Signature-based Denial of Service and Probe Detection, a Machine Learning approach
Computer Networks and the internet are increasingly becoming the backbone of our social fabric. However, because of the diverse characteristics of these networks they are prone to various attacks and as a result the computer networks need to be highly secured to ensure confidentiality, integrity and availability of information. Presently, a key strategy in subduing these attacks is by use of Intrusion Detection (ID). Intrusion Detection Systems (IDSs) are used to detect attacks on a network. However, the uniqueness and frequency of these attacks calls for novel approaches such as the use of machine learning techniques to model the network traffic as it changes and detect anomalous traffic. In this paper we present some work on the detection of these Denial Of Service(DOS) and Probe attacks in network traffic using machine learning and data mining techniques. We build our models based on the common KDD dataset as well as live data from a wireless network at an institution of learning that has numerous and diverse users. We show the efficacy of machine learning algorithms for detecting these two attacks.