Intrusion Detection in Computer Networks using Lazy Learning Algorithm

Abstract

Intrusion Detection Systems (IDS) are used in computer networks to safeguard the integrity and confidentiality of sensitive data. In recent years, network traffic has become sizeable enough to be considered under the big data domain. Current machine learning based techniques used in IDS are largely defined on eager learning paradigms which lose performance efficiency by trying to generalize training data before receiving queries thereby incurring overheads for trivial computations. This paper, proposes the use of lazy learning methodologies to improve overall performance of IDS. A novel heuristic weight based indexing technique has been used to overcome the drawback of high search complexity inherent in lazy learning. IBk and LWL, two popular lazy learning algorithms have been compared and applied on the NSL-KDD dataset for simulating a real-world like scenario and comparing their relative performances with hw-IBk. The results of this paper clearly indicate lazy algorithms as a viable solution for real-world network intrusion detection.