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Artificial Intelligence and Machine Learning for Detection of Advanced Cyber...
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Artificial Intelligence and Machine Learning for Detection of Advanced Cyber Attacks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (1 March 2023) | Viewed by 7756

Special Issue Editors

Dr. Ankur Chowdhary

E-Mail Website
Guest Editor
Information Assurance Center, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281, USA
Interests: cloud security; software defined networks; application of artificial intelligence and machine learning in the field of cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Adel Alshamrani

E-Mail Website
Guest Editor
Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
Interests: cloud security; SDN; APT

Special Issue Information

Dear Colleagues,

Recent years have seen a surge in advanced cyberattacks, such as using adaptive phishing toolkits, adaptive malware, and ransomware. Some of these attacks can be classified as advanced persistent threats (APTs). While research has explored this topic in the past, the practical adoption of AI and ML solutions to deal with advanced cyber attacks is limited. Moreover, there are limited datasets that can be used universally to generalize detection for these attacks. This issue explores using artificial intelligence and machine learning techniques to detect and combat these advanced cyber attacks. The scope of the Special Issue includes:

  • AI/ML algorithms that improve detection against advanced cyber attacks;
  • Practical learnings in large-scale application of AI and ML against advanced cyber attacks;
  • Datasets that help to improve the detection and mitigation of advanced attacks;
  • Practical considerations for detections of attacks such as APT at scale;
  • Challenges in the detection of stealthy attacks such as APT;
  • Cost–benefit analysis of AI/ML techniques used.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Penelope Wang via <[email protected]> directly.

Dr. Ankur Chowdhary
Dr. Adel Alshamrani
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • advanced cyber attacks
  • advanced persistent threat (APT)
  • Artificial Intelligence
  • Machine Learning
  • datasets
  • practical learnings
  • cost-benefit analysis

Published Papers (2 papers)

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Research

17 pages, 4296 KiB  
Article
Evaluation of Machine Learning Algorithms for Malware Detection
by Muhammad Shoaib Akhtar and Tao Feng
Sensors 2023, 23(2), 946; https://doi.org/10.3390/s23020946 - 13 Jan 2023
Cited by 13 | Viewed by 4823
Abstract
This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety [...] Read more.
This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety by exploiting vulnerabilities in the Internet. The proliferation of harmful software has rendered manual heuristic examination of malware analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into sparse vector models for use in further machine learning efforts. Classifiers used to synthesise the results of this study included kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB classifier. After reviewing the test and experimental data for all five classifiers, we found that the RF, SGD, extra trees and Gaussian NB Classifier all achieved a 100% accuracy in the test, as well as a perfect precision (1.00), a good recall (1.00), and a good f1-score (1.00). Therefore, it is reasonable to assume that the proof-of-concept employing autonomous behaviour-based malware analysis and machine learning methodologies might identify malware effectively and rapidly. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Detection of Advanced Cyber Attacks)
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21 pages, 941 KiB  
Article
A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed
by Georgios Koutroulis, Belgin Mutlu and Roman Kern
Sensors 2023, 23(1), 257; https://doi.org/10.3390/s23010257 - 27 Dec 2022
Cited by 2 | Viewed by 2310
Abstract
Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also [...] Read more.
Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also to the public Internet, mostly for remote control and monitoring purposes. Sophisticated cyber-attacks may take advantage the increased interconnectedness or other security gaps of an ICS and infiltrate the system with devastating consequences to the economy, national security, and even human life. Due to the paramount importance of detecting and isolating these attacks, we propose an unsupervised anomaly detection approach that employs causal inference to construct a robust anomaly score in two phases. First, minimal domain knowledge via causal models helps identify critical interdependencies in the system, while univariate models contribute to individually learn the normal behavior of the system’s components. In the final phase, we employ the extreme studentized deviate (ESD) on the computed score to detect attacks and to exclude any irrelevant sensor signals. Our approach is validated on the widely used Secure Water Treatment (SWaT) benchmark, and it exhibits the highest F1 score with zero false alarms, which is extremely important for real-world deployment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Detection of Advanced Cyber Attacks)
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