On intrusion detection matching algorithm from the perspective of multi- agent

Wang, Jinghong; Yang, Dong; Li, Haifeng

doi:10.1109/icmlc.2012.6358991

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The existing IDS models have same features: (1) a database containing few thousand patterns, of different lengths, and (2) the patterns may be seen anywhere in any packet payload. For instance, Snort is an open-source (NIDS), Network-based Intrusion Detection System which has proven utility to listen in packets on a network link, characterize anomalous intruder behavior with a set of patterns, and generate logs and alerts by predefined actions [8].…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

General Study of Mobile Agent Based Intrusion Detection System (IDS)

Jain¹,

Saxena²

2016

JCC

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The extensive access of network interaction has made present networks more responsive to earlier intrusions. In distributed network intrusions, there are many computing nodes that are assisted by intruders. The evidence of intrusions is to be associated from all the held up nodes. From the last few years, mobile agent based technique in intrusion detection system (IDS) has been widely used to detect intrusion over distributed network. This paper presented survey of several existing mobile agent based intrusion detection system and comparative analysis report between them. Furthermore we have focused on each attribute of analysis, for example technique (NIDS, HIDS or Hybrid), behavior layer, detection techniques for analysis, uses of mobile agent and technology used by existing IDS, strength and issues. Their strengths and issues are situational wherever appropriate. We have observed that some of the existing techniques are used in IDS which causes low detection rate, behavior layers like TCP connection for packet capturing which is most important activity in NIDS and response time (technology execution time) with memory consumption by mobile agent as major issues.

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Section: Intrusion Detection Systemmentioning

confidence: 99%