Novel Approach Using Deep Learning for Intrusion Detection and Classification of the Network Traffic

A variety of challenges are being faced nowadays of network intrusion which is continually increasing. These are due to vulnerabilities in software, hardware, and network protocols. Therefore, stronger IDS is required; ML and DM have further strengthened the IDS technology. At the same time threat has also become more sophisticated. Now overfitting and structured optimization techniques are used in IDS. In this paper, we proposed a deep neural network-based IDS. The DL based system monitors the traffic coming from authentic and non-authentic sources. It classifies and segregates malicious traffic with accuracy up to 99.78used for experimentation and comparative analysis with previous techniques shows encouraging results.