MOBILE BOTNET DETECTION: A Machine Learning Approach using SVM

Mobile botnets have become a growing cybersecurity concern, leveraging infected Android devices for malicious activities such as DDoS attacks, phishing, and credential theft. Detecting these threats requires advanced techniques due to their evasive nature. This paper proposes a machine learning-based detection system utilizing Support Vector Machine (SVM) to classify applications as benign or malicious. The model extracts and analyses 342 static features from Android applications to identify anomalous behaviour. Evaluated in a real-world setting, the approach demonstrates high detection accuracy with minimal false positives. The findings suggest that machine learning, particularly SVM, is an effective tool for mobile botnet detection. Future enhancements include expanding datasets and incorporating deep learning to improve detection performance and adaptability.