Integrated Membrane Computing Framework for Modeling Intrusion Detection Systems
| Autor: | Zulaiha Ali Othman, Rufai Kazeem Idowu, Ravie Chandren Muniyandi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Speedup
Network packet Computer science Distributed computing Throughput 0102 computer and information sciences 02 engineering and technology Intrusion detection system 01 natural sciences Upgrade 010201 computation theory & mathematics Packet loss 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing Membrane computing |
| Zdroj: | Bio-inspired Computing – Theories and Applications ISBN: 9789811036101 BIC-TA (1) |
| DOI: | 10.1007/978-981-10-3611-8_27 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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