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Electronics
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AI-Based Solutions for Cybersecurity
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AI-Based Solutions for Cybersecurity

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1904

Special Issue Editors

Dr. Eunjung Choi

E-Mail Website
Guest Editor
Department of Information Security, Seoul Women’s University, Seoul 01797, Republic of Korea
Interests: artificial intelligence; cybersecurity; malware; privacy; OSINT
Special Issues, Collections and Topics in MDPI journals
Dr. Jiyeon Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
Interests: artificial intelligence; cybersecurity; digital twin; cloud and IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI is having a significant impact on cybersecurity, and there are also concerns related to the security of AI, which include trust in AI, the ethical application of AI, and cybersecurity.

AI is able to play a positive role in cybersecurity, aiding in threat anticipation and case summarization, while cybersecurity will be essential to ensure AI’s trustworthiness.

AI solution is becoming increasingly important in the field of cybersecurity, and it is expected to continue to play a significant role in the years to come.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • AI-based threat detection;
  • Behavioral analytics;
  • Cybersecurity automation;
  • AI-powered authentication;
  • Adversarial machine learning;
  • AI in IoT security;
  • Cyber threat intelligence;
  • AI and cloud security;
  • AI-based security analytics;
  • Ethical considerations.

Dr. Eunjung Choi
Dr. Jiyeon Kim
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. Electronics 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 2400 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

  • AI solution for cybersecurity
  • machine learning-based solution for cybersecurity
  • deep learning-based solution for cybersecurity
  • AI solution for cloud security
  • AI solution for privacy

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Published Papers (2 papers)

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Research

39 pages, 21483 KiB  
Article
SPM-FL: A Federated Learning Privacy-Protection Mechanism Based on Local Differential Privacy
by Zhiyan Chen and Hong Zheng
Electronics 2024, 13(20), 4091; https://doi.org/10.3390/electronics13204091 - 17 Oct 2024
Viewed by 726
Abstract
Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as model weights can still be [...] Read more.
Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as model weights can still be analyzed or attacked, leading to potential privacy breaches. Traditional federated learning methods often disturb models by adding Gaussian or Laplacian noise, but under smaller privacy budgets, the large variance of the noise adversely affects model accuracy. To address this issue, this paper proposes a Symmetric Partition Mechanism (SPM), which probabilistically perturbs the sign of local model weight parameters before model aggregation. This mechanism satisfies strict ϵ-differential privacy, while introducing a variance constraint mechanism that effectively reduces the impact of noise interference on model performance. Compared with traditional methods, SPM generates smaller variance under the same privacy budget, thereby improving model accuracy and being applicable to scenarios with varying numbers of clients. Through theoretical analysis and experimental validation on multiple datasets, this paper demonstrates the effectiveness and privacy-protection capabilities of the proposed mechanism. Full article
(This article belongs to the Special Issue AI-Based Solutions for Cybersecurity)
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17 pages, 2567 KiB  
Article
Dynamic Cyberattack Simulation: Integrating Improved Deep Reinforcement Learning with the MITRE-ATT&CK Framework
by Sang Ho Oh, Jeongyoon Kim and Jongyoul Park
Electronics 2024, 13(14), 2831; https://doi.org/10.3390/electronics13142831 - 18 Jul 2024
Viewed by 829
Abstract
As cyberattacks become increasingly sophisticated and frequent, it is crucial to develop robust cybersecurity measures that can withstand adversarial attacks. Adversarial simulation is an effective technique for evaluating the security of systems against various types of cyber threats. However, traditional adversarial simulation methods [...] Read more.
As cyberattacks become increasingly sophisticated and frequent, it is crucial to develop robust cybersecurity measures that can withstand adversarial attacks. Adversarial simulation is an effective technique for evaluating the security of systems against various types of cyber threats. However, traditional adversarial simulation methods may not capture the complexity and unpredictability of real-world cyberattacks. In this paper, we propose the improved deep reinforcement learning (DRL) algorithm to enhance adversarial attack simulation for cybersecurity with real-world scenarios from MITRE-ATT&CK. We first describe the challenges of traditional adversarial simulation and the potential benefits of using DRL. We then present an improved DRL-based simulation framework that can realistically simulate complex and dynamic cyberattacks. We evaluate the proposed DRL framework using a cyberattack scenario and demonstrate its effectiveness by comparing it with existing DRL algorithms. Overall, our results suggest that DRL has significant potential for enhancing adversarial simulation for cybersecurity in real-world environments. This paper contributes to developing more robust and effective cybersecurity measures that can adapt to the evolving threat landscape of the digital world. Full article
(This article belongs to the Special Issue AI-Based Solutions for Cybersecurity)
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