A novel Multi-Threaded K-Means clustering approach for intrusion detection

Due to the proliferation of high-speed Internet access, more and more organizations are becoming vulnerable to potential cyber-attacks. An intrusion is defined as any set of actions that compromise the integrity, confidentiality or availability of a resource. Intrusion Detection System (IDS), as the main security defending technique, is widely used against malicious attacks. IDS system should be good enough to detect existing attacks as well as novel attacks at high speed. Thus to fulfil these requirements a new novel Multi-Threaded K-Means clustering approach has been used which has resulted in high detection rate and low false alarm rate. A subset of KDD99 Data set has been used as an input dataset for experiments.