A Cooperative Network Intrusion detection Based on Fuzzy SVMs

Teng, Shaohua; Du, Hongle; Wu, Naiqi; Zhang, Wei; Su, Jiangyu

doi:10.4304/jnw.5.4.475-483

Cited by 18 publications

(4 citation statements)

References 5 publications

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“…Several defense techniques may be applied in industrial networks. IDS are recognized as a strong line of defense mainly in situations with no other efficient cybersecurity mechanisms [19]. One evidence of this is the volume of studies related to these subjects.…”

Section: Shamoon Notpetyamentioning

confidence: 99%

“…This situation is even worse in industrial networks based on serial protocols because of their limitations. So, if there is always a vulnerability that can be exploited, a system that performs analytical tests against anomalous patterns could be a second line of defense [19]. IDS often implement this type of protection in ICS [11], which is indicated to be present in any industrial networks [8], [13], [15].…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

“…Statistical Routable [19] Multi-Feature Data Clustering Data's weighted distances and coefficients are classified based on the priority threshold of data attribute feature for each node in the network. Based on the classification, the cluster can be marked as normal or abnormal.…”

Section: Main Ai Techniques Applied In Ids For Industrial Networkmentioning

confidence: 99%

“…As the serial protocols that support these networks implement almost no cybersecurity defenses [4], [17], in a successful penetration, an attacker can easily take control and manipulate sensors and actuators directly, endangering assets and human lives [18]. Thus, IDS may be applied as an additional or, in some cases, the only line of defense [19] in this critical layer.…”

Section: Introductionmentioning

confidence: 99%

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Cybersecurity in Industrial Networks: Artificial Intelligence Techniques Applied to Intrusion Detection Systems

Luis de Moura,

Franqueira,

Pessin

2023

2023 Congress in Computer Science, Computer Engineering, &Amp;amp; Applied Computing (CSCE)

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Industrial control systems (ICS) operate on serialbased networks which lack proper security safeguards by design. They are also becoming more integrated to corporate networks, creating new vulnerabilities which expose ICS networks to increasing levels of risk with potentially significant impact. Despite those risks, only a few mechanisms have been suggested and are available in practice as cybersecurity safeguards for the ICS network layer, maybe because they might not be commercially viable. Intrusion detection systems (IDS) are typically deployed in the corporate networks to protect against attacks since they are based on TCP/IP. However, IDS are not used in serialbased ICS networks yet. This study examines and compares modern Artificial Intelligence (AI) techniques applied in IDS that are potentially useful for serial-based ICS networks. The results showed that current AI-based IDS methods are viable in such networks. A mix of AI techniques would be the best way forward to detect known attacks via rules and novel attacks, not previously mapped, via supervised and unsupervised techniques. Despite these strategies' limited use in serial-based networks, their adoption could significantly strengthen cybersecurity of ICS networks.

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Section: Shamoon Notpetyamentioning

confidence: 99%