Intrusion detection using deep belief networks

With the advent of digital technology, security threats for computer networks have increased dramatically over the last decade being much bolder and brazen. There is a great need for an effective Intrusion Detection System (IDS) which are intelligent specialized system designed to interpret the intrusion attempts in incoming network traffic. Deep belief neural (DBN) networks proved to be the most influential deep neural nets and generative neural networks that stack Restricted Boltzmann Machines. In this paper, we explore the capabilities of DBN´s performing intrusion detection through series of experiments after training it with NSL-KDD dataset. The trained DBN network now identifies any kind of unknown attack in dataset supplied to it and to the best of our knowledge this is first comprehensive paper performing intrusion detection using deep belief nets. The proposed system not only detect attacks but also classify them in five groups with the accuracy of identifying and classifying network activity based on limited, incomplete, and nonlinear data sources. The proposed system achieved detection accuracy about 97.5% for only fifty iterations that is state of art performance compare to the existing system till today for intrusion detection.