Identification and Prevention for Brute Force Attack

Abstract

In today's digital age, information security is of paramount importance, and protecting sensitive data from unauthorized access is crucial. Brute force attacks pose a significant danger to system and network security because they use weak passwords and authentication measures to gain unauthorized access. The goal of this research is to look at the detection and prevention of brute force attacks. This paper provides an in-depth analysis of various methods employed in identifying brute force attacks. It examines both network-based and host-based approaches, highlighting their strengths and limitations. Network-based approaches involve monitoring network traffic patterns and detecting abnormal activities, while host-based approaches focus on analyzing system logs and detecting repeated login attempts. Additionally, the study investigates the utilization of machine learning algorithms for improved identification accuracy, considering factors such as user behavior analysis and anomaly detection. Furthermore, this research explores preventive measures to mitigate the risk of brute force attacks. It discusses the importance of strong password policies and multi-factor authentication as fundamental safeguards against such attacks. In addition, the study underlines the importance of account lockout procedures and intrusion detection systems in deterring brute force attacks. The paper also assesses the effectiveness of CAPTCHA methods and rate limiting approaches in preventing automated brute force attacks. To validate the effectiveness of the identification and prevention techniques, the study conducts experiments using real-world datasets and evaluates the performance metrics such as detection accuracy, false positive rates, and computational overhead. The results demonstrate the efficacy of the proposed approaches in detecting and preventing brute force attacks.