Reboot Communications Ltd.’s Post

BANGIN' #OSINT RESOURCE Stumbled across a pretty cool #osint tool. Uses a matrix type of set up to perform searches. Enter the search query, choose the type of asset it is or AI search then platform to use. Some pretty good results from the AI powered searches. Some tweaking could really pull some decent data. Cylect.io is an AI-powered opensource intelligence (OSINT) search tool designed to help users locate information on people, usernames, documents, companies, etc from publically accessable sources across the internet. It can assist in finding hard-to-locate data by utilising powerful AI and natural language processing capabilities to sift through online data and uncover relevant information based on user queries. Key Features - AI-Driven Search: Cylect.io leverages advanced AI algorithms to understand user queries and search for relevant data across the internet. - OSINT Capabilities: It is specifically designed for open source intelligence (OSINT) tasks, enabling users to uncover valuable public data for intelligence purposes. - Data Aggregation: Cylect.io aims to aggregate and integrate multiple AI tools and data sources into a single platform, streamlining the OSINT workflow. - Open Source Tools: The platform provides access to various free cybersecurity and OSINT tools contributed by the community. https://cylect.io/ #HVCK

📣 Come and join HK Fintech Week 2023! 🚀   🎯 Booth Name: Mastering Cyber Security for the Future of FinTech 🔒 Booth Slogan: Securing Financial Intelligence: Unleashing the Power of AI, NLP, and Cyber Security 📍 Booth Location: EE17 💡 At #CheckPointSoftware, we're passionate about harnessing the power of cutting-edge technologies to safeguard financial intelligence in the digital era.   Join us at our booth to explore how we're revolutionizing the industry through the convergence of AI, NLP, and cybersecurity. 🔒 Here's what you can expect at our booth:   1️⃣ AI-enabled accounting service for growing businesses.: Experience firsthand how we leverage the power of artificial intelligence to assess a business’s performance to create an alternative credit score by looking into their business behavioural patterns.   2️⃣ Natural Language Processing (NLP) Innovations: Explore our groundbreaking NLP solutions tailored for the prospectus before an initial public offering (IPO). Witness how our advanced language models can analyze and understand complex financial texts, enabling Pre IPO verification.   3️⃣ Harness the power of generative AI : Streamline the due diligence of ESG and annual reports against global standards and regulatory requirements.   4️⃣ Cybersecurity Demonstrations: Immerse yourself in live demonstrations showcasing our state-of-the-art Cyber Threat Map. Expanding our zero-day protection, introducing our innovative AI-powered engine to prevent local and global brand impersonation employed in cyber attacks, ...   5️⃣ Expert Insights: Engage with our team of cybersecurity and AI experts who will be available to provide valuable insights into the intersection of AI, NLP, and cybersecurity in the fintech domain. Gain actionable knowledge and learn about the latest trends and best practices.   📢 Don't miss this opportunity to witness how we're securing financial intelligence by unleashing the power of AI, NLP, and cybersecurity. Join us at HK Fintech Week 2023 and be a part of the future of fintech security!   🌐 To learn more about our company and our innovative solutions, visit our website: https://lnkd.in/gdWJ66gD 📢 Stay tuned for updates and announcements leading up to the event by following #HKFintechWeek2023 and #SecuringFinancialIntelligence.   See you at HK Fintech Week 2023! 🤝🔒💼 #HKFintechWeek #FintechRevolution #AI #NLP #Cybersecurity #SecureFinancialIntelligence Thanks Wizpresso 濃說 & BINERY for the support!

Can AI in intelligence investigations identify and predict emerging threats before they materialize? How does it achieve this? Yes, AI can play a pivotal role in identifying and predicting emerging threats before they materialize in intelligence investigations. This capability is grounded in several core functionalities of AI: 1. Predictive Analytics: AI algorithms can analyze historical and current data to forecast future events or behaviors. By identifying patterns, trends, and anomalies in vast datasets, AI can predict potential security threats or criminal activities before they occur. This analysis can include data from a wide range of sources, such as financial transactions, communication patterns, social media activities, and more. 2. Machine Learning Models: Machine learning (ML), a subset of AI, allows systems to learn from data, identify patterns, and make decisions with minimal human intervention. Over time, as these models are exposed to more data, their predictive accuracy improves. For emerging threats, ML models can adapt to new information, making them adept at spotting novel or evolving threats that haven't been seen before. 3. Natural Language Processing (NLP): NLP enables AI to understand and interpret human language within large volumes of text data. This capability is crucial for monitoring online communications, social media, and news sources for early signs of potential threats, such as discussions of radical ideologies, plans for unlawful activities, or the spread of misinformation campaigns. 4. Anomaly Detection: AI systems are highly efficient at detecting outliers or anomalies in data that deviate from the norm. This function is critical for uncovering subtle signs of emerging threats, such as unusual financial transactions, atypical travel patterns, or strange network activity, which may indicate planning stages of illegal acts or security breaches. 5. Data Fusion and Cross-Linking: AI can integrate and analyze data from disparate sources, creating a holistic view of potential threats. By correlating information across different datasets, AI can uncover hidden connections and relationships that might indicate coordinated activities or emerging threats that would be difficult to detect through manual analysis. 6. Continuous Learning and Adaptation: AI systems, particularly those using machine learning, continuously update their models based on new data. This means they can adapt to the changing nature of threats over time, recognizing new patterns of behavior as they emerge. AI achieves these capabilities through a combination of advanced computing power, sophisticated algorithms, and access to large datasets. https://www.inteli-ai.com/ #Lawenforcement #Lawenforcementagency #Investigations #Osint #Intelligence #Intelligenceinvestigations #Cyberintelligence

🔍 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗞-𝗙𝗼𝗹𝗱 𝗖𝗿𝗼𝘀𝘀-𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗔 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 🔍 In the meticulous world of Deep Neural Network (DNN) development, K-Fold Cross-Validation is the gold standard for model evaluation. Let’s unfold the mathematical blueprint of K-Fold and overlay it with a tapestry of security best practices to safeguard our validation process. 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝗽𝗶𝗻𝗻𝗶𝗻𝗴𝘀: At its core, K-Fold Cross-Validation divides a dataset ‘D’ with ‘n’ instances into ‘k’ mutually exclusive folds. This stratified sampling ensures that each fold is a good representative of the whole. Mathematically, for each fold ‘i’: . We define the training set ‘T_i’ as ‘D - D_i’. . We validate on ‘V_i = D_i’. . We compute the error ‘E_i’ for each fold. . The cumulative insight is the average error ‘E_avg = (1/k) * sum(E_i for i=1 to k)’. This rigorous statistical approach not only enhances model robustness but also mitigates overfitting, ensuring a generalizable performance across unseen data. 🔐 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀: 🔐 𝗗𝗮𝘁𝗮 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻 & 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Secure your datasets with the latest encryption standards and establish stringent access controls. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Implement cross-validation using secure random seed settings to maintain consistency while preventing predictability, using notation such as ‘random_state=42’. 𝗣𝗲𝗲𝗿 𝗥𝗲𝘃𝗶𝗲𝘄 & 𝗖𝗼𝗱𝗲 𝗔𝘂𝗱𝗶𝘁𝗶𝗻𝗴: Regularly conduct thorough code reviews to spot and address potential security vulnerabilities. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Ensure your computational environment is fortified with up-to-date security measures and is continuously monitored for integrity during validation runs. By intertwining the mathematical rigor of K-Fold Cross-Validation with uncompromising security measures, we can trust our DNNs to perform reliably and ethically in real-world scenarios. Let’s not just build AI; let’s build trust in AI. 📝 #DeepLearning #KfoldValidation #DataScience #MachineLearning #AISecurity #EthicalAI #ArtificialIntelligence #DataIntegrity #ModelEvaluation

More needs to be done to make #LLMs optimized for Chain-of-Thought (#CoT) reasoning safer, according to adversarial testing research from #Anthropic. What is adversarial testing and why does it matter? Automated and human safety testers create adversarial prompts that push a model for harmful behaviors. They then align the model with stated ethics and human values. This can involve fine-tuning algorithms, system prompts, and safety guardrails along with writing additional datasets to train the model to perform within safety guidelines. The ultimate safety testing is called red-teaming. Red teamers use adversarial testing to bypass security measures, expose biases in training data, and exploit vulnerabilities before they impact end users. Red teaming simulates the most toxic and malicious attacks that may be perpetrated on a model with the goal of aligning its behavior safely and ethically. This new research tests current adversarial techniques and concludes that “standard behavioral training techniques may need to be augmented with techniques from related fields…or entirely new techniques altogether,” credit: ”Sleeper Agents” paper in comments. More on red teaming, here: https://lnkd.in/e_zS2vMW If you build AI models, agents, virtual assistants or #chatbots, when do you perform safety testing? Reply in comments- #ai #llm #nlp #genai #aiethics #aibias #promptengineer #conversationalai #redteaming #adversarial #responsibleai

Awesome article by the great and powerful ⁦‪Thomas Roccia on using Large Language Models for threat intelligence. Be sure to give it a read! #ai #cti https://lnkd.in/efX2xg5G

Can AI in intelligence investigations identify and predict emerging threats before they materialize? How does it achieve this? Yes, AI can play a pivotal role in identifying and predicting emerging threats before they materialize in intelligence investigations. This capability is grounded in several core functionalities of AI: 1. Predictive Analytics: AI algorithms can analyze historical and current data to forecast future events or behaviors. By identifying patterns, trends, and anomalies in vast datasets, AI can predict potential security threats or criminal activities before they occur. This analysis can include data from a wide range of sources, such as financial transactions, communication patterns, social media activities, and more. 2. Machine Learning Models: Machine learning (ML), a subset of AI, allows systems to learn from data, identify patterns, and make decisions with minimal human intervention. Over time, as these models are exposed to more data, their predictive accuracy improves. For emerging threats, ML models can adapt to new information, making them adept at spotting novel or evolving threats that haven't been seen before. 3. Natural Language Processing (NLP): NLP enables AI to understand and interpret human language within large volumes of text data. This capability is crucial for monitoring online communications, social media, and news sources for early signs of potential threats, such as discussions of radical ideologies, plans for unlawful activities, or the spread of misinformation campaigns. 4. Anomaly Detection: AI systems are highly efficient at detecting outliers or anomalies in data that deviate from the norm. This function is critical for uncovering subtle signs of emerging threats, such as unusual financial transactions, atypical travel patterns, or strange network activity, which may indicate planning stages of illegal acts or security breaches. 5. Data Fusion and Cross-Linking: AI can integrate and analyze data from disparate sources, creating a holistic view of potential threats. By correlating information across different datasets, AI can uncover hidden connections and relationships that might indicate coordinated activities or emerging threats that would be difficult to detect through manual analysis. 6. Continuous Learning and Adaptation: AI systems, particularly those using machine learning, continuously update their models based on new data. This means they can adapt to the changing nature of threats over time, recognizing new patterns of behavior as they emerge. AI achieves these capabilities through a combination of advanced computing power, sophisticated algorithms, and access to large datasets. https://www.inteli-ai.com/ #Lawenforcement #Lawenforcementagency #Investigations #Osint #Intelligence #Intelligenceinvestigations #Cyberintelligence

𝐀𝐈 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: 𝐁𝐢𝐠 𝐑𝐢𝐬𝐤𝐬, 𝐁𝐢𝐠 𝐑𝐞𝐰𝐚𝐫𝐝𝐬 Large language models (LLMs) are powerful AI tools that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. But like any powerful tool, there are challenges to consider. Here's a quick breakdown of two main areas of concern: 𝟏. 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: ⚪ LLMs can sometimes make up information, called hallucinations. This can be dangerous if the information is used for important tasks like medicine or education. ⚪ Hackers can trick LLMs into giving wrong answers or revealing private information through cleverly crafted prompts, adversarial attacks. ⚪ LLMs may not always follow our instructions perfectly, and small changes in how we ask them questions can lead to big differences in their responses, prompt brittleness. 𝟐. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: ⚪ LLMs need a lot of computing power to run, which can be expensive and inconvenient, memory and scalability challenges. ⚪ LLMs are trained on massive datasets of text and code, and this data might contain personal information or biases, security and privacy concerns. 𝐒𝐨, 𝐰𝐡𝐚𝐭 𝐜𝐚𝐧 𝐰𝐞 𝐝𝐨? Researchers are working on ways to make LLMs more reliable and trustworthy. Here are some promising areas: • 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Techniques are being developed to identify when LLMs are making things up and flag those outputs for review. • 𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐃𝐞𝐟𝐞𝐧𝐬𝐞: Researchers are creating methods to train LLMs to recognize and resist being tricked by hackers. • 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: By carefully crafting instructions, we can improve the quality and consistency of LLM outputs. • 𝐏𝐫𝐢𝐯𝐚𝐜𝐲-𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: New algorithms can help train LLMs without revealing the sensitive details in the data they're trained on. LLMs are a powerful new technology, but it's important to be aware of the challenges and ongoing efforts to address them. By working together, we can ensure that LLMs are used safely and ethically for the benefit of everyone. #artificialintelligence #machinelearning #bigdata #nlp #llms #techchallenges #futureofwork #genAI Rahul Maheshwari

【Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework】 Full article: https://lnkd.in/gJR_i4hC (Authored by Alicia Biju, et al., from Georgia Institute of Technology, USA.) Since the introduction of Chat GPT-4 and its widespread adoption, there has been a notable increase in the usage of Large Language Models (#LLMs) and Generative Artificial Intelligence (#GenAI) across various applications. With the scope of the LLMs continuously broadening it brings forth distinct security issues. This study extends the current Common Vulnerability Scoring System (#CVSS) guidelines and provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. #Prompt_Injections #Training_Data_Poisoning 

Note #43 on Generative AI and foundation models: How to ensure the #robustness and #security of #llms against #adversarial attacks? The robustness and security of LLMs against adversarial attacks is crucial in a reliable deployment. But how could we measure this reliability? Here are some measurements: 1. Adversarial training: incorporate adversarial examples into the training process; generating examples that intentionally try to mislead the model and training the model to correctly handle these inputs and try utilize robust optimization techniques 2. Defensive distillation: train a smaller, more robust model to mimic a larger model to smooth the decision boundaries, making it harder for adverserial attacks 3. Regularization techniques: try regularization techniques such as dropout or noise injection during training to improve model's generalization/robustness. 4. Ensemble learning: of multiple models to make predictions rather than jut one 5. Input sanitization: such as normalization, filtering, or encoding as well as anomaly detection before anomalies reach the model 6. Robust architectures: try advanced architectures like capsule networks for more resistant to adversarial attacks. Also utilize built-in defenses, such as using robust loss functions or incorporating defensive layers 7. Monitoring and detection: continuously monitor the inputs and outputs of the model and implement alert systems for anomalous/potentially harmful inputs 8. Differential privacy: use differential privacy methods to ensure that the model does not overly rely on any single data point 9. Robust evaluation: try stress test against known adversarial attack techniques and use robust benchmarks and adversarial attack datasets to evaluate and compare model performance under adversarial conditions 10. Security practices: implement strict access controls to limit who can interact with the model and access its underlying infrastructure .... pic ref: LLM benchmarks [holisticai.com] #machinelearning #deeplearning #neuralnetwork #nlp #generatieveai #llm #llms #datascience #dataanalytics #foundationmodels #genai #ml #cloudcomputing #cloudai #nlp #ai #transformers #foundatinmodels #finetune #finetunellm #rag #ragsystem #robustness #adverserialattack