups in Asia Do you want to have an insight into the Asia tech startup scene? If you are a tech geek, then you must be willing to gather every minute information about the places where the market conditions are good enough. Due to Asia's rich venture capital market, tech startups are thriving without any hassle. Plus, this place has been one of the topmost global sources and destinations for global venture capital in specific areas like virtual reality, autonomous vehicles, 3-D printing, robotics, drones, and AI. it has been predicted that by 2040, Asia is going to project at the topmost level with 50% of global GDP and drive 40% of the world's consumption. It has been stated that the companies in the Asian region are getting success at a higher rate as compared to other parts of the world. In fact, the CB Insights global unicorn club tracker has also declared that six out of 19 companies are unicorns valued at $1B+. The companies are as follows- Grab ($14.3B, Singapore), GO-JEK ($10B, Indonesia), Coupang ($9B, South Korea), Olacabs ($6.2B, India), Preferred Networks ($2B, Japan), and Klook ($1B, Hong Kong). As a matter of fact, there are a vast amount of opportunities in Asia for the tech startup scene. But it is very important for novice tech geek of the tech scene in the Asian region to examine the local climate over there before jumping in carefully. In order to quench this thirst, here are some of the important information. China Market In the past decades, the tech business market in China has been going strong enough. This happens due to the changed attitude towards innovation, technology, and economic growth. Nowadays, China ranks second only to the US in terms of startup investment. Since 2014, China has provided 20% of the world's venture capital. It would not be overstated to say that China is a major contender in the international tech market, thus making it an advantageous location to set up your business. Some of the major successful companies who have done excellent wonders in their respective businesses are Tencent, Baidu, and Alibaba. It is blatantly obvious that China houses some of the most influential talents. Hong Kong To be very much apparent, Hong Kong is quite different from China. The factors that contribute to the distinction between two places are culture, mindset, freedom, and ease of doing business perspective. Officially, Hong Kong is a Special Administrative Region of the People's Republic of China. This place is also known for being the tech revival, and the emergent market for attracting tech geeks and novice . In fact, several programs support entrepreneurs who are seeking for either on-site or off-site assistance. For instance,- The provides business owners the resources to accelerate their business growth. investors Cyberport Incubation Programme You are looking for an on-site support system, let it be office space in Hong Kong, but you are not unable to get the proper resources to strive for your business. So, with this program, you can get an office space for you and your team members. However, in the case of an off-site facility, you can take advantage of various perks without asking. They have tons of tools for business ventures, such as a great audio-video room there that is for members to use. Aside from this facility, the program also lets business people use the resources to take off their financial crisis. If you are looking for a place which is a collaborative mixture of the innovative and ambitious environment on one side, and a population of internationally traveled, wealthy tycoons on the other, Hong Kong could be a powder keg of technology waiting to explode. Due to the investment support from various programs, strategic location, and free trading environment, is proliferating, and giving a taste of success to everyone. Hong Kong's tech startup culture Singapore The government of Singapore also supports tech startups in their nation's startup ecosystem. In fact, the Prime Minister — Lee Hsien Loong in a Q in June, Singapore’s Prime has said that, if Singapore rejects technology and science, it will be the end of us.” &A at the Smart Nation Summit This country has a leading digital power with a booming tech startup scene. Fortunately, with the availability of government support, getting heavy investments in tech and science projects, and the business-friendly policies. In fact, Singapore has also established an ample number of government agencies that will give support to tech companies such as the Economic Development Board (EDB) and the Standards, Productivity, and Innovation Board. India Indian government has been launching programs to motivate the tech startup scene in their country as well. They have enrolled more than 1.2 billion people to its biometric digital identity program. With this program, they have given their people a legal identity for the first-ever time to get access to banking, credit, government benefits, education, and other services. India has also attracted and more than ten million business owners onto a common digital platform via a goods and service tax. In short, these types of programs has helped India to accelerate the process of digitization more broadly across entire economies. top startups South East Now, have a look at the South East region of Asian. There are many companies like Grab, GO-JEK Indonesia, and Sea, which are all unicorns. And they have grabbed the opportunities that come from technology. In short, we can say that the revolution has just started. Korea One of the most innovative economies in the world- "Korea.' Take a look at this place, and observe yourself, how the technology in this place is reshaping everything. You will note yourself how the combination of all these forces is accelerating Asia forward towards tech startups. Very interesting.' Because you know, there's reason to be positive about the outlook." Got an idea about the Asian tech startup scene? If you are thinking to kick start a new life in the business world, then do research work on the Asian regions.

An insight into Asia's Tech Startup Scene

We skipped last year, but this year SparkLabs Group is back with our . fourth rankings and report of the Top Ten Startup Ecosystems in the World These past few years we have seen the rest of the world catch up a bit to Silicon Valley, but it is still the clear leader in startup growth and innovation. Almost every city increased its assessment numbers, apart from London, which was affected by Brexit. Even so, London barely maintained the tenth spot, with Berlin hot on its tail. There was strong movement from Beijing, which pulled away from a prior tie with Tel Aviv to become the clear second-place leader after Silicon Valley. Stockholm inched past Tel Aviv for third place, while Tel Aviv dropped to fourth. New York city (NYC) and Shanghai both moved up into a tie for fifth place. Los Angeles and Seoul are tied for seventh, and Boston and London remained in their respective prior positions. The rankings for 2019 are: 1.  Silicon Valley 2.  Beijing 3.  Stockholm 4.  Tel Aviv 5.  NYC 5.  Shanghai 7.  Los Angeles 7.  Seoul 9.  Boston 10. London The world is truly becoming flatter in terms of innovation and startup development. This year was the first year that we saw the majority of the startup ecosystems move upward. For years, there has been discussion on how access to information and the transfer of knowledge has helped close the gap between Silicon Valley and the rest of the world, but finally you see more tangible results. From our global deal flow across our nine accelerators and three venture capital funds, we see world-class entrepreneurs in many cities across the globe. The talent is getting better and better, especially in the areas of software development, UI / UX, and product development. The execution gap is shrinking between Silicon Valley and the rest of the world. The more difficult aspect to transfer is culture. is its secret sauce and differentiator, along with its density of talent and mentors to guide founders. Culture can be learned, and we do see it happening in Beijing and other locations, but this takes much longer than transferring execution know-how. Silicon Valley’s “pay it forward” culture For our study, we looked at eight factors: •  Funding Ecosystem & Exits •  Engineering Talent •  Active Mentoring •  Technical Infrastructure •  Startup Culture •  Legal & Policy Infrastructure •  Economic Foundation •  Government Policies & Programs. Here are the cumulative scores (out of a total of 80 possible points) for each of the top ten startup cities: Our analysis is primarily based on venture capital insight and metrics, and our on-the-ground perspective of innovation ecosystems in different parts of the world. This contrasts with other studies, which might rely more on surveys, or are not that knowledgeable about many markets in Asia, or don’t assess startups for actual investment purposes (as our team does). For example, while Singapore is well known as a strong hub for Fortune 1000 companies to set up their APAC headquarters, its startup activity, investments, and historic exits don’t come close to the activity in Seoul. South Korea has consistently been producing impactful tech companies — starting from the mid-90s, when Nexon (US $12 billion market cap) and Naver (US $23 billion market cap) were founded to today’s unicorns (companies with a valuation of more than US $1 billion) such as Coupang (US $9 billion valuation) and Toss (US $2.2 billion valuation). So several years ago, our team as a whole wasn’t that interested in investing in Singapore-based startups. There were too many government-backed accelerators, and a lot of startup activity on a road to nowhere since there weren’t many venture capital funds focused on the Series A rounds. But now, our team is bullish on Singapore. The quality of startups has greatly increased, there are more early-stage venture capital funds, and the noise-to-reality ratio has greatly improved. Even a few years ago, Singapore wouldn’t have been in our top 20 — but today it is. When analyzing a region’s startup ecosystem, factors such as the number of unicorns and exits are just one part of our overall assessment. Even though Beijing is second on our list this year and is also second in its number of unicorns, we've previously had Tel Aviv and Stockholm ranked as number two, with both cities having only single-digit numbers of unicorns. For our rankings, we also consider macro-trends, such as Asia’s rapidly growing middle class. While the global middle class is expected to grow from 3 billion to 4.9 billion people by 2030 and two-thirds of that growth will come from Asia. The overall promise of Asia’s growth and impact on innovation should be met with some reserve due to current geopolitical tensions, which includes the trade wars and corporate espionage. Many manufacturing centers have shifted out of China to Vietnam, and R&D projects have moved to Taiwan and South Korea. It is uncertain how these moves will affect the startup innovation engine in China. Vietnam and India are two promising markets where venture capitalists are increasingly active. Both have good to great engineering talent and a strong entrepreneurial culture and drive. Vietnam’s angel and venture capital ecosystem is still far behind Silicon Valley and at least 10 years behind India’s, but still holds a lot of promise. On the heels of our top ten list, we see up-and-coming startup ecosystems continuing to develop further, such as Singapore, Chicago, Paris, and others. Berlin was very close to re-entering the top ten and displacing London, so some on our team are already predicting that Berlin will be back in the top ten next year. Its entrepreneurial energy is palpable and we love many of the founders that we have met there. However, Berlin’s venture capital environment doesn’t seem to match this energy. Let me know what you think about our rankings — and tell us, what innovation ecosystems did we miss out on?

Top Ten Startup Ecosystems in the World 2019: Flat Earth Ahead

According to CB Insights’ unicorn tracker, unicorns have raised a combined total of nearly $257 billion. Data by startup and venture capital intelligence firm show a total of 310 private companies around the world valued at more than $1 billion as of January 2019. Last year, 112 new companies joined the global unicorn club, a 58% increase from the 71 new unicorns in 2017. CB Insights The collective worth of all unicorns currently identified by CB Insights — and published in a new infographic under 13 categories — is $1,052 billion. They have raised a combined total of nearly $257 billion. Credits: CB Insights The top 10 unicorns by market value are: Bytedance (valued at $75 billion) ($72 billion) Uber Didi Chuxing ($56 billion) ($47 billion) WeWork Airbnb ($29.3 billion) SpaceX ($21.5 billion) and ($20 billion) Palantir Stripe Juul Labs and Epic Games ($15 billion) Pinterest, Bitmain Technologies, and Samumed ($12 billion) , GrabTaxi, and Global Switch ($11 billion) Lyft In terms of countries represented in the list, most of the unicorns come from: 🇺🇸 United States with 150 unicorns, or 49% of the total — up 2 percentage points since August 2018; 🇨🇳 China (84, including 1 in Hong Kong), or 26% — down from 30% last summer; 🇪🇺 33 in the European Union (🇬🇧 16 in the UK, or 6%; 🇩🇪 9 in Germany; 🇫🇷 2 in France; 1 each in 🇪🇪 Estonia, 🇱🇺 Luxembourg, 🇲🇹 Malta, 🇵🇹 Portugal, 🇪🇸 Spain, 🇸🇪 Sweden); 🇮🇳 14 in India, or 4% The largest unicorns by valuation in each region are: 🇨🇳 Bytedance in China (valued at $75 billion); 🇺🇸 in the US ($72 billion); Uber 🇸🇬 GrabTaxi in Singapore and 🇬🇧 Global Switch in the UK (both valued at $11 billion); 🇮🇳 One97 Communications in India ($10 billion); 🇰🇷 Coupang in South Korea ($9 billion); 🇨🇭 Roivant Sciences in Switzerland and 🇮🇩 in Indonesia (both $7 billion). Tokopedia The 10 oldest unicorns are: VANCL (China), a unicorn since December 2010; (US) since May 2011; Palantir Airbnb (US) since July 2011; (Sweden) since December 2011; Klarna Trendy Group International (China) since February 2012; Pinterest (US) since May 2012; Fanatics (US) since June 2012; (US) and SpaceX (US) since December 2012; Cloudflare Mu Sigma (US) since February 2013; Automattic (US) since May 2013.

🦄 The world’s 310 unicorns are valued at over $1,000 billion

A Startup Unicorn is a company which has passed the billion dollars mark of valuation. Based on CB Insights research on Startup Unicorns. Variants of Unicorns include a Decacorn, made of companies valued at over $10 billion. And a Hectocorn, made of companies valued at over $100 billion. The top ten Startup Unicorns as of January 2019, comprise Bytedance, Uber, Didi Chuxing, WeWork, Lu.com, Airbnb , SpaceX, Palantir Technologies, Stripe and JUUL Labs. The US remains the country with the highest numbers of Unicorns, followed by China. The top five industries for Startup Unicorns comprise Fintech, Internet Software, and Services, E-commerce/marketplace, On-demand, Healthcare. The methodology used for the FourWeekMBA Unicorns Business Models Map Classifying a isn’t a simple task, a business model is made of many moving parts. And creating a representation of a company on a single characteristic or feature is too reductive. business model There are . Each aims at understanding how a company works, and how its engine moves. That engine is made of things like key partners, resources, processes and how a company makes money. several approaches to define a business model Those approaches start from assumptions about what a company is and what aspects to focus on. Therefore, any attempt at classifying business model might never be complete enough. For the sake of this article, I’ll put a label on each , by picking what I think is the most qualifying ingredient for the organization in the current moment. The aim is to provide you with as many business insights or at least stimulate your curiosity about these organizations. business model As any labeling system, it has many limitations. Just like putting a label on a person is reductive, putting one on a complex organization is even more simplifying. It is important to remark that this is not a science. A business strategy itself is made of assumptions about how the world works. In my opinion, the role of the entrepreneur is to test those assumptions in the real world as quickly as possible. When those assumptions turn out to be wrong, we’ll call them “failures.” Yet in reality, those are feedbacks about the world at that moment. For instance, an idea might be too early for the world to accept, and an entrepreneur which is trying to push it to the market will fail. It is important to notice that complex organization (think of , , and ) don’t rely on a single , but have combined them to create a unique organization that can capture a long-term competitive advantage. Amazon Google Microsoft business model pattern For instance, , from a subscription-based, to cloud as a service, to an online store, third-party stores and more. Amazon runs on several models While this is true for larger organizations, consolidated over the years; for smaller organizations, or unicorn startups a simpler business model allows them to scale up to a billion dollar valuation. I did my best to put the right label on each of the Unicorns, part of the FourWeekMBA Unicorns for 2019. This label comprises the main feature of that company from both product and monetization standpoint. Business Models The Unicorns come from a list of 311 companies by . CB Insights I hope you enjoy and get as many insights from it! The FourWeekMBA Unicorns Business Models For 2019 : digital short-video format model powered by AI Toutiao (Bytedance) business model : ride-sharing on-demand marketplace model Uber business model : digital media on-demand marketplace model Didi Chuxing business model : office space sharing/real-estate-as-a-service model WeWork business model : online finance marketplace model Lu.com business model : e-commerce/on-demand home-sharing marketplace model Airbnb : space transportation model SpaceX business model : big data-based enterprise SaaS model Palantir Technologies business model : payment marketplace model Stripe business model : consumer electronics model JUUL Labs business model : digital games marketplace model Epic Games business model : digital advertising model Pinterest business model : crypto mining blockchain-based model Bitmain Technologies business model : biotech-based model Samumed business model ride-sharing on-demand marketplace model Lyft business model: : ride-sharing on-demand marketplace model GrabTaxi business model : multi-tenanted data centers-as-a-service model Global Switch business model : cloud-based enterprise SaaS model Infor business model : drones products-based model DJI Innovations business model : marketplace and virtual bank model One97 Communications (operates Paytm) business model : mobile-based eCommerce/Marketplace for consumer products Coupang business model : digital currency exchange marketplace model Coinbase business model : on-demand grocery delivery services and marketplace model Instacart business model : team-communication-as-a-service freemium model Slack Technologies business model : daily-deal e-commerce/marketplace model Snapdeal business model : biotech in-license drugs and development model Roivant Sciences business model Other 60 business model patterns FourWeekMBA has put together in partnership with BMI Lab . Originally published at fourweekmba.com on January 19, 2019.

Visualizing Unicorns Companies For 2019

by Joseph Flaherty In 2007, Groupon took in the of what would ultimately be $1.4B of venture capital, raised with ambitions of reshaping shopping. For a while, it looked like group buying, social commerce, daily deals, or whatever you call the practice of time-limited sales paired with social pressure was really going to change buying behaviors in a big way. In the space of four years the category exploded: first million dollars Groupon became the fastest startup in history to reach a . billion-dollar valuation Founder Andrew Mason appeared on the covers of and . Forbes Wired even though it had no clear connection to any aspect of the search giant’s business. Google offered $6B to buy Groupon Not to be outdone by Google, . Amazon invested heavily in LivingSocial Shockingly, Facebook as well. copied the idea A slick business about Groupon, and the phenomena it generated, was published and blurbed by the likes of Andrew Ross Sorkin and Tony Hsieh. book Hundreds of VC-backed daily deal sites sprung up focusing on every geography, vertical, and buying gimmick you could imagine. So many startups were founded in this space that ! multiple daily deal aggregators got VC funding Poorly conceived were aired. SuperBowl commercials Groupon went public and at its peak commanded a . $16B market cap “Social shopping” wasn’t a platform in the way mobile is, but everyone in tech started thinking about how it would transform their market. The business model just didn’t work very well. under the stress of baking beignets at bargain basement prices. Daily deals turned out to be a great ad platform for indoor skydiving facilities and escape rooms, but that’s not the kind of business that changes commerce — It’s the kind that trades for 2X profits. Brunch spots broke There was a flash sale on GRPN stock that saw its value drop by 85%. . The clowder of copycats screeched their last. LivingSocial died a lonely death It wasn’t a total washout. Zulilly IPO’d and was . quickly acquired by QVC Wish has adapted the model and currently holds an . $8.5B valuation Coupang, the Korean alternative to Amazon, , though they thrived after pivoting into a full-stack ecommerce model. commands a $5B valuation Even after all the drama, Groupon is still around and has a $2.4B market cap. Many of these companies were and are successes. They may have fallen short of outrageously high expectations, but the model worked in some specific contexts. It just wasn’t a generally applicable approach and a lot of startups paid the price for chasing a trend that didn’t materialize. The real loss is the years of effort wasted by entrepreneurs blindly chasing after the “next big thing.” The media’s incentives are different than startups’ Why did Groupon get overhyped? The media has to drive an exciting narrative every day. The problem is, truly disruptive innovations don’t come along often. Interesting but ultimately marginal developments are oversold. For instance, in 2015, following in the wake of Facebook’s acquisition of Oculus, declared that “ ” Three years later, we’re still pawing at glass like morlocks. And when VR does arrive, there’s a good chance it’ll primarily be a video game accessory rather than a general purpose computing platform to rival smartphones. Time Magazine Virtual reality was about to change the world. . . . These were all proclaimed as the “Next Big Thing” by major news outlets. Good businesses can be built in any of these areas, but in retrospect, they did not become broadly lucrative trends. Personal Robots Gamification Wearables Today, “ ” is touted as a disruptive platform, but at present, it looks more like a new UI layer for Apple and Amazon services than a technology that will launch a hundred startups. voice These reporters aren’t purposefully misleading people. They’ll make bad predictions in much the same way VCs will make bad investments. It’s the job of founders to look at these trends critically and to remember that may be deadly to reporters, they can be fabulously lucrative to founders. boring ideas Startup who chase after these trends often find themselves stumped by “ ” The Platform Paradox. For the record, VCs also have a role in this, hyping your startups is part of the gig, but they also pay a heavy financial penalty when their companies go belly up. The solution? Embrace the use case The same year Groupon got started, Peter Gassner raised to fund , a vertically-focused (and very boring) CRM-style SaaS solution for life science companies. Its launch was little noted by the tech press. The product’s ability to streamline communication between pharma companies and clinical research organizations didn’t herald the arrival of any new tech paradigms. Incubators didn’t create special purpose funds to spawn imitators. $3M dollars Veeva However, many life sciences professionals thought it was the best startup in the space since Emil Erlenmeyer got into the flask making business and were willing to pay accordingly. Veeva went public a couple years after Groupon and today it’s worth over $10B. Considering Veeva only raised $7M in total venture capital, it is easily one of the most successful tech IPOs of the last half decade. Groupon was sold as a paradigm shift in ecommerce that would ripple through the broader economy. Veeva was “just” a well-conceived B2B product for a niche market. It turns out niches hold riches. Don’t believe the hype As wrote, “By the time there is a special purpose VC fund devoted to a trend, it is probably too late to build a meaningful company in that space.” David Frankel _One of the questions a VC gets most often is “what are you interested in?” Many investors have developed “themes” that…_hackernoon.com Why today’s tech themes are poor predictors of tomorrow’s hot startups Keep an eye on macro trends, but employ them in the service of a specific use case for a specific kind of customer. Technological fads come and go, but human use cases are durable and can be exceedingly valuable.

Startups, Beware the Next Big Thing

The relation of love to pain is one of the most difficult problems

Flytrap Is Spreading Fast and Has Compromised 10,000+ Users Worldwide. Codenamed "FlyTrap", this new Android Trojan malware has already according to a recent report from Zimperium, the global leader in mobile security. What makes this malware special is it employs social engineering tactics to compromise victim's Facebook accounts. FlyTrap targets Android users and can collect personal information such as: infected over 10,000 victims across more than 140 countries, their Facebook IDs, location information, email addresses, IP addresses, and the cookies and tokens associated with their Facebook accounts. The hackers can later disguise themselves as the victims to send more phishing links to the user's contacts via direct messages and posts or send them links covering others. They can also send them the updated, more dangerous malware after gaining the trust. How Does FlyTrap Malware Work? Zimperium's zLabs mobile threat research teams several previously undetected applications. Following their forensic investigation, the zLabs team determined this earlier undetected malware is part of a class of Trojans that employ social engineering tricks to compromise Facebook accounts. recently found Fake Applications The threat actors made use of several themes (such as free Netflix coupon codes, Google AdWords coupon codes, and voting for the best soccer team or player) that were popular among users. Originally available in Google Play and third-party stores, the application lured users toward downloading and trusting the application with high-quality designs and social engineering. After installation, the malicious application displays pages that engage the user and asks for a response from them. These are the nine malicious apps: GG Voucher (com.luxcarad.cardid) Vote European Football (com.gardenguides.plantingfree) GG Coupon Ads (com.free_coupon.gg_free_coupon) GG Voucher Ads (com.m_application.app_moi_6) GG Voucher (com.free.voucher) Chatfuel (com.ynsuper.chatfuel) Net Coupon (com.free_coupon.net_coupon) Net Coupon (com.movie.net_coupon) EURO 2021 Official (com.euro2021) Before the malware apps serving out the "promised goodies", to pick their vote or collect the coupon code or credits. For sure, there is no actual voting or coupon code for Netflix but just another trick to lure the users to give up their credentials. victims are told to log in with their Facebook accounts "Just like any user manipulation, the high-quality graphics and official-looking login screens are common tactics to have users take action that could reveal sensitive information," - zLabs researchers explained. " One difference of this trojan is that victims are actually logging in to the real Facebook website. However, while the user is logging into their official account, the FlyTrap Trojan is hijacking the session information using the techniques called "javascript injection". Then, the trojan uses victimized Facebook accounts to further spread its legs. By disguising as reliable owners, the malicious links and posts would seem legitimate. The malware would automate the process and send social posts and personal messages with links to the FlyTrap trojan. With one light touch, the trojan would propagate disinformation using the victim's device location information. Adding localized information to the social engineering attacks makes the spreading of this trojan highly effective from one victim to another. How to Protect Your Mobile Device In the worst cases, it can be painful or nearly impossible to, like any other personal data, get your data back - let alone the accounts that were broken into. That s why the best way to limit the loss is to ensure it never happens in the first place. Review SMS-Based 2FA of All Accounts You can use several other methods as your second "factor" that is more protected than text message-based verification. As mentioned, providing your phone number to a 3rd-party introduce the risk of data leak and further hijack other accounts. With a little investment, you can de-couple your cell phone number as a second factor. Security Keys Using a physical token (or security key) only for this purpose could help, such as the ones from Yubico called Yubikeys and Titan from Google. Most security key providers offer different form factors like USB-A, USB-C, lightning, or Bluetooth. No matter which one you choose, it's probably a physical thumb drive-shaped accessory that can fit on your keychain. When authentication is required, you need to plug the key into a USB port on your device, or, if it has an NFC wireless chip in it, hold the key up to your NFC-enabled phone. You can use security keys as secure logins on platforms like: , Google , Facebook , Dropbox , and Microsoft other sites that support or protocol. FIDO Y2F The main problem with keys is service compatibility. Another problem would be losing or misplacing a security key. For example, if keys are lost or stolen for people who put them on their keys, account lockouts are likely. Therefore, when you set up your key, you should set up a second backup key if anything bad happens to the first. 3rd-party Authenticator Apps You may already know how to use two-factor authentication to log into accounts. But may limit to using services that give a phone number or email address to receive a security code that requires active connections or phone services. This is a 2-factor authentication because: The phone with the app installed - Something you have. The time code in the app - Something you know. It also proves you have the device at the time period. Authenticator apps generate a time code to log into an account and provide stronger security Moreover, you don't need to be connected to the Internet to receive them, and they aren't vulnerable to being hacked via SIM hijacking. without the added privacy concern of giving out a phone number. Some accounts request you use a specific authenticator app, while others let you choose. Popular options include Google Authenticator (Android, iOS) and Microsoft Authenticator, supporting multiple accounts, and Authy, which also supports a range of accounts and offers secure cloud backups. Cyber Hygiene Although iPhone is less suspectable to hacking, configuration checks, jailbreak status, and dangerous network access are also useful for users to reduce attack vectors and mitigate risk. and are free and could offer various security checking including malicious apps flagging, Network access monitoring, and root status - for Android. Bitdefender McAfee provides malicious apps flagging, checking for potentially dangerous Wi-Fi networks, and if the iPhone has been jailbroken - for iOS. Lookout You Can't Protect What You Can't See. We have a process manager on laptops and desktops for monitoring. We can also increase our visibility on mobile devices' activity and background processes with some useful tools. - for iOS only. Lockdown Privacy - the easiest one I tried is Glasswire, which supports Android and Windows. Glasswire These tools are great for enhancing visibility and check if there is any new internet access from an app or background process. You can also quickly check if there is an app that consumes data that is out of scale. Another area that is worth reviewing is DNS. You can use this tool to check if there is any DNS request in a random format - some random domain name that is accessing by your device. This could be an indicator that the app is compromised and trying to send out information. Once you find out which one is contributing to the connection attempts, delete the app immediately. Key Takeaways Threat actors are leveraging common user misunderstandings that logging into the true domain is always secure. The targeted domains are popular social media platforms like Facebook and Google that people used to sign-up for websites. This malware campaign has been particularly powerful in harvesting social media users profiles from 140+ countries. These stolen accounts can be used as a botnet (or zombies) for various purposes: from expanding the popularity of pages/sites/products to spreading misinformation or political propaganda. Similar to any user manipulation, legitimate-looking login screens and high-quality graphics are typical tactics to have users give up sensitive information. In this case, while the user is logging into their official Facebook account, the FlyTrap Trojan is hijacking the information on the fly for wicked intent. FlyTrap is merely an example of the continuing, current threats against mobile devices intended to steal social media credentials. Mobile devices are often treasure troves of defenseless username/passwords to social media accounts, banking applications, and more. More importantly, the tools and techniques used by FlyTrap are not new but are effective enough because there is no advanced mobile endpoint security on these devices or in most devices. It would not take much for a malicious party to take FlyTrap or any other Trojan and modify it to target even more critical information. Thank you for reading. May InfoSec be with you🖖. This article was first published at: https://medium.com/technology-hits/how-to-keep-yourself-safe-from-the-flytrap-trojan-91c695fd390c Some definitions and explanations have been reworded from the following article: https://blog.zimperium.com/flytrap-android-malware-compromises-thousands-of-facebook-accounts/

How to Keep Yourself from Becoming a Victim of Flytrap Malware

The best tech stories published on hackernoon.com in the last 48 hours. Sign up for the newsletter today . [ . ] This Noon Notification is made possible by CreditStacks: Mastercard® for Software Developers. Issued by WebBank, Member FDIC How Data Analysis Helps Unveil the Truth of Coronavirus These days we are all scared of the new airborne contagious coronavirus (2019-nCoV). Even if it is a tiny cough or low fever, it might underlie a lethargic symptom. However, what is the real truth? By xyng17 Read the full story 🧩 Why Do We Need APIs Anyways? If you’ve been anywhere close to a computer engineer, you’d have definitely noticed them drop the term - API. A software engineer would use the term for so many varied purposes that it’s easy to lose the track of the conversation and left hanging in there, wondering, “Why the heck we need APIs?” By Mohit Mamoria Read the full story Create Your Own Full Page Slider Implementation with JQuery [Tutorial] In this tutorial you will learn how to create a simple mouse wheel scroller. The following solution is very basic. It can be greatly improved, but it just an example of how fast it can be implemented or a good starting point to create your own slider. By Edvinas Pranka Read the full story What is Modern Monetary Theory (MMT)? [My Two Cents] A Pocket Guide To Modern Monetary Theory and Why It Matters By Noam Levenson Read the full story If your goal is to be a software engineer, you've set the bar too low When I started coding, it was partially because I thought it’d be cool to make a computer do my bidding. By Patrick Lee Scott Read the full story How a Data Scientist Sees a Deck of Cards The Data Scientist Creativity Paradox By Baptiste Coulange Read the full story Why I Built a No Code Platform to Automate my Cryptocurrency Trading During the past couple of years, I’ve been building and running my own automated cryptocurrency trading strategies. While I am not as profitable as my naive past-self imagined, I did discover a few things that helped me grow, and ultimately change how I perceive and approach the markets. By Rj Bernaldo Read the full story Bitcoin in the Age of Quantum Computing Deemed as one of the biggest potential technological revolutions of recent history, quantum computing also poses security risks for the cryptocurrency space. Quantum-computing is able to harness quantum mechanics to reach data processing levels otherwise impossible with traditional computing. By Jacob Ramirez Read the full story Mean Reversion Trading Systems and Cryptocurrency Trading [A Deep Dive] Prices move in a wave like fashion, moving back and forth following a broader trend. While doing so, it often revolves around a mean. It might move across or bounce off the mean. Mean reversion systems are designed to exploit this tendency. By Alok Read the full story Painkillers, Vitamins and Misunderstood Startup Lessons If you’re a startup founder, you may be short of many things : funding, sleep, the will to live. By Sefi Read the full story Change Management: How To Use Narratives To Make Better Products The Unavoidable Essence of a Product Manager Role: How to succeed as a Change Manager through the power of Narratives. By Sara Tortoli Read the full story The Best Companies Built On Top Of Slack Entire companies are being built on top of the worlds fastest growing software. By Peter Schroeder Read the full story How to Design a Web Application: Software Architecture 101 So you’ve embarked on the entrepreneurial journey to build your own web application. You’ve got the idea in place, but the significance of getting the architecture right is extremely important. By Fahim ul Haq Read the full story How to Structure a PyTorch ML Project With Google Colab and TensorBoard Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard By Michael Li Read the full story Creativity is the Heart of Cybersecurity Insights from a Q&A with Deflect’s Founder, Kevin Voellmer By Elisheva Marcus Read the full story The Feedback Fairy 🧚‍♀️ Shouldn't feedback be one of the easiest things we do as designers or developers? Just look at Who Wants to Be A Millionaire. When you get stuck, just phone-a-friend and make a million dollars. Easy right? By Dane Lyons Read the full story [ . ] This Noon Notification is made possible by CreditStacks: Mastercard® for Software Developers. Issued by WebBank, Member FDIC The best tech stories published on hackernoon.com , delivered directly to your inbox. Sign up for the newsletter today .

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The Data Scientist Creativity Paradox Take a standard 52 deck of cards and draw two cards. Can you find something in common about these two cards? Can you conclude from this observation that someone sorted the deck in a special way? You may not be certain. Let's pick two more pairs of cards. Now that you have three data points, what is your conclusion: Someone arranged this deck in a special way or not? That's what most data scientist are doing in a daily basis. Given a dataset, we're trying to find surprising correlations. Most of the time, like with these cards, we don't exactly know what we are looking for. And, looking at the data, we see it: each pair of cards has the same suit! So we define an hypothesis: "Each pair of cards of this deck has the same suit." Finally, we have to verify if the first intuition is . statistically significant The probability to have two cards with the same suits is 1/4. As a consequence, the probability to have 3 consecutive pairs with the same suit is a bit less than 2%.  This is a meaningful discovery: Every pair of cards of this deck has the same suit with a p-value of 0.02. Except it is not. This deck has nothing particular, you only detected your own enthusiasm to find a result. The data scientist imagination paradox This is my new favorite imagination game, I take a deck of card and I try to find a link between each pair of card. With some training, I now manage most of the time to find something in common with every single pair of card. As so, finding something in common in a pair of cards has nothing particular in my own case. The data scientist imagination paradox : The more imaginative you are, the less significant will be your observations. Let's imagine there is a finite amount of patterns on data. And let's suppose that all these patterns are all as likely to happen on our data by chance. In other words, there is a chance to observe this pattern on random data. N 1/N And now let's have two data scientists : is not very creative, the amount of ideas he can think of is , way below . Captain Obvious C N is very creative, he can think at ideas. times more ideas than Marvin 100C 100 Captain Obvious and look at our data and a pattern in the data after looking at samples. Which one of them should we follow? Captain Obvious Marvin K Let's compute the probability that they detected something particular in random noise (assuming all the patterns are independent). Since Marvin is 100 more creative, he is 100 times more likely to discover something in noise! When we see a pattern in data, we underestimate all the other patterns that would have surprised us the same way. The Scientific method Are creative people condemned to try to be less creative? Hopefully not! The solution is to apply the scientific method: 1. Ask a question 2. Construct a hypothesis 3. Test with an experiment 4. Draw conclusions Unfortunately, a lot of data analysis apply it the wrong way: 3. Test with experiments 2. Construct a hypothesis 1. Ask a question that match our hypothesis and experiments 4. Draw conclusion If you decide on your hypothesis after making your experiments, you are confirming your bias, not learning. Let's go back on the deck of card. After making the hypothesis I need to draw 3 new pairs of cards as an experiment to confirm. "Each pair of cards of this deck has the same suit." And I can conclude that my hypothesis was wrong. You always need to validate your hypothesis on data you've never looked at before.

How a Data Scientist Sees a Deck of Cards

The 99% Accurate Machine Learning Algorithms You Shouldn't Buy Machine learning is now everywhere and seems to be able to solve most problems. But running neural network is not an easy task without a proper data science team. So more and more companies are issuing A.I. focused Request For Tender. One main challenge is: how to measure the accuracy of an algorithm? More than often we see requierements to one accuracy metric : Your Machine Learning algorithm needs to have over 90% accuracy. - another artificial intelligence Request For Tender This article will show that a high score can hide poor business performance. Detecting a rare problem is inspecting wind turbines since 2011. By 2019, we have acquired 4 millions of pictures. During the same period, our experts annotated only 6000 high criticity defects. Cornis On average, only 0.1% of the pictures contains a high criticity defect. The worst would be a system predicting no defect. Such a ridiculous system would still be 99.9% accurate on this database (6000 defects are only 0.1% of 4 millions  images). defect detection algorithm False negative Other metrics can give you better information on the performance.  Since high critical defects are what we don't want to miss, we can count how many defects the algorithm detects. We call this metric the rate. true positive If an algorithm detects well the 6000 defects of our database it has a 100% true positive rate. If it misses 600 defects, it has a 90% true positive rate. Is this the score you should ask for? Is a perfect 100% true positive rate telling that every detection is a defect? Not at all. This score only tells you that the solution doesn't miss any defect. But missing very few defects has a cost : the number of false detections. Let's imagine the dumb algorithm that says that every single image contains a defect. It will well detect 100% of the defects but also 100% of the images without defect... To judge a machine learning algorithm, you need to check at least four metrics : The rate: Defects detected true positive The rate: Defects missed false negative The rate: No defect and not detected true negative The rate: No defect but detected false positive Even if these four metrics are mandatory, they may not at all reflect any business value. You still need to use them to compute business KPI. Back to work To expertise a wind turbine, we process the images of 4 faces of the 3 blades. We slice the blade panoramas on small 256x256 pixels images to detect defects. We count on average 150000 images per wind turbine to analyse. If no defects are on this turbine, a 99% precise algorithm will still detect on average 1500 images. The main expertise KPI is the time spent per turbine. How much time can we gain by keeping only 1500 images on the turbine? We discovered the hard way that the answer can go from 99% of the expertise time to . no time at all If all the detections are around the same zone, the expertise can last only some minutes. If detections are happening about everywhere on the blade, expertise will be longer than without A.I. Better than a random 99% score, a KPI improvement. In 2019, Cornis announced expertise twice as fast thanks to A.I. This algorithm is far from 99% false positive performance. But with our tools you can perform quality expertise in half the time you used to. When you want to assess the quality of a machine learning algorithm don't ask for a magic 99% number. Ask for several evaluations and focus on how it will improve your processes.

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