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, nondeterministic and maximally parallel computing model.
| Original language | English |
|---|---|
| Title of host publication | Bio-inspired Computing – Theories and Applications - 11th International Conference, BIC-TA 2016, Revised Selected Papers |
| Publisher | Springer Verlag |
| Pages | 336-346 |
| Number of pages | 11 |
| Volume | 681 |
| ISBN (Print) | 9789811036101 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016 - Xian, China Duration: 28 Oct 2016 → 30 Oct 2016 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 681 |
| ISSN (Print) | 18650929 |
Other
| Other | 11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016 |
|---|---|
| Country | China |
| City | Xian |
| Period | 28/10/16 → 30/10/16 |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Integrated membrane computing framework for modeling intrusion detection systems. / Idowu, Rufai Kazeem; Muniyandi, Ravie Chandren; Ali Othman, Zulaiha.
Bio-inspired Computing – Theories and Applications - 11th International Conference, BIC-TA 2016, Revised Selected Papers. Vol. 681 Springer Verlag, 2016. p. 336-346 (Communications in Computer and Information Science; Vol. 681).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Integrated membrane computing framework for modeling intrusion detection systems
AU - Idowu, Rufai Kazeem
AU - Muniyandi, Ravie Chandren
AU - Ali Othman, Zulaiha
PY - 2016
Y1 - 2016
N2 - 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, nondeterministic and maximally parallel computing model.
AB - 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, nondeterministic and maximally parallel computing model.
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UR - http://www.scopus.com/inward/citedby.url?scp=85010030230&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-3611-8_27
DO - 10.1007/978-981-10-3611-8_27
M3 - Conference contribution
SN - 9789811036101
VL - 681
T3 - Communications in Computer and Information Science
SP - 336
EP - 346
BT - Bio-inspired Computing – Theories and Applications - 11th International Conference, BIC-TA 2016, Revised Selected Papers
PB - Springer Verlag
ER -