Behavioral Biometrics and Machine Learning: Enhancing User Authentication in Cybersecurity
Keywords:
Behavioral biometrics, machine learningAbstract
The increasing sophistication of cyber threats necessitates more robust user authentication methods to safeguard sensitive information. Traditional authentication methods, such as passwords and static biometric systems, are often inadequate against advanced attacks. This paper explores the integration of behavioral biometrics with machine learning models to enhance user authentication processes in cybersecurity systems. Behavioral biometrics analyzes patterns in user behavior, such as keystroke dynamics, mouse movements, and navigation habits, to establish a unique user profile. Coupled with machine learning algorithms, these behavioral features can be continuously monitored for real-time anomaly detection. The paper discusses various behavioral biometrics techniques, the role of machine learning in refining these techniques, and their potential to enhance security through continuous authentication. Challenges related to data privacy, model training, and deployment in real-world scenarios are also addressed. The findings suggest that the integration of behavioral biometrics and machine learning significantly improves user authentication, offering a promising avenue for enhancing cybersecurity measures.
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