OMAMIDS: Ontology Based Multi-Agent Model Intrusion Detection System for Detecting Web Service Attacks

Anusha, K.; Sathiyamoorthy, E.

doi:10.1080/19361610.2016.1211847

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…First, some architectures adopted shared knowledgebase or, in some cases, shared ontologies. A shared knowledge base or ontology represents a central hub for all agents to exchange their desires and beliefs; this scheme adopted by [4], [9], [15], [58], [59]. The problem of this scheme is that the multi-agent IDS architectures become susceptible to the risk of central point of failure, central the hub may face errors and crashes.…”

Section: ) Knowledge Sharingmentioning

confidence: 99%

“…In [15], a self-learning ontology was proposed using Intuitionistic Fuzzy Logic (IFL) to generate new attack rules. The problem in this mechanism is the need for labeled data, which will not be available in the case of the swift evolution of cyberattacks.…”

Section: ) Adaptation and Learningmentioning

confidence: 99%

“…Centralized. Adaptation, coordination and mobility 3 [7] Distributed Centralized/ Distributed Centralized/ Distributed Cooperation, scalability, adaptation and robustness 4 [9], [11], [72], [94] Distributed Distributed Centralized Scalability, load balancing, fault tolerance, and reasoning 5 [15] Distributed Centralized Centralized Self-learning and adaptation 6 [17], [18] Distributed Decentralized Centralized Distribution, Cooperation, adaptation and learning 7 [19] Distributed Centralized Centralized Lightweight, adaptation, dynamics 8 [20] Distributed Centralized.…”

Section: A Multi-agent Ids Architectural Properties and Characteristicsmentioning

confidence: 99%

“…17 [91] Distributed Distributed Decentralized Adaptation, collaboration and expansibility 18 [92] Distributed Decentralized Centralized Collaboration and adaptation 19 [98] Distributed Dynamic. Distributed Collaboration 20 [105] Distributed Decentralized Centralized Distribution, cooperation, intelligence [4] Adaptive autonomous, cooperative, and intelligent 2 [5] Autonomous, cooperative, and collaborative 3 [6] Autonomous, intelligent, and mobile 4 [7] Adaptive, autonomous, and intelligent 5 [8] Autonomous,cooperative, intelligent, and mobile 6 [9], [10], [72], [94] Adaptive, cooperative, and Intelligent 7 [11] Adaptive and intelligent 8 [14] Cooperative and intelligent 9 [35] Adaptive and intelligent 10 [15] Intelligent 11 [16]- [18] Autonomous,adaptive, collaborative, cooperative, and intelligent 12 [19] Cooperative, intelligent, and mobile 13 [20] Intelligent and interactive 14 [37] Autonomous and collaborative 15 [38] Cooperative and intelligent 16 [42] Intelligent, 17 [43] Adaptive,deliberative, and intelligent 18 [44] Autonomous and cooperative 19 [45] Autonomous,adaptive,cooperative, deliberative,intelligent, and reflexive 20 [46] Autonomous, cooperative, and intelligent 21 [47] Autonomous, cooperative, and intelligent 22 [48] Collaborative and intelligent 24 [49] Autonomous, competitive, cooperative, and reactive 25 [50] Autonomous, cooperative, collaborative and intelligent 26 [51] Autonomous and cooperative 27 [52] Adaptive, autonomous, cooperative, intelligent, and mobile 28 [53] adaptive, autonomous, cooperative, and collaborative, 29…”

Section: A Multi-agent Ids Architectural Properties and Characteristicsmentioning

confidence: 99%

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A Systematic State-of-the-Art Analysis of Multi-Agent Intrusion Detection

et al. 2020

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Multi-agent architectures have been successful in attaining considerable attention among computer security researchers. This is so, because of their demonstrated capabilities such as autonomy, embedded intelligence, learning and self-growing knowledge-base, high scalability, fault tolerance, and automatic parallelism. These characteristics have made this technology a de facto standard for developing ambient security systems to meet the open and dynamic nature of today's online communities. Although multi-agent architectures are increasingly studied in the area of computer security, there is still not enough empirical evidence on their performance in intrusions and attacks detection. The aim of this paper is to report the systematic literature review conducted in the context of specific research questions, to investigate multi-agent IDS architectures to highlight the issues that affect their performance in terms of detection accuracy and response time. We used pertinent keywords and terms to search and retrieve the most recent research studies, on multi-agent IDS architectures, from the major research databases and digital libraries such as SCOPUS, Springer, and IEEE Explore. The search processes resulted in a number of studies; among them, there were journal articles, book chapters, conference papers, dissertations, and theses. The obtained studies were assessed and filtered out, and finally, there were over 71 studies chosen to answer the research questions. The results of this study have shown that multi-agent architectures include several advantages that can help in the development of ambient IDS. However, it has been found that there are several issues in the current multi-agent IDS architectures that may degrade the accuracy and response time of intrusions and attacks detection. Based on our findings, the issues of multi-agent IDS architectures include limitations in the techniques, mechanisms, and schemes used for multi-agent IDS adaptation and learning, load balancing, scalability, fault-tolerance, and high communication overhead. It has also been found that new measurement metrics are required for evaluating multi-agent IDS architectures.

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Section: ) Knowledge Sharingmentioning

confidence: 99%

Section: ) Adaptation and Learningmentioning

confidence: 99%