"AI-Powered Forensic Toolkit for Cyber Investigations"

Just launched my cybersecurity intelligence system - GeoGuardian AI! An advanced platform combining geolocation monitoring with reinforcement learning to detect and prevent cyber threats in real-time. Key features include: ✅ Ethical data governance with PII protection ✅ Geolocation intelligence for anomaly detection ✅ Adaptive AI with Q-Learning reinforcement learning ✅ Real-time Discord alerts for security teams The system processes historical log data to create predictive threat models while maintaining strict privacy standards through SHA-256 hashing and IP truncation.Built with Python, pandas, numpy, and scikit-learn. Now open source! Check it out: https://lnkd.in/gpCfHNiM #Cybersecurity #AI #MachineLearning #Python #DataScience #InfoSec #ReinforcementLearning #Geolocation #OpenSource #ThreatDetection

\*\*Detecting Malicious Traffic with AI: A Powerful Approach t Detecting Malicious Traffic with AI: A Powerful Approach to Network Security In today's digital landscape, network security is a top priority for organizations and individuals alike. With the rise of cyber threats, detecting malicious traffic has become a daunting task. However, with the advent of Artificial Intelligence \(AI\) and Machine Learning \(ML\), we can empower our network security systems to detect and prevent malicious activities more effectively. K-Nearest Neighbors \(KNN\) for Malicious Traffic Detection Here's a Python code snippet that utilizes a K-Nearest Neighbors \(KNN\) machine learning model to classify network traffic as malicious or legitimate based on packet features: python from sklearn.neighbors import KNeighborsClassifier from sklearn.model\_selection import train\_test\_split import pandas as pd from scapy.all import sniff, TCP, IP, Raw # Capture network traffic using Scapy packets = sniff\(count=1000\) # Extract packet features \(e.g., source IP, desti... --- \*This https://lnkd.in/ge8KmRM9

**Detecting Malicious Traffic with AI: A Deep Dive into KNN-based Network Security** In today's increasingly connected world, network security has become a top priority. As cyber threats continue to evolve, traditional security measures may not be enough to detect malicious traffic. That's where artificial intelligence (AI) and machine learning (ML) come in – to help security systems stay one step ahead of hackers. Here's a code snippet in Python that utilizes a machine learning model (K-Nearest Neighbors, or KNN) to classify network traffic as malicious or legitimate based on packet features: ```python from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # Load dataset import pandas as pd df = pd.read_csv('network_traffic.csv') # Split data into features (X) and labels (y) X = df.drop(['label'], axis=1) y = df['label'] # Scale features using StandardScaler scaler = StandardScal...

Researchers have uncovered significant flaws in AI systems that could pose serious threats. These bugs, if exploited, could lead to AI systems being manipulated without detection. The complexity of AI often masks these vulnerabilities, making the issue even more pressing. It's crucial for developers to address these challenges to ensure our technology remains secure. By understanding these bugs and improving our AI frameworks, we can safeguard vital systems from potential harm. Dive deeper into the details of this research to better understand the intricacies of these risks. #AIVulnerabilities #Cybersecurity #TechSafety

OpenAI introduces Aardvark, an agent that finds and fixes security bugs using GPT-5 AARDVARK is an AI agent that scans codebases, finds security issues, and automatically creates patches. • It starts by scanning a Git repository to detect vulnerabilities. • These are analyzed through a threat model to understand the risks. • In a validation sandbox, AARDVARK tests if the issues are real. • Using Codex, it then writes and tests a patch to fix them. • A human reviewer checks the fix, and a pull request is opened in Git. Each cycle helps AARDVARK learn and get better at securing code automatically. https://lnkd.in/dGE9Rkc2

🛡 OpenAI launches Aardvark, an AI agent that hunts code vulnerabilities OpenAI has introduced Aardvark, a new AI-powered security agent that scans, tests, and flags vulnerabilities in codebases, working like a tireless digital security analyst. 🔸 Aardvark connects directly to GitHub, continuously reviewing code for potential security flaws and weak spots. 🔸 Using advanced reasoning from GPT-5, it identifies risks, explains their severity, and suggests specific fixes, all without changing code automatically. 🔸 In early trials, the agent uncovered previously unknown bugs in open-source projects that later received official CVE classifications. 🔸 Currently in invite-only beta, Aardvark will expand access after further testing and developer feedback. By automating vulnerability detection, OpenAI aims to speed up the security review process, turning code audits from a manual bottleneck into a proactive, AI-driven defense layer.

🛡 OpenAI launches Aardvark, an AI agent that hunts code vulnerabilities OpenAI has introduced Aardvark, a new AI-powered security agent that scans, tests, and flags vulnerabilities in codebases, working like a tireless digital security analyst. 🔸 Aardvark connects directly to GitHub, continuously reviewing code for potential security flaws and weak spots. 🔸 Using advanced reasoning from GPT-5, it identifies risks, explains their severity, and suggests specific fixes, all without changing code automatically. 🔸 In early trials, the agent uncovered previously unknown bugs in open-source projects that later received official CVE classifications. 🔸 Currently in invite-only beta, Aardvark will expand access after further testing and developer feedback. By automating vulnerability detection, OpenAI aims to speed up the security review process, turning code audits from a manual bottleneck into a proactive, AI-driven defense layer.

🛡 OpenAI launches Aardvark, an AI agent that hunts code vulnerabilities OpenAI has introduced Aardvark, a new AI-powered security agent that scans, tests, and flags vulnerabilities in codebases, working like a tireless digital security analyst. 🔸 Aardvark connects directly to GitHub, continuously reviewing code for potential security flaws and weak spots. 🔸 Using advanced reasoning from GPT-5, it identifies risks, explains their severity, and suggests specific fixes, all without changing code automatically. 🔸 In early trials, the agent uncovered previously unknown bugs in open-source projects that later received official CVE classifications. 🔸 Currently in invite-only beta, Aardvark will expand access after further testing and developer feedback. By automating vulnerability detection, OpenAI aims to speed up the security review process, turning code audits from a manual bottleneck into a proactive, AI-driven defense layer.

Interesting release from OpenAI: Aardvark, an autonomous security researcher powered by GPT-5, designed to enhance software security by detecting and addressing vulnerabilities at scale. Aardvark aims to mimic how human security researchers work, leveraging LLM-powered reasoning to analyze codebases, confirm vulnerabilities in sandboxed environments, and recommend fixes. Here’s how it works: 🔹 𝐓𝐡𝐫𝐞𝐚𝐭 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠: It starts by scanning the entire repository to create a security threat model. 🔹 𝐂𝐨𝐦𝐦𝐢𝐭 𝐒𝐜𝐚𝐧𝐧𝐢𝐧𝐠: Inspects commit-level changes against the repository and threat model to identify vulnerabilities. 🔹 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧: Tries to trigger any potential vulnerabilities in an isolated sandbox environment to confirm exploitability. 🔹 𝐏𝐚𝐭𝐜𝐡𝐢𝐧𝐠: Suggests fixes via OpenAI Codex, enabling human-approved, one-click patching. OpenAI reports that 𝐀𝐚𝐫𝐝𝐯𝐚𝐫𝐤 𝐜𝐚𝐮𝐠𝐡𝐭 𝟗𝟐% 𝐨𝐟 𝐤𝐧𝐨𝐰𝐧 𝐚𝐧𝐝 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐝 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 in their benchmarks. While impressive on paper, it’s worth noting that they haven’t shared much detail about which repositories were scanned or the kinds of issues were missed. Definitely curious to see how it’ll hold up in practice and how much it can actually move the needle for security teams.

This will be a great way to augment traditional threat modeling and cover any gaps they could have been missed in the research and interview phase.