Distributed intrusion detection scheme for next generation networks

Manan, Jamila; Ahmed, Atiq; Ullah, Ihsan; Merghem-Boulahia, Leïla; Gaïti, Dominique

doi:10.1016/j.jnca.2019.102422

Cited by 21 publications

(10 citation statements)

References 15 publications

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“…In recent years, collaborative architecture remains in the interest of several works [100,154,121,78,202]: for their robustness and scalability, compared to centralized architectures. In a collaborative system, removing the central entity results in peer to peer (P2P) organisation, where no more privileges are accorded to any entity regarding others [157].…”

Section: Distributed (Decentralized)mentioning

confidence: 99%

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Bougueroua

Mazouzi

Belaoued

et al. 2021

Journal of Artificial Intelligence and Soft Computing Research

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Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.

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Section: Distributed (Decentralized)mentioning

confidence: 99%

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Bougueroua

Mazouzi

Belaoued

et al. 2021

Journal of Artificial Intelligence and Soft Computing Research

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“…In the decentralized structure, there are a few studies that used mechanisms for load balancing. In [102], A method to control the CPU usage ratio of the IDS server with both normal traffic and flooding attacks was used. The method works by managing registration requests of the clients to keep low CPU load on the IDS server.…”

Section: ) Load Balancingmentioning

confidence: 99%

“…Centralized/ Decentralized Collaboration 3 [14] Distributed Distributed. Decentralized Coordination and scalability 4 [44] Distributed Decentralized Decentralized Fault-tolerance 5 [45] Distributed Decentralized Decentralized Robustness and fault tolerance 6 [49], [50], [57], [106] Distributed Distributed Decentralized Cooperation 7 [51] Distributed Distributed Decentralized Cooperation, learning and scalability 8 [54] Distributed Decentralized Centralized Self-orgnization 9 [59] Distributed Centralized Centralized Self-healing 10 [61] Distributed Distributed Decentralized Coordination, scalability and load balancing 11 [65] Distributed Distributed Decentralized Adaptation, self healing and cooperation 12 [66] Distributed Decentralized Decentralized Cooperation, coordination, scalability and learning 13 [68] Distributed Decentralized Decentralized Cooperation, learning, and self-adaptation 14 [70] Distributed Decentralized Decentralized Adaptation and intelligence 15 [95], [101] Distributed Decentralized Decentralized Cooperation 16 [79] Distributed Distributed Decentralized Cooperation, extendability,load balancing and fault tolerance 17 [87] Distributed Distributed Centralized Cooperation and reasoning 18 [93] Distributed Decentralized Decentralized Autonomy and cooperation 19 [102] Distributed Distributed Decentralized fault tolerance 20 [107] Distributed Decentralized Decentralized Cooperation, scalability Dynamically adaptive structure (20 studies) 1 [5] Distributed Dynamic. Centralized Adaptation and collaboration 2 [16] Distributed Decentralized Centralized Collaboration, adaptation, reliability 3 [35] Distributed Decentralized centralized Adaptation, coordination and learnung 4 [37] Distributed Centralized Centralized self management 5 [48] Distributed Decentralized Centralized/ Decentralized Scalability, collaborative, learning, and robustness [62] Distributed Decentralized Decentralized Cooperation, adaptation, scalability and learning 13 [67] Distributed Centralized Centralized Adaptation, cooper...…”

Section: A Multi-agent Ids Architectural Properties and Characteristicsmentioning

confidence: 99%

“…53 [89], [90] Autonomous, collaborative, and intelligent 54 [91] Adaptive and collaborative 55 [92] Adaptive, cooperative, and intelligent 56 [93] Autonomous, cooperative, and mobile 57 [94] Adaptive, and intelligent 58 [95] Autonomous and cooperative 59 [98] Collaborative and mobile, 60 [100] Collaborative and intelligent. 61 [101] Autonomous, cooperative and reactive 62 [102] Adaptive 63 [103] Autonomous, cooperative, intelligent, and mobile 64 [104] Cooperative 65 [105] Intelligent, cooperative 66 [106] Intelligent, autonomous, cooperative and collaborative 67 [107] Collaborative…”

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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“…Session Initiation Protocol (SIP) (3) proposed to solve various issues in the IP Multimedia Subsystem and minimize vulnerability to the phase of registration . Attacks called DDoS focused on the protocol system and structure further proposed for identifying the intrusion in a distributed system with its baseline techniques.…”

Section: Introductionmentioning

confidence: 99%

Bee inspired secured protocol for routing in cognitive radio ad hoc networks

Ramkumar¹,

Vadivel²

2020

IJST

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Background: Enhancing security and minimizing the delay is a significant task present in all modern networks. Dynamic emerging problems like node failure, route failure, intrusions, and security attacks lead the network to failure. Hence, there exist needs for development of a protocol that detects the intrusions and find the better route to destination. Objectives: The main objectives of this research is to understand the issues and challenges in routing that rise dynamically in cognitive radio ad hoc network and propose a bio-inspired routing protocol to enhance security and routing efficiency which will result in reduced delay cum energy consumption. Methods: This study proposes Bee Inspired Secured Protocol (BISP) for routing in cognitive radio ad hoc networks that focuses on increasing the security before sending data packets and decreasing the overall delay. An instinctive characteristic of Bees towards searching for food is utilized to design the proposed routing protocol, which selects the better path to the destination. To enrich the security during data transmission, Rivest Shamir Adelman algorithm is applied. The proposed protocol analyzes the security level of the route and neighbor node energy level before sending the data. Findings: NS2.35 simulator used to evaluate the performance of BISP. Simulation results indicate that BISP has better performance than the existing protocol (i.e., WPIP and GRP) in terms of throughput, packet delivery ratio, reduced delay and enhanced security. Novelty : Comprehensive analysis indicates that BISP have superior performance in classifying the intruding nodes, enhancing the security of data getting transmitted, and reducing the delay.

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Distributed intrusion detection scheme for next generation networks

Cited by 21 publications

References 15 publications

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

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

Bee inspired secured protocol for routing in cognitive radio ad hoc networks

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