A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems

Patel, Ahmed; Alhussian, Hitham; Pedersen, Jens Myrup; Bounabat, Bouchaïb; Júnior, Joaquim Celestino; Katsikas, Sokratis K.

doi:10.1016/j.cose.2016.07.002

Cited by 62 publications

(32 citation statements)

References 15 publications

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“…Collaborative security has been applied in multiple security domains, including intrusion detection, antispam, antimalware, identification of insider attackers, and detection of botnets . Although the term regards both attack detection and response, the studies conducted and solutions developed have focused on the incident monitoring and analysis part . Similarly, the majority of developed applications have concentrated on the technical dimension, while on the higher level, the security collaboration can be established between people and organizations.…”

Section: Collaborative Security Information Sharing Situation Awarementioning

confidence: 99%

“…As a promising direction in detecting modern cyberattacks, collaborative intrusion detection system (CIDS) has been studied intensively already for more than a decade . Recent proposals include a privacy‐preserving machine‐learning–based CIDS for vehicular ad hoc networks, a CIDS designed specifically to protect the smart grid, a trust‐based clustering solution that supports deploying CIDS in wireless sensor networks or a CIDS for Advanced Metering Infrastructure …”

Section: Related Workmentioning

confidence: 99%

“…As many cyberattacks can only be detected by correlating evidence provided from different sites, collaborative security solutions constitute a promising direction . In that concept, system monitoring data are gathered from multiple distributed locations and analyzed collectively to support security‐related decisions .…”

Section: Introductionmentioning

confidence: 99%

“…If managed properly, it also enables fine‐grained situational awareness (SA) that is indispensable to contemporary computer systems. For these reasons, collaborative security has been attracting much attention in the last decade . It is worth to note that so far collaborative security has been focusing on incident detection.…”

Section: Introductionmentioning

confidence: 99%

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Threat intelligence platform for the energy sector

Wróbel

2019

Softw Pract Exp

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In recent years, critical infrastructures and power systems in particular have been subjected to sophisticated cyberthreats, including targeted attacks and advanced persistent threats. A promising response to this challenging situation is building up enhanced threat intelligence (TI) that interlinks information sharing and fine-grained situation awareness. In this paper, a framework that integrates all levels of TI, ie, strategic, tactical, operational, and technical, is presented. The platform implements the centralized model of information exchange with peer-to-peer interactions between partners as an option. Several supportive solutions were introduced, including anonymity mechanisms or data processing and correlation algorithms. A data model that enables communication of cyberincident information, both in natural language and machine-readable formats, was defined. Similarly, security requirements for critical components were devised. A pilot implementation of the platform was developed and deployed in the operational environment, which enabled practical evaluation of the design. Also, the security of the anonymity architecture was analyzed.

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Section: Collaborative Security Information Sharing Situation Awarementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Threat intelligence platform for the energy sector

Wróbel

2019

Softw Pract Exp

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“…4 The IDS became a requisite component for wired networks, 5 wireless Ad-Hoc network, 6 wireless sensor network, 7 cloud computing, 8 and smart grids. 9,10 An IDS represents an important component for the network management, it is the first notifier that allows us to take counter measures in case of attack. An IDS can be classified into 2 categories: misuse detection and anomaly detection.…”

mentioning

confidence: 99%

An intrusion detection system based on combining probability predictions of a tree of classifiers

Ahmim

Derdour

Ferrag

2018

Int J Communication

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Intrusion detection system (IDS) represents an unavoidable tool to secure our network. It is considered as a second defense line against the different form of attacks. The principal limits of the current IDSs are their inability to combine the detection of the new form of attacks with high detection rate and low false alarm rate. In this paper, we propose an intrusion detection system based on the combination of the probability predictions of a tree of classifiers. Specifically, our model is composed of 2 layers. The first one is a tree of classifiers. The second layer is a classifier that combines the probability predictions of the tree.The built tree contains 4 levels where each node of this tree represents a classifier. The first node classifies the connections in 2 clusters: Denial of Service attacks and Cluster 2. Then, the second node classifies the connections of the Cluster 2 in Probing attacks and Cluster 3. The third node classifies the connections of the Cluster 3 in Remote-to-Local attacks and Cluster 4. Finally, the last node classifies the connections of the Cluster 4 in User-to-Root attacks and Normal connections. The second layer contains the last classifier that combines the probability predictions of the first layer and take the final decision. The experiments on KDD'99 and NSL-KDD show that our model gives a low false alarm rate and the highest detection rate. Furthermore, our model is more precise than the recent intrusion detection system models with accuracy equal to 96.27% for KDD'99 and 89.75% for NSL-KDD. KEYWORDScomputer security, data mining, hybrid IDS, hierarchical IDS, intrusion detection system, IDS, network security Int J Commun Syst. 2018;31:e3547.wileyonlinelibrary.com/journal/dac

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Collaborative Intrusion Detection in the Era of IoT: Recent Advances and Challenges

Li¹,

Meng²

2021

Security and Privacy in the Internet of Things

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A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems

Cited by 62 publications

References 15 publications

Threat intelligence platform for the energy sector

Threat intelligence platform for the energy sector

An intrusion detection system based on combining probability predictions of a tree of classifiers

Collaborative Intrusion Detection in the Era of IoT: Recent Advances and Challenges

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