Intelligent Intrusion Detection System Using Deep Learning Technique

There is constant growth in the digitization of information across the world. However, this rapid growth has raised concerns over the security of the information. Today’s internet is made up of nearly half a million different networks. Network intrusions are very common these days which put user information at high risk. An intrusion detection system (IDS) is a software/system to analyze and monitor the data for the detection of intrusions in the host/network. An intrusion Detection System competent in detecting zero-day attacks and network anomalies is highly demanded. Researchers have used different methods to develop robust IDS. However, none of the methods is exceptionally well and meets every requirement of IDS. Machine learning/Deep learning (ML/DL) are among the widely used methods to develop IDS. This proposed technique uses a DL model, Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU) framework. There are several datasets to evaluate the performance of the learning techniques. CICIDS 2017 is the dataset that contains a variety of cyber-attacks. The proposed technique will use the same to evaluate the deep learning technique. Moreover, the study will also showcase the comparison between the results of existing machine learning algorithms and the proposed algorithm. The comparison would be done on different matrices such as True positive (TP) and False positive (FP) rates, accuracy, precision, etc.