ALEXANDRIA, Va. —The ad intelligence company AdImpact has launched a new political media buying software solution, Potomac, which the company is billing as a first of its kind centralized platform for planning, buying, revising, and reconciling political media campaigns.

AdImpact Launches Potomac Political Media Buying Software

In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

AI vs software outsourcing: An opportunity or a threat?

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(Reuters) - AMD said on Tuesday it plans to buy an artificial intelligence startup called Nod.ai as part of an effort to bolster its software capabilities. In its race to catch rival chipmaker Nvidia, Advanced Micro Devices plans to invest heavily in the ...

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Google Ups the Ante with Extended Software Support Google recently made headlines by announcing that its Pixel 8 phones will receive seven years of software updates, creating a new benchmark in the Android market.

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PC and printer major HP has launched a new software platform called “AI Studio”, designed to help data scientists and AI practitioners and a moveable 23.8-inch all-in-one PC designed for a ...

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Paxata

paxata

Data science is a rapidly developing sector of study. Its main goal is to translate vast amounts of records into valuable business insights. Implementing data science-based tools into your company can be highly beneficial. AI software is more efficient and accurate than humans have ever been. It all sounds great, so how come that only a minority of companies on the market are using or working on the application of data science solutions? Well, the process is not as simple as it may seem. Time-consuming cleaning and preparation of data for analysis The first and crucial step in order to use the full potential of massive amounts of data is its preparation. The process involves finding inadequate records and correcting or removing them from the base. Data cleansing is a mandatory process - otherwise, you can end up with false conclusions or predictions. To cut on the painstaking job you can use special tools. The most popular ones are Trifacta, OpenRefine, Paxata, Alteryx, Data Ladder, WinPure. You can also take advantage of libraries, for example, Python, Dplyr, or Pandas. The need for context of data to fully use the potential of AI capabilities Context analytics are based on understanding the importance of contextual history secluded from insights about entities in the process of finding conclusions beneficial for the business. By connecting big data with context, it is possible to determine patterns, trends, and relationships from both structured and unstructured data of an enterprise. Digital Transformation - yes, but for whose benefit? Naturally, first in the line for profits is the very company that initiated a digital revolution are leaders, but what about other sides involved like employees, distributors, customers, etc. If none of them gain any benefit they’ll perceive the transformation only as an additional problematic change. While implementing AI solutions into your employees' habits make sure to first gain their trust in the new technology. Don’t expect them to get all excited about the extra work you’re putting on them without giving a reason that really speaks to them. Remember, you force your team to use tools that may have enough potential to replace them in the future. Without the engagement of both the company and the rest of the sides involved in the process, digital transformation can become a failure. Need for qualified data analysts Data analyst plays a significant role in every company focused on development. This job position requires certain skills that an employee should master in order to make a real impact on the company. The key features your future data analyst should possess are: understanding how your company works, knowing its business strategy, market position, targets, both strong sides and weaknesses technical skills, understanding of data value chain people skills, communicate both with colleagues and data suppliers, understanding which information is safe to share and which are not critical thinking, investigating the significance of particular information ability to visualize data and its meaning to share insights Unaware CEO’s If you want to have a successful business that uses the full potential of data science analysis you have to start at the top. Only a company run by a CEO who understands the significance of data management and has enough knowledge to be an active side in a conversation concerning modern analytics like the usage of AI, for instance, can make smart decisions positioning their business on the top of its field. Data Science transformation - 3 tips on how to take full advantage of it. Ensure an easy access to data for as many decision-makers as possible. Their influence determines a success or a failure of your business. Stay flexible. The speed of data, inputs, outputs, and requirements will change every month, and to take full advantage of new information you have to be prepared to make changes frequently in your data strategy. Start with a small project that essentially resembles the crucial purposes and values of your company. The process of successful implementation of data science into your business may turn out to be problematic. Concentrate on a project where you can measure the possible outcome and which you can complete within a year. Data science can be from retail to finance, and many more. The possibilities are limitless. The biggest players on the market are already aware of the benefits data science has to offer. SMBs are observing and learning from their mistakes. applied in diverse industries, Soon, complex data analysis with AI solutions will become a standard - necessary to stay and grow in the market.

Data Science Innovations: 5 Main Obstacles in the Implementation Process and How To Overcome Them

A brief infographic at the end of this blog. AI — Artificial Intelligence is to mimic human intelligence for better life. The invention of that learn and perform actions took place in the classic times. Alongside , there were many fictional stories and dramas depicting artificial things and their immense potentials. artificial things by Calculus Ratiocinator Llull Marry Shelly’s Frankenstein? You must watch it if you haven’t. The idea behind machines being able to imitate humans, and their cognitive process like , , and finding out is, analysing sorting patterns — which means machines can simulate any process of formal reasoning (from ). Church-Turing thesis Wiki In common terms, machines can imitate anything with connected and meaningful information (datasets). Theory that backed up the brains of creators like and . Allen Newell, Herbert Simon, John McCarthy, , Marvin Minsky Arthur Samuel PDAbM — Public Display of Attention by Machine ;) When machines can’t watch someone misspelling their creator’s name, Well, these are some of the bold stories from our past around (Artificial Intelligence). If we look into AI’s present state and what we are up to — is prevailing from that are online to we have in our AI AI (Artificial Intelligence) bots things home. The best part is, we now have of helping us and r. cavalcades AI machines stay connected, informed, only do things that matte For the later ones, and objects that barely need our attention come hand in hand. automated tools Imagine the days when speech recognition will have transformed into ‘ ’. recognizing intents from eyeballs’ dilation range Or, one that is called which stores the behavioural pattern of every particle of a subject. Which on requirement lets the subject travel through the encrypted particle patterns and produces a brand new outcome. Dr. Quantum Do you still think ? that it lacks a unit of time? Here is the . time never stops Chronon Let’s quickly jump into the that are making our day-day life darn effortless! list of 50 AI (Artificial Intelligence) tools AI “This as a hitchhiker is so smart which is driving to our desired destination having our least conscious of what’s happening” Artificial Intelligence for Vision — helps you to organise media libraries Clarifai — visual analysis for medical and transport industry Cortica — image detection for a range of uses and industries Deepomatic — brand and face recognition DeepVision — makes satellite imagery useful Descartes Labs — the first intelligent cloud video platform Flixsense — computer vision for automotive and human detection FotoNation — matches similar products and helps to suggests combinations GrokStyle — facial recognition Haystack — uses facial recognition to help you decided on job candidates HireVue Artificial Intelligence for IoT/IIoT learns about shoppers behaviour in your store Black — helps to unify application development and data science C3 IoT — sensor analytics solution for businesses KONUX — machine learning for manufacturing process optimisation Imubit — a range of workflow optimisation products for fuel and industry Maana — helps you develop, deploy, and operate industrial apps Predix — helps renewable energy companies utilise their data better Planet OS — manufacturing analytics Sight Machine — automates data engineering for data science Sentenai — add a voice assistant to your connected product Snips — Artificial Intelligence for Development develop more adaptive, trusted and programmable AI models Bonsai — helps predict project timelines Deckard.ai — adds intelligent decision making to web and mobile apps Fuzzy.ai — connecting projects with the right team Gigster — augments your coding environment with web available knowledge Kite — deep learning platform for prediction and personalisation Layer 6 AI — makes developing chatbots for your business easy Morph — make your bot smarter, by helping it self learn Ozz — rapidly web and mobile app testing RainforestQA — increase server uptime and predict downtime SignifAI — Artificial Intelligence for Data Analytics — helps with sentiment analysis, search relevance, and more CrowdFlower — data science platform for prototype, deploy, and run at scale Dataiku — enterprise data science platform for R&D and production DataScience — platform for collaborating, building and deploying Domino Data Lab — makes DS accessible to analysts with Open Source algorithms Exploratory — helps you learn, work, and play with machine learning models Kaggle — makes data science teams more productive RapidMiner — helps DS teams put machine learning models into production Seldon — a platform to build, test, and deploy AI algorithms SherlockML — research engine, capable of discovering complex patterns in data Spark Artificial Intelligence for BI (Business Intelligence) LOL! behavioural AI for IoT Arimo — a suite of intelligent applications for enterprise Ayasdi — a range of products to improve enterprise products DataRobot — discovers events and breaking information before the news Dataminr — helps you to answer questions about your business Electra by Lore — a smarter Salesforce Einstein — adds intelligent decision making to web and mobile apps Fuzzy AI — helps you index, search, visualise and analyse your data Logz.io — is a framework for temporal pattern recognition and prediction NXT AI — to transform raw data into useful information automatically Paxata — Seems like a lot? There are many other routine AI technologies that give us precise and actionable data. The better the data are used, the better our future will be. If you come across any noteworthy AI technology, add them in the comments. As I promised, here is the infographic of List Of 50 Unique AI Technologies What else! Leave some 👏👏👏 :)

List Of 50 Unique/Routine AI Technologies

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

Unsupervised Investments (I): A Guide to AI Investors

Emotion As A Service: AI’s Next Market Is Your Heart

العاطفة كخدمة: السوق القادمة للذكاء الاصطناعي هي قلبك

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La emoción como servicio: el próximo mercado de la IA es tu corazón

Emosyon kòm yon sèvis: pwochen mache AI a se kè ou

感情をサービスとして: AIの次の市場はあなたの心

អារម្មណ៍ជាសេវាកម្ម៖ ទីផ្សារបន្ទាប់របស់ AI គឺជាបេះដូងរបស់អ្នក។

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Emotion seSevhisi: AI Inotevera Musika Mwoyo Wako

ስምዒት ከም ኣገልግሎት፡ ቀጻሊ ዕዳጋ AI ልብኻ’ዩ።

Cảm xúc như một dịch vụ: Thị trường tiếp theo của AI là trái tim của bạn

The Isekai Archetypes of Computer Programming

From Vietnamese Upbringing to Marketing to a Global Audience: a Candid Convo with Linh Dao Smooke

ভিয়েতনামী লালন-পালন থেকে বিপণন থেকে একটি বিশ্বব্যাপী দর্শক: লিন দাও স্মুকের সাথে একটি অকপট কনভো

Von der vietnamesischen Erziehung zum Marketing für ein globales Publikum: ein offenes Gespräch mit Linh Dao Smooke

De la educación vietnamita al marketing para una audiencia global: una conversación sincera con Linh Dao Smooke

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Wireshark, the free open-source packet sniffer and network protocol analyzer, has cemented itself as an indispensable tool in network troubleshooting, analysis, and security (on both sides). This blog post delves into the features, uses, and practical tips for harnessing the full potential of Wireshark, expanding on aspects that may have been glossed over in discussions or demonstrations. Whether you're a developer, security expert, or just curious about network operations, this guide will enhance your understanding of Wireshark and its applications. https://www.youtube.com/watch?v=QVWRomT2Ppo&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my which covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my If you want to get back to Java after a while, check out my , Debugging book Java Basics book. Java 8 to 21 book . Introduction to Wireshark Wireshark was initially developed by Eric Rescorla and Gerald Combs and designed to capture and analyze network packets in real-time. Its capabilities extend across various network interfaces and protocols, making it a versatile tool for anyone involved in networking. Unlike its command-line counterpart, tcpdump, Wireshark's graphical interface simplifies the analysis process, presenting data in a user-friendly "proto view" that organizes packets in a hierarchical structure. This facilitates quick identification of protocols, ports, and data flows. The key features of Wireshark are: Eases the analysis of network packets compared to command-line tools. Graphical User Interface (GUI): Displays packet data in a tree structure, simplifying protocol and port identification. Proto View: Supports a wide range of network interfaces and protocols. Compatibility: Browser Network Monitors Firefox and Chrome contain a far superior network monitor tool built into them. It is superior because it is simpler to use and works with secure websites that are out of the box. If you can use the browser to debug the network traffic, you should do that. In cases where your traffic requires low-level protocol information or is outside of the browser, Wireshark is the next best thing. Installation and Getting Started To begin with, Wireshark, visit their for the download. The installation process is straightforward, but attention should be paid to the installation of command-line tools, which may require separate steps. Upon launching Wireshark, users are greeted with a selection of network interfaces, as seen below. Choosing the correct interface, such as the loopback for local server debugging, is crucial for capturing relevant data. official website When debugging a Local Server (localhost), use the loopback interface. Remote servers will probably fit with the en0 network adapter. You can use the activity graph next to the network adapter to identify active interfaces for capture. Navigating Through Noise with Filters One of the challenges of using Wireshark is the overwhelming amount of data captured, including irrelevant "background noise," as seen in the following image. Wireshark addresses this with powerful display filters, allowing users to hone in on specific ports, protocols, or data types. For instance, filtering TCP traffic on port 8080 can significantly reduce unrelated data, making it easier to debug specific issues. Notice that there is a completion widget on top of the Wireshark UI that lets you find out the values more easily. In this case, we filter by port , which is the port typically used in Java servers (e.g., Spring Boot/tomcat). tcp.port == 8080 But this isn't enough, as HTTP is more concise. We can filter by protocol by adding to the filter, which narrows the view to HTTP requests and responses, as shown in the following image. http Deep Dive into Data Analysis Wireshark excels in its ability to dissect and present network data in an accessible manner. For example, HTTP responses carrying JSON data are automatically parsed and displayed in a readable tree structure, as seen below. This feature is invaluable for developers and analysts, providing insights into the data exchanged between clients and servers without manual decoding. Wireshark parses and displays JSON data within the packet analysis pane. It offers both hexadecimal and ASCII views for raw packet data. Beyond Basic Usage While Wireshark's basic functionalities cater to a wide range of networking tasks, its true strength lies in advanced features such as ethernet network analysis, HTTPS decryption, and debugging across devices. These tasks, however, may involve complex configuration steps and a deeper understanding of network protocols and security measures. There are two big challenges when working with Wireshark: Decrypting HTTPS traffic requires additional configuration but offers visibility into secure communications. HTTPS Decryption: Wireshark can be used to troubleshoot network issues on various devices, requiring specific knowledge of network configurations. Device Debugging: The Basics of HTTPS Encryption HTTPS uses the Transport Layer Security (TLS) or its predecessor, the Secure Sockets Layer (SSL), to encrypt data. This encryption mechanism ensures that any data transferred between the web server and the browser remains confidential and untouched. The process involves a series of steps, including handshake, data encryption, and data integrity checks. Decrypting HTTPS traffic is often necessary for developers and network administrators to troubleshoot communication errors, analyze application performance, or ensure that sensitive data is correctly encrypted before transmission. It's a powerful capability in diagnosing complex issues that cannot be resolved by simply inspecting unencrypted traffic or server logs. Methods for Decrypting HTTPS in Wireshark Decrypting HTTPS traffic should only be done on networks and systems you own or have explicit permission to analyze. Unauthorized decryption of network traffic can violate privacy laws and ethical standards. Important: Pre-Master Secret Key Logging One common method involves using the pre-master secret key to decrypt HTTPS traffic. Browsers like Firefox and Chrome can log the pre-master secret keys to a file when configured to do so. Wireshark can then use this file to decrypt the traffic: Set an environment variable ( ) to specify a file where the browser will save the encryption keys. Configure the Browser: SSLKEYLOGFILE Use Wireshark to capture the traffic as usual. Capture Traffic: Point Wireshark to the file with the pre-master secret keys (through Wireshark's preferences) to decrypt the captured HTTPS traffic. Decrypt the Traffic: Using a Proxy Another approach involves routing traffic through a proxy server that decrypts HTTPS traffic and then re-encrypts it before sending it to the destination. This method might require setting up a dedicated decryption proxy that can handle the TLS encryption/decryption: Tools like Mitmproxy or Burp Suite can act as an intermediary that decrypts and logs HTTPS traffic. Set Up a Decryption Proxy: Ensure the client's network settings route traffic through the proxy. Configure Network to Route Through Proxy: Use the proxy's tools to inspect the decrypted traffic directly. Inspect Traffic: Integrating tcpdump with Wireshark for Enhanced Network Analysis While Wireshark offers a graphical interface for analyzing network packets, there are scenarios where using it directly may not be feasible due to security policies or operational constraints. tcpdump, a powerful command-line packet analyzer, becomes invaluable in these situations, providing a flexible and less intrusive means of capturing network traffic. The Role of tcpdump in Network Troubleshooting tcpdump allows for the capture of network packets without a graphical user interface, making it ideal for use in environments with strict security requirements or limited resources. It operates under the principle of capturing network traffic to a file, which can then be analyzed at a later time or on a different machine using Wireshark. https://www.youtube.com/watch?v=nLXu3_fzHhQ&embedable=true Key Scenarios for tcpdump Usage: In places like banks or government institutions where running network sniffers might pose a security risk, tcpdump offers a less intrusive alternative. High-security Environments: Debugging issues on a cloud server can be challenging with Wireshark due to the graphical interface; tcpdump captures can be transferred and analyzed locally. Remote Servers: Customers may be hesitant to allow third-party tools to run on their systems; tcpdump's command-line operation is often more palatable. Security-conscious Customers: Using tcpdump Effectively Capturing traffic with tcpdump involves specifying the network interface and an output file for the capture. This process is straightforward but powerful, allowing for detailed analysis of network interactions: The basic command structure for initiating a capture involves specifying the network interface (e.g., for wireless connections) and the output file name. Command Syntax: en0 Once the command is run, tcpdump silently captures network packets. The capture continues until it's manually stopped, at which point the captured data can be saved to the specified file. Execution: The file generated by tcpdump can be opened in Wireshark for detailed analysis, utilizing Wireshark's advanced features for dissecting and understanding network traffic. Opening Captures in Wireshark: The following shows the tcpdump command and its output: $ sudo tcpdump -i en0 -w output
Password:
tcpdump: listening on en, link-type EN10MB (Ethernet), capture size 262144 bytes
^C3845 packets captured
4189 packets received by filter
0 packets dropped by kernel Challenges and Considerations Identifying the correct network interface for capture on remote systems might require additional steps, such as using the command to list available interfaces. This step is crucial for ensuring that relevant traffic is captured for analysis. ifconfig Final Word Wireshark stands out as a powerful tool for network analysis, offering deep insights into network traffic and protocols. Whether it's for low-level networking work, security analysis, or application development, Wireshark's features and capabilities make it an essential tool in the tech arsenal. With practice and exploration, users can leverage Wireshark to uncover detailed information about their networks, troubleshoot complex issues, and secure their environments more effectively. Wireshark's blend of ease of use with profound analytical depth ensures it remains a go-to solution for networking professionals across the spectrum. Its continuous development and wide-ranging applicability underscore its position as a cornerstone in the field of network analysis. Combining tcpdump's capabilities for capturing network traffic with Wireshark's analytical prowess offers a comprehensive solution for network troubleshooting and analysis. This combination is particularly useful in environments where direct use of Wireshark is not possible or ideal. While both tools possess a steep learning curve due to their powerful and complex features, they collectively form an indispensable toolkit for network administrators, security professionals, and developers alike. This integrated approach not only addresses the challenges of capturing and analyzing network traffic in various operational contexts but also highlights the versatility and depth of tools available for understanding and securing modern networks. Also published . here

Wireshark & tcpdump: The Debugging Duo For Network Troubleshooting

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Alyvia Walters, Rutgers University, USA; (2) Tawfiq Ammaris, Rutgers University, USA; (3) Kiran Garimella, Rutgers University, USA; (4) Shagun Jhaver, Rutgers University, USA. Table of Links Abstract & Introduction Background and Related Work Methods The Tools of CRT Meme Production Knowledge Production in a Post-Truth World Conclusion & References Knowledge Production in a Post-Truth World Memes are an excellent vehicle for making hot-button political issues digestible to the average person. However, almost none of the CRT memes we analyzed actually got its definition “right.” Instead, it appears that the most salient rhetorical tools across all 27 of these highly-circulated memes revolved around creating the most convincing definitions of both critical race theory, as that was the topic at hand, and antiracism, as convincing others that an opinion is not-racist is the only socially-palatable way to speak on race in the US in the 2020s. That said, what does this lack of attention to institutional definitions mean in a “post-truth” society, and what should we do about it? Political epistemology allows us to theorize on the importance of these memes as sensemaking and sensegiving tools for the public, and allows us to question the importance of community-based political knowledge over “real” institutional knowledge. In the context of CRT memes, the institutional definition of critical race theory does, of course, matter in a general sense. The work flowing from this definition has produced massive change at institutional and individual levels, and scholars, writers, and activists use these ideas to dismantle oppressive systems globally (Delgado and Stefancic 2023). However, that definition is not the one doing political work within these memes, and we would be missing the point entirely if we critique them as simple misinformation or fake news. In a fact-checking sense, all of these memes, both anti- and pro-, are largely false, but it is not useful to write them off as such. This technically false information is filling an information void for people who have likely never before heard of critical race theory, and that means it is these definitions–not the institutional ones–that are doing political work. The discourse is not actually about CRT; CRT simply became a catch-all phrase to hold discourse about race. In reality, the discourse revealed in these memes is about how the US should handle race moving forward, and how we define what is racist and what is antiracist. The rhetoric within this discussion presents another point of interest: despite research that suggests overt racism has become more acceptable in a post-Trump America, explicit appeals to racism were not present at all, even in antiCRT images. After the election of Donald Trump as the US President, several studies have indicated that explicit (Gantt Shafer 2017) or nearly-explicit racism (Schaffner et al. 2018) became a usable mechanism for Republicans in ways that it has not been since the 1950s and 60s (Mendelberg 2001). These studies argue that the president’s rhetoric ushered in a new era of acceptability of overt racism. However, in the case of the highly-circulated CRT memes we analyzed, this does not appear to be true. While we would argue that, based on Bonilla-Silva (1997)’s definition of racism, antiCRT memes are forwarding a racist agenda, none of those collected use overt racism in the rhetorical style of far-right groups. Instead, they trend toward the more traditionallypalatable implicit rhetorics that have been successful among conservative voters in the past (Mendelberg 2001). This is, perhaps, a surprising outcome of this analysis, and may indicate that there is still wider-spread conservative appeal toward implicit rather than explicit racism. While these implicitly racist appeals follow a long tradition of political rhetoric, the context in which they operate has changed. Following the election of Donald Trump and the ushering in of the “post-truth” era, we argue that implicit racism, in particular, runs the risk of being classified simply as disinformation. It is disinformation, for example, to posit that “the only remedy to past discrimination is present discrimination,” as cluster 22 does, but it is also much more than that: it is a tapping in to white supremacist understandings of what antiracism is. This, according to Cassam (2021), is one of the largest risks we run by taking post-truth as a political epistemology. Rather than understanding these rhetorics as an epistemological formulation of conservative politics, post-truth as a lens for understanding our current political moment could misconstrue this type of rhetoric as simple disregard for truth rather than a calculated dog whistle. This leads us to two main takeaways. First, institutional definitions matter, but they have little material meaning if the public is defining terms otherwise, especially through highly transmissible and easily digestible artifacts such as memes. Second, fact-checking as a practice perhaps misses the mark if it only seeks “truth” in a traditional sense, thereby framing false claims as dismissible, post-truth politics. While it is important to assert truth in a misinformation landscape, it is perhaps more important to understand what the actual issue at the heart of the political discourse is, what the stakes are, and what the use-value of the term being wielded is in order to disrupt oppressive practices and support emancipatory ones appropriately.

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