Abstract
In today’s world, a computer is highly exposed to attacks. In here, I try to build a predictive model to identify if the connection coming is an attack or genuine. Machine learning is that part of computer science in which instead of programming a machine we provide the ability to learn. Knowingly or unknowingly machine learning has become a part of our day to day lives. It could be in many ways like predicting stock market or image recognition while uploading a picture in Facebook and so on. Deep learning is a new concept which is trending these days, which moves a step towards the main aim of Machine Learning which is artificial intelligence. This machine learning/artificial intelligence can be used to make intrusion detection in a network more intelligent. We use different machine learning techniques including deep learning to figure out which approach is best for intrusion detection. To do this, we take a network intrusion dataset by Lincoln Labs who created an artificial set up to imitate U.S. Air Force LAN and get the TCP dumps generated. This also includes simulations of various types of attacks. We apply different machine learning algorithms on this data. And choose the machine learning algorithm which is most efficient to build a predictive model for intrusion detection. Now to the same dataset, we will apply Deep Learning mechanisms to build a predictive model with the algorithm that works the best for this data, after comparing the results generated by various deep learning algorithms. We build tool for each of the models (i.e. machine learning and deep learning). Now, the two tools one generated by machine learning and other by deep learning will be compared for accuracy.