The field of cybersecurity is moving towards more sophisticated and adaptive threat detection systems, with a focus on addressing the limitations of traditional security mechanisms. Recent research has highlighted the importance of considering the security risks associated with machine learning-based systems, as well as the need for more comprehensive and refined security properties. The development of novel frameworks and models, such as those utilizing behavioral analytics and deep evidential clustering, has shown promise in detecting insider threats and improving password security. Noteworthy papers include:

• A survey on the security risks of ML-based malware detection systems, which provides a comprehensive analysis of practical security risks and suggests potential future directions.
• A paper on real-time detection of insider threats using behavioral analytics and deep evidential clustering, which achieves an average detection accuracy of 94.7% and a 38% reduction in false positives.