International Journal of Science, Engineering and TechnologyAuthors: Shivang, Mohd Kaif, Nitin Saini Rathor, Parikshit Saini, Abdul Aahad, Siddhant Sharma, Mr. Vikalpa Tyagi
Abstract: A Brute force attack is one of the most common cyber threats in which attackers repeatedly try different username and password combinations to gain unauthorized access to systems, networks, or applications. Traditional security systems often fail to provide transparent and real-time detection of such attacks, leading to data breaches and system compromise. This project proposes a Transparent Brute Force Attack Detection Model that identifies suspicious login attempts and malicious authentication activities efficiently and accurately. The proposed model continuously monitors user login behavior, IP addresses, failed login attempts, access frequency, and session patterns to detect abnormal activities. Machine learning and rule-based analysis techniques are used to differentiate between legitimate users and attackers. When the number of failed attempts exceeds a predefined threshold, the system automatically generates alerts, blocks suspicious IP addresses temporarily, and records attack logs for further analysis.
