·4 min read
Detection of Brute-Force Login Attempts Using Machine Learning
Notes on our IJICT published research applying machine learning models to detect automated login attacks.
Machine LearningSecurityResearch
Background
Credential stuffing and brute-force login attempts remain a persistent threat to web applications and networked systems. Traditional threshold-based lockouts often miss distributed or low-rate attacks.
Machine Learning Approach
In our paper published in IJICT (Vol. 9, Issue 1, April 2026), we explored supervised machine learning models to classify authentication traffic and distinguish legitimate user logins from automated brute-force attempts.
- Paper Title: Detection of Brute-Force Login Attempts Using Machine Learning
- DOI:
10.31987/ijict.9.1.371 - Read Full Paper: IJICT Archive