threat detection

In the digital age, the significant increase in information generation and processing is accompanied by a growing threat of unauthorized access, illegal distribution, and use. One of the most promising strategies for protecting information from various cyber threats and malicious attacks is the use of Natural Language Processing (NLP) processors. This article focuses on the methodology of data protection in the context of utilizing Natural Language Processing for sentiment analysis and trend detection.

The growing popularity of containerization in cloud environments is accompanied by an increasing number of attacks that leverage obfuscated malware designed to evade detection by static scanners. This paper presents an experimental comparison of two container security tools – Trivy (static analysis) and Tracee (dynamic observation based on eBPF) – in detecting malicious executables hidden in non-standard paths such as /tmp/.