A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis
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引用次数: 0

Abstract

Cyberattacks have increased radically over the last years, while the exploitation of Artificial Intelligence (AI) leads to the implementation of even smarter attacks which subsequently require solutions that will efficiently confront them. This need is indulged by incorporating Federated Intrusion Detection Systems (FIDS), which have been widely employed in multiple scenarios involving communication in cyber–physical systems. These include, but are not limited to, the Internet of Things (IoT) devices, Industrial IoT (IIoT), healthcare systems (Internet of Medical Things/IoMT), Internet of Vehicles (IoV), Smart Manufacturing (SM), Supervisory Control and Data Acquisition (SCADA) systems, Multi-access Edge Computing (MEC) devices, among others. Tackling the challenge of cyberthreats in all the aforementioned scenarios is of utmost importance for assuring the safety and continuous functionality of the operations, crucial for maintaining proper procedures in all Critical Infrastructures (CIs). For this purpose, pertinent knowledge of the current status in state-of-the-art (SOTA) federated intrusion detection methods is mandatory, towards encompassing while simultaneously evolving them in order to timely detect and mitigate cyberattack incidents. In this study, we address this challenge and provide the readers with an overview of FL implementations regarding Intrusion Detection in several CIs. Additionally, the distinct communication protocols, attack types and datasets utilized are thoroughly discussed. Finally, the latest Machine Learning (ML) and Deep Learning (DL) frameworks and libraries to implement such methods are also provided.
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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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