Smart Approach for Botnet Detection Based on Network Traffic Analysis

Abstract

Today, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. By categorizing behavior based on time slots, we investigate the viability of detecting botnet behavior without seeing a whole network data flow. We also evaluate the efficacy of two well-known classification methods with reference to this data. We demonstrate experimentally, using existing datasets, that it is possible to detect botnet activities with high precision.