A Multi-agents Intrusion Detection System Using Ontology and Clustering Techniques

Brahmi, Imen; Brahmi, Hanen; Yahia, Sadok Ben

doi:10.1007/978-3-319-19578-0_31

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…IDS is typically capable to identify the diverse cyber threats and attacks, even the unknown zero-day attack, and able to respond in real-time based on the user's requirements. IDS gathers data from different sources in a computer network or device for this purpose and identifies security policy breaches that can be used to detect internal and external attacks [52,53]. IDS can be several types based on environment type and detection approaches.…”

Section: Cybersecurity Defense Strategiesmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or Industry 4.0), which can be used for the protection of Internet-connected systems from cyber threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving machine learning and deep learning methods, the concept of natural language processing, knowledge representation and reasoning, as well as the concept of knowledge or rule-based expert systems modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an intelligent computing or AI-based technical point of view.

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Section: Cybersecurity Defense Strategiesmentioning

confidence: 99%

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Furhad²,

2021

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“…The traditional well-known security solutions such as firewalls, user authentication, and access control, and data encryption and cryptography systems however might not be effective according to today's need in the cyber industry [16] [17] [18] [19]. On the other hand, IDS resolves the issues by analyzing security data from several key points in a computer network or system [39] [40]. Moreover, intrusion detection systems can be used to detect both internal and external attacks.…”

Section: Cybersecurity Defense Strategiesmentioning

confidence: 99%

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Sarker¹

2020

Preprint

111

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In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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“…Thus, the ontology is designed to allow sharing and reuse of knowledge between entities of the same field [27]. RDF-S (Resource Description Framework-Schema) provide a visualization service for ontologies and knowledge representation of any object [24]. The RDF is a model and defined as a set, called Resources, Literals, Properties (subset of Resources), and Statements, where every element is a triplet of the form: subject, predicate, object [27,122].…”

Section: B Ontologymentioning

confidence: 99%

“…Predicate is a member of Properties, subject is a member of Resources, and object is also a member of Resources or Literals [27]. Few approaches and works in the field of intrusion detection are dedicated to the integration of the ontological model with the IDS [24]. In order to improve the accuracy and efficiency of detection and to make intelligent reasoning, the approaches are adapted to the trend of collaborative IDS mixed with an ontological structure [24,145].…”

Section: B Ontologymentioning

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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A Multi-agents Intrusion Detection System Using Ontology and Clustering Techniques

Cited by 11 publications

References 17 publications

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Cybersecurity Data Science: An Overview from Machine Learning Perspective

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

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