eprintid: 9414 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/94/14 datestamp: 2023-11-09 16:21:24 lastmod: 2023-11-09 16:21:24 status_changed: 2023-11-09 16:15:04 type: article metadata_visibility: show creators_name: Patel, A. creators_name: Alhussian, H. creators_name: Pedersen, J.M. creators_name: Bounabat, B. creators_name: Júnior, J.C. creators_name: Katsikas, S. title: A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems ispublished: pub keywords: Computer crime; Ecosystems; Electric power transmission networks; Fuzzy logic; Knowledge based systems; Learning systems; Machine components; Network architecture; Network security; Ontology; Risk assessment; Smart power grids; Soft computing, Autonomic management; Distributed management; Intelligent infrastructures; Intrusion detection and prevention; Intrusion detection and prevention systems; Intrusion Detection Systems; SCADA; Smart grid, Intrusion detection note: cited By 60 abstract: Smart Grid (SG) systems are critical, intelligent infrastructure utility services connected through open networks that are potentially susceptible to cyber-attacks with very acute security risks of shutdown, loss of life, and loss of revenue. Traditional intrusion detection systems based on signature and anomaly techniques are no longer sufficient to protect SGs due to their new connectivity and management challenges, the ever-rapidly-evolving masquerades, and cyber criminality levied against them. SGs require cyber-security systems to render them resilient and protected through advanced Intrusion Detection and Prevention System (IDPS) techniques and mechanisms. This paper proposes a smart collaborative advanced IDPS to provide the best possible protection of SGs with a fully distributed management structure that supports the network and host based detections and the prevention of attacks. By facilitating a reliable, scalable, and flexible design, the specific requirements of IDPS for SGs can be more easily met via a fuzzy risk analyzer, an independent and ontology knowledge-based inference engine module. These can work collaboratively by managing functions across multiple IDPS domains. A set of extensive and intensive simulated experiments shows that with its smart advanced components incorporating soft computing machine-learning techniques and a rich ontology knowledge base with fuzzy logic analysis, it detects and prevents intrusions more efficiently. The multi-faceted results of the simulation also show that the proposed Collaborative Smart IDPS (CSIDPS) system increases the intrusion detection accuracy and decreases the false positive alarms when compared to traditional IDPSs. This is epitomized by the skillful use of the confusion matrix technique for organizing classifiers, visualizing their performance, and assessing their overall behavior. In the final analysis, the CSIDPS architecture is designed toward contributing to de facto norms for SG ecosystems. © 2016 Elsevier Ltd date: 2017 publisher: Elsevier Ltd official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994874812&doi=10.1016%2fj.cose.2016.07.002&partnerID=40&md5=5f6f90e6647d272108664e9c5f43cf5e id_number: 10.1016/j.cose.2016.07.002 full_text_status: none publication: Computers and Security volume: 64 pagerange: 92-109 refereed: TRUE issn: 01674048 citation: Patel, A. and Alhussian, H. and Pedersen, J.M. and Bounabat, B. and Júnior, J.C. and Katsikas, S. (2017) A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems. Computers and Security, 64. pp. 92-109. ISSN 01674048