Botnet traffic identification using neural networks

Abstract

Advancement of information and communication techniques have led to share big amount of information which is increasing day by day through online activities and creating new added value over the internet services. At the same time threats to the security of cyber world has been increased with increasing number of heterogeneous connection points having powerful computational capacity. Internet being used to interact and control such automatic network devices connected to it. But hackers/crackers can exploit this network environment by putting malicious dummy node(s) or machine(s) called Botnet(s) to co-ordinate the attacks on security such as Denial of Service (DoS) or Distributed Denial of Service (DDoS). The proposed method attempts to identify those mallicious Botnet traffic from regular traffic using novel deep learning approaches like Artificial Neural Networks (ANN), Gatted Recurrent Units (GRU), Long or Short Term Memory (LSTM) model. The proposed model demonstrates significant improvement of all previous works. The testing dataset, Bot-IoT dataset is the latest and one of the largest public domain dataset used to justify improvement. Testing shows 99.7% classification accuracy which is precise and better than all previous works done. Results analysis and comparison shows the accuracy and supremacy over the latest work done on this field.