The Red Team Field Guide
Ever-evolving commands and resources for red teaming—covering port scanning, enumeration, exploitation, and privilege escalation.
Port Scanning nmap -sC -sV -oA nmap/init 10.10.10.10 nmap -sC -sV -p- -oA nmap/all 10.10.10.10 # UDP nmap -sU -vv -p- -oA nmap/alludp 10.10.10.10 nmap -sU -p 123,161,162 10.10.10.10 # Ping Sweep nmap -n -sn -oG - 10.10.10.0/24 | grep -i 'up' nmap -n -sn -oG - 10.10.10.0/24 | awk '/Up$/{print $2}' | sort -V | tee ips.
Automating Vulnerability Assessments with Machine Learning
Automating Vulnerability Assessments with Machine Learning In today’s digital landscape, cybersecurity threats are evolving at an unprecedented rate, both in volume and sophistication. Traditional manual assessment methods are increasingly inadequate for keeping up with this dynamic environment. Organizations are turning to artificial intelligence (AI) and machine learning (ML) to automate vulnerability assessments, thereby enhancing their ability to detect, analyze, and mitigate risks promptly and effectively.
Introduction Cybersecurity has become a paramount concern for businesses and governments worldwide.
Optimizing Nuclei for Mass Scanning: Strategies and Configurations
Introduction In the realm of cybersecurity, efficient and scalable vulnerability scanning is paramount. Nuclei, developed by ProjectDiscovery, stands out as a powerful, fast, and customizable vulnerability scanner that uses simple YAML-based templates. It allows security professionals to perform targeted scans across various protocols like HTTP, DNS, TCP, and more.
However, when it comes to mass scanning—running Nuclei against hundreds or thousands of targets—there are challenges related to resource utilization, performance optimization, and accurate results.
The Evolution of Malware: How GANs Could Create Adaptive Cyber Threats
The Evolution of Malware: How GANs Could Create Adaptive Cyber Threats The cybersecurity landscape is continually evolving, with threat actors leveraging advanced technologies to outpace defensive measures. One such technology is Generative Adversarial Networks (GANs), a class of artificial intelligence algorithms that have shown remarkable capabilities in generating realistic data. This article explores how GANs might be applied in the development of new types of malware that can adapt and evolve to avoid detection by traditional security measures.
Building an RL System to Generate Realistic Malware Samples for Defensive Training
Introduction In our previous posts, we explored:
Using Reinforcement Learning (RL) for Malware Evasion: How attackers might use RL to craft malware that adapts to evade detection. Detecting Malware Using Dynamic Memory Heatmaps and Video Classification: Leveraging dynamic analysis and deep learning to detect malicious behavior. Now, we’ll bring these concepts together to advance cybersecurity research. Our goal is to:
Embed the dynamic classifier into a malware gym framework. Train an RL agent to bypass this dynamic classifier using custom actions.