Enhanced Keylogger Classification Using Deep Learning: A Systematic Review.

Abstract:

This systematic review explores methodologies for detecting and mitigating keyloggers, pervasive cybersecurity threats that surreptitiously capture keystrokes. After conducting thorough database searches, 26 relevant studies were found, showing a wide range of methods including machine learning algorithms, heuristic techniques, and behavior-based strategies. The review underscores the efficacy of combining proactive and reactive measures in countering keylogger threats, with machine learning algorithms exhibiting varying degrees of success. Significantly, creating interfaces that are easy for users to use is found to be a crucial element in improving user knowledge and making it easier to take quick action. However, the analysis also points out some drawbacks, such as the lack of extended verification for suggested approaches and variations in how algorithms are designed in different research. These findings underscore the imperative for ongoing innovation and collaboration among practitioners and policymakers to develop standardized protocols and address emerging threats comprehensively. Overall, this review offers valuable information on how to detect and prevent keyloggers, which can help direct future research in this important area.