Securing Keystroke Data: An ML-Based Approach to Keylogger Defense
P. Sowjanya, Sai Yashwanth Vakada, Seela Bhavana, Ch. Venkatesh, Valentina Pavan · International Journal for Research in Applied Science and Engineering Technology · 2025
Abstract: Keyloggers pose a significant threat to cybersecurity by covertly capturing every keystroke a user makes, which can lead to identity theft, unauthorized access, and data breaches. This paper introduces a thorough, multilayered strategy to combat keylogging attacks through detection, mitigation, and obfuscation. Our system utilizes a machine learning-based Random Forest Classifier to precisely detect suspicious keylogging activities. Upon detection, the system promptly isolates and terminates the keylogger process to prevent further compromise of data. Furthermore, the obfuscation module ensures that even if a keylogger captures keystrokes, the data are scrambled and rendered useless. By employing real-time behavioral monitoring and intelligent countermeasures, this solution enhances detection accuracy, accelerates response times, and fortifies defenses against evolving keylogger techniques. The experimental results validated the effectiveness of the system in protecting sensitive user information from keylogging threats.