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Algorithms
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Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges
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Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 736

Special Issue Editors

Dr. Sihai Tang

E-Mail Website
Guest Editor
Department of Computer Science, Schreiner University, Kerrville TX 78028, USA
Interests: artificial intelligence; edge computing; connected autonomous vehicles; LLM; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Song Fu

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
Interests: connected and autonomous vehicles; edge and cloud computing; cyberinfrastructures; cybersecurity; distributed and IoT systems; intelligent systems; machine learning; high performance computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into cybersecurity has significantly influenced the methodologies employed in threat detection, risk assessment, and incident response. AI techniques, including machine learning and deep learning, have been utilized to analyze complex datasets, identify anomalies, and automate responses to potential threats. This Special Issue aims to examine the current landscape of AI applications in cybersecurity, focusing on the development and evaluation of algorithms that enhance security measures.

We invite contributions that explore theoretical frameworks, algorithmic innovations, and practical implementations of AI in cybersecurity contexts. Topics of interest include, but are not limited to, AI-based intrusion detection systems, malware analysis, threat intelligence, privacy-preserving machine learning, and the ethical considerations surrounding the deployment of AI in security infrastructures. By consolidating research from academia and industry, this issue seeks to provide a comprehensive overview of the advancements and ongoing challenges at the nexus of AI and cybersecurity.

Dr. Sihai Tang
Prof. Dr. Song Fu
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. 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 1800 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

  • artificial intelligence (AI)
  • cybersecurity
  • machine learning
  • intrusion detection systems
  • malware analysis
  • threat intelligence
  • privacy-preserving machine learning
  • ethical AI
  • security automation
  • anomaly detection

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Published Papers (2 papers)

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Research

19 pages, 3288 KB  
Article
A Transformer-Based Framework for DDoS Attack Detection via Temporal Dependency and Behavioral Pattern Modeling
by Yi Li, Xingzhou Deng, Ang Yang and Jing Gao
Algorithms 2025, 18(10), 628; https://doi.org/10.3390/a18100628 - 4 Oct 2025
Viewed by 241
Abstract
With the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks. Traditional DDoS detection systems face significant challenges due to the unpredictable nature, diverse protocols, and coupled behavioral patterns of attack [...] Read more.
With the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks. Traditional DDoS detection systems face significant challenges due to the unpredictable nature, diverse protocols, and coupled behavioral patterns of attack traffic. To address this issue, this paper proposes a novel approach for DDoS attack detection by leveraging the Transformer architecture to model both temporal dependencies and behavioral patterns, significantly improving detection accuracy. We utilize the global attention mechanism of the Transformer to effectively capture long-range temporal correlations in network traffic, and the model’s ability to process multiple traffic features simultaneously enables it to identify nonlinear interactions. By reconstructing the CIC-DDoS2019 dataset, we strengthen the representation of attack behaviors, enabling the model to capture dynamic attack patterns and subtle traffic anomalies. This approach represents a key contribution by applying Transformer-based self-attention mechanisms to accurately model DDoS attack traffic, particularly in handling complex and dynamic attack patterns. Experimental results demonstrate that the proposed method achieves 99.9% accuracy, with 100% precision, recall, and F1 score, showcasing its potential for high-precision, low-false-alarm automated DDoS attack detection. This study provides a new solution for real-time DDoS detection and holds significant practical implications for cybersecurity systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)
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21 pages, 2310 KB  
Article
Development of a Model for Detecting Spectrum Sensing Data Falsification Attack in Mobile Cognitive Radio Networks Integrating Artificial Intelligence Techniques
by Lina María Yara Cifuentes, Ernesto Cadena Muñoz and Rafael Cubillos Sánchez
Algorithms 2025, 18(10), 596; https://doi.org/10.3390/a18100596 - 24 Sep 2025
Viewed by 279
Abstract
Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but [...] Read more.
Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)
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