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Algorithms
Special Issues
Artificial Intelligence in Intrusion Detection Systems
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Artificial Intelligence in Intrusion Detection Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 2409

Special Issue Editor

Dr. Angelo Spognardi

E-Mail Website
Guest Editor
Department of Computer Science, University of Rome, 00161 Roma RM, Italy
Interests: anomaly detection;, network and computer security;, social network security

Special Issue Information

Dear Colleagues,

With the rapid development of computer technology and the continuous expansion of application fields, the detection and prevention of intrusion attacks have become urgent and important issues, in addition to the security of computer systems, network systems, and the entire information infrastructure. Intrusion detection technology is designed and focused on ensuring the security of computer systems through the timely detection and reporting of unauthorized or abnormal phenomena in the systems. It is a technology also used to detect violations of security policies in computer networks. The combination of artificial intelligence and intrusion detection technology will overcome some shortcomings of traditional intrusion detection systems. At the same time, it will also have an excellent impetus for artificial intelligence technology itself. The leading artificial intelligence technologies currently used in intrusion detection systems are expert systems, artificial neural networks, data mining technology, artificial immune technology, autonomous agents, data fusion, and other technologies. The community agrees that the combination of new intrusion detection approaches and the adoption of artificial intelligence technology will significantly improve the performance of existing intrusion detection systems and, at the same time, encourage the design of new artificial intelligence algorithms.

Dr. Angelo Spognardi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • anomaly detection
  • network and computer security
  • social network security

Published Papers (1 paper)

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Research

15 pages, 3252 KiB  
Article
An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
by Lei Chen, Zhihao Wang, Ru Huo and Tao Huang
Algorithms 2023, 16(4), 197; https://doi.org/10.3390/a16040197 - 05 Apr 2023
Cited by 6 | Viewed by 1833
Abstract
As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers [...] Read more.
As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence in Intrusion Detection Systems)
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