Advances in Artificial Intelligence for Cybersecurity
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 966
Editor
Interests: AI for security; digital twin networks; Internet of Things; mobile computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rapid advances in artificial intelligence, machine learning, and data-driven technologies are transforming the cybersecurity landscape. As modern digital systems become more complex and interconnected, new and advanced cyber threats continue to surface. This generates an urgent need for intelligent, adaptable, and autonomous defence systems capable of protecting critical infrastructure, cloud environments, IoT ecosystems, and large-scale enterprise networks. Therefore, this Special Issue seeks to present innovative ideas, new methodologies, and experimental results in applying AI to cybersecurity challenges, from theoretical foundations and algorithmic developments to practical implementations and real-world case studies.
Areas relevant to AI-driven cybersecurity include, but are not limited to, intelligent threat detection, anomaly and malware analysis, intrusion detection systems, adversarial machine learning, secure federated learning, privacy-preserving AI, automated vulnerability assessment, and security analytics for big data environments. Research on AI models robust to adversarial attacks, AI for digital forensics, and the use of deep learning in securing networked and distributed systems is also of significant interest.
This Special Issue will publish high-quality, original research papers in the following overlapping fields:
- Artificial intelligence and machine learning for cybersecurity;
- Deep learning for threat detection and intrusion response;
- Adversarial machine learning and robust AI models;
- Malware detection, analysis, and classification;
- Network, cloud, IoT, and edge security analytics;
- Privacy-preserving and trustworthy AI;
- Secure federated and distributed learning;
- Cyber threat intelligence and automated security operations;
- Big data security and anomaly detection;
- AI-driven digital forensics and incident response.
We look forward to receiving your contributions.
Dr. Samuel Okegbile
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 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.
Keywords
- artificial intelligence for cybersecurity
- machine learning for threat detection
- deep learning in security
- adversarial machine learning
- intrusion detection systems
- malware analysis
- privacy-preserving AI
- cyber threat intelligence
- secure federated learning
- network and IoT security
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