LLM agents, specifically GPT-4, can autonomously exploit real-world one-day vulnerabilities in various systems, including websites, container management software, and vulnerable Python packages, with an 87% success rate. This capability far exceeds that of other LLMs and open-source vulnerability scanners.
Illicit activities on the dark web, especially those involving cryptocurrencies, have strong correlations and can be grouped into campaigns that span multiple onion sites and blockchain addresses.
This research proposes an AI-powered cyber incident response system that leverages network traffic classification, web intrusion detection, and malware analysis to enhance cybersecurity in cloud environments.
Awareness of well-known cybersecurity threats and solutions is quite low among cyber and information security decision-makers, and is positively associated with adoption of advanced antimalware solutions and security operation centers.
A code-aware data generation technique is introduced to efficiently detect emerging malware in embedded systems, even with limited exposure to malware samples.
A critical vulnerability in the SSH dependency, xz utils, allows hackers to potentially take over servers, causing widespread concern among security experts.
The content covers a range of critical cybersecurity issues, including a $10 million bounty for the BlackCat ransomware gang, sophisticated malware targeting macOS and Android users, multiple vulnerabilities in Linux systems, and the resurgence of the TheMoon botnet exploiting outdated devices.
An incremental hybrid adaptive network-based intrusion detection system that can detect known and unknown stealthy attacks in Software Defined Networks by adapting to changes in attacker behavior (concept drift).
Innovative approach for early detection of suspicious domain registrations using a combination of NLP and MLP models.
Statistische Merkmale als robuste Backdoor-Triggers nutzen.