The Prospects of Using Spiking Neural P System for Intrusion Detection

Rufai Kazeem Idowu, Ravie Chandren Muniyandi, Zulaiha Ali Othman

Abstract


Spiking Neural P (SN P) System is one of the variants of Membrane computing. SN P system is a parallel computing model which derives its motivation from the biological living cells. On the other hand, „Intrusion‟ issue has become a major concern not only to the cyber security experts but also to all the users of the internet. Therefore, to totally eradicate this menace or putting it in a state of abeyance, several approaches like the use of Expert system, Intelligent algorithms, Artificial Neural Networks, Statistical methods and a host of others had been deployed. However, there is still room for improvement. SN P system being a maximally parallel biological model, has proved to be a versatile tool. This paper therefore attempts to evoke a new direction in the application of SN P to intrusion detection. Specifically, it answers the following questions among others: What are the principles of intrusion detection? What are the approaches being used and the challenges impeding the realization of an efficient Intrusion Detection System (IDS)? What is an SN P system? Does SN P syetem have the potentials to enhance the performance of IDS? In all, the paper points to a new direction for using SN P systems in detecting known and unknown attacks in Intrusion detection systems thereby providing the baseline for future works.

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