Project Title: Android Botnet Detection using Supervised Machine Learning Methods

Team Members

Dr. G. Padmavathi, Dean - PSCS, Professor, Department of Computer Science

Dr. P. Subashini, Professor, Department of Computer Science

Ms. A. Roshni, Research Assistant, Centre for Cyber Intelligence, DST - CURIE - AI

Ms. S. Shalini, Master of Computer Application

Project Summary

In today's world, A cyberattack is any offensive manoeuvre that attacks computer information systems or computer networks in the modern world, mobile applications and network architecture. Cyber threats are increasing day by day, botnet is one of the major cyber attack that affect mobile devices. A botnet is a malware-infected network of computers controlled by a single attacker, known as the "bot-herder". A bot is an autonomous computer under the command of the bot-herder. According to the recent statistics report, in Quartely3 Spamhaus Malware Labs discovered 2,656 botnet C&Cs in 2021, up from 1,462 throughout Quartely2 in 2021. This was an increase of 82 percent from the previous quarter. In Quartely3, the monthly average grew from 487 botnet C&Cs per month to 885 botnet C&Cs per month. The number of new botnet C&Cs discovered by Quartely3 has increased by a staggering 82 percent. This project, investigates the detection of Android Botnet using Supervised Machine learning algorithms. Machine learning is an artificial intelligence branch (AI) to provide automatic detection of botnet attacks without any human intervention. The process of Android Botnet Detection using Machine Learning methods consists of six phases. The Phase 1 is the data acquisition. In Phase 2, deals with data pre-processsing to remove unrelevant data. In Phase 3, the appropriate wrapper based feature selection methods are used to select the significant features. In Phase 4, deals with model building using supervised machine learning methods includes Support Vector Machine, Naive Bayes, Decision Tree, Multi- layer Perceptron and Random Forest. In Phase 5, the comparative analysis is made between the supervised machine learning models to suggest the suitable model for Android Botnet detection using top 85 features. While training the model, Random forest detect 99.92% of android botnet in top 85 feature. The Evaluation of the models based on the performance metrics such as accuracy, precision, recall, f1 score in a significant way.