Advanced Technologies for Network Security and Anomaly Detection

Dear Colleagues,

The increasing complexity of software and networked systems has made the timely discovery, assessment, and validation of security weaknesses increasingly challenging. Traditional vulnerability analysis and security testing methods often require substantial manual effort and may struggle to scale with rapidly evolving systems and attack surfaces. At the same time, anomalous and malicious behaviors are becoming more adaptive and difficult to identify using static rules or conventional detection mechanisms.

Recent advances in artificial intelligence (AI), machine learning, large language models, graph-based learning, and autonomous agents are creating new opportunities to automate and enhance key cybersecurity tasks. AI-assisted vulnerability discovery can support the identification and prioritization of previously unknown weaknesses, while automated security assessment and intelligent penetration testing can improve attack-path exploration and vulnerability validation. In parallel, advanced anomaly detection methods can help to identify suspicious behaviors and emerging threats by learning complex patterns from security-relevant data. Despite this progress, important challenges remain regarding reliability, generalization, explainability, robustness, scalability, and the practical validation of AI-driven security technologies.

This Special Issue will present recent advances in AI-assisted vulnerability discovery, automated security assessment, and intelligent anomaly detection. We invite original research articles, reviews, and practical studies that develop novel methods, systems, frameworks, datasets, or evaluation methodologies for automating the discovery, assessment, validation, and detection of cybersecurity risks. Particular interest is paid to approaches that combine artificial intelligence with vulnerability analysis, penetration testing, attack-path reasoning, and anomaly detection, as well as studies addressing the reliability, robustness, explainability, and real-world applicability of such technologies. Topics of interest for publication include, but are not limited to, the following:

* AI-assisted vulnerability discovery and detection;

* Vulnerability assessment, prioritization, and validation;

* Automated and intelligent penetration testing;

* Automated security assessment and red-team technologies;

* Attack surface analysis and attack-path discovery;

* Large language models and autonomous agents for cybersecurity;

* Machine learning and deep learning for security analysis;

* Graph-based learning and reasoning for vulnerability and attack analysis;

* Intelligent anomaly and malicious behavior detection;

* Zero-day and emerging threat detection;

* Adversarial robustness and trustworthy AI for security applications;

* Explainable AI for vulnerability analysis and anomaly detection;

* Security datasets, benchmarks, and evaluation methodologies;

* Practical validation of AI-driven security assessment and detection systems.

Prof. Dr. Yong Fang
Dr. Yijia Xu
Prof. Dr. Chengwei Liu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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.

• network security
• anomaly detection
• vulnerability detection
• automated penetration testing
• artificial intelligence
• security assessment