Abstract
Several activities take place within a network environment which include (but not restricted to) movement of traffics (packets) among the nodes. An Intrusion Detection system (IDS) which is primarily concerned with the monitoring of an information system with the sole aim of reporting activities which are symptomatic of an attack, needs constant review and upgrade to enhance its operations. In this work, we argue that two of the variants of Membrane computing (MC); spiking neural P (SNP) system and tissue-like P system could best be used as tools to enhance the activities and security properties of any computer network system. Therefore, this paper proposes an alternative but dependable integrated modeling framework which applies membrane computing paradigms to intrusion detection systems. This framework combines the membrane systems model for rule-based intrusion detection systems as well as attack detection model implemented on GPU for high throughput and detection speedup for checkmating packet loss/drop. MC is a newly introduced but yet to be fully explored technology in the area of network/information system security. It is a versatile, non-deterministic and maximally parallel computing model.
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Idowu, R.K., Muniyandi, R.C., Othman, Z.A. (2016). Integrated Membrane Computing Framework for Modeling Intrusion Detection Systems. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_27
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