Artificial Intelligence in Effective Intrusion Detection for Clouds
A special issue of Network (ISSN 2673-8732).
Deadline for manuscript submissions: 30 November 2026 | Viewed by 1721
Special Issue Editor
Interests: machine learning with applications in cyber security (digital forensics; intrusion prevention detection and response; social engineering; OSINT; insider threats; malware and distributed denial of service attacks; AI application in cybersecurity)
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cloud computing has revolutionized the way organizations manage and store data, offering unparalleled scalability, flexibility, and cost-efficiency. However, the widespread adoption of cloud services has also introduced new security challenges, including the risk of cyber attacks and data breaches. Intrusion detection systems (IDSs) play a critical role in protecting cloud infrastructures by monitoring and identifying malicious activities in real-time. Traditional IDS approaches, however, often struggle to keep up with the rapidly evolving nature of cyber threats.
To address these challenges, this Special Issue focuses on the integration of artificial intelligence (AI) techniques to enhance the effectiveness of intrusion detection in cloud environments. AI offers the promise of intelligent, adaptive, and autonomous IDS solutions capable of identifying and mitigating security breaches more effectively than traditional methods.
The goal of this Special Issue is to bring together researchers and practitioners from academia and industry to explore recent advances, challenges, and opportunities in AI-driven intrusion detection for clouds. We invite original research articles and review papers that contribute to the development of innovative AI-based IDS solutions for cloud security. Topics include, but are not limited to, the following:
- Novel AI algorithms and models for intrusion detection in cloud environments;
- Integration of machine learning, deep learning, and data mining in IDS for clouds;
- Adaptive and self-learning IDS systems for dynamic cloud architectures;
- Big data analytics and anomaly detection for cloud security;
- AI-driven threat intelligence and risk assessment in cloud infrastructures;
- Performance evaluation and benchmarking of AI-based IDS solutions;
- Privacy-preserving techniques and ethical considerations in AI-driven intrusion detection;
- Case studies, practical implementations, and real-world applications of AI in cloud security.
You may choose our Joint Special Issue in Electronics.
Dr. Stavros Shiaeles
Guest Editor
Manuscript Submission Information
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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. Network is an international peer-reviewed open access quarterly 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 1200 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
- intrusion detection
- cloud computing
- machine learning
- deep learning
- cybersecurity
- network security
- threat detection
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