Performance Improvement of Intrusion Detection Systems

Abstract

Intrusion Detection Systems (IDSs) have been crucial in defending intrusive attacks (both active and passive) in various application scenarios in recent trends. Over the years, many research activities have been carried out on intrusion detection systems. The IDSs have been evolved over times with various detection methodologies, approaches, and technology types. The IDSs after several evaluations and different approaches still face a major challenge-performance improvement. This improvement can be quantified in two broad ways- the detection rate and the rate of false positives. The improved performance involves the efficiency and accuracy of detection. The efficiency can be attributed to performance in case of a very high amount of attacks and the accuracy can be attributed to a significantly low amount of false positives. In the same context, we have found that the IoT networks which are in high demand in recent trends also suffer from such types of attacks in operational environments due to limited storage and processing capabilities. In order to protect the IoT application, the scenario necessitates the need of IDS that is lightweight in implementation and provides a significantly higher amount of accuracy which is at par with the IDSs implemented in conventional networks. In this work, we have proposed an improved technique for performance improvement of IDSs in IoT domain.