Recent Advances in Anomaly Detection and Network Security

Dear Colleagues,

In the digital transformation era, the volume and complexity of data transmitted across networks have increased exponentially. With the proliferation of connected devices, cloud computing, and IoT, safeguarding data integrity and ensuring network security have become paramount. Anomaly detection is a crucial aspect of network security, enabling the identification of unusual patterns that may indicate malicious activities such as cyber-attacks, unauthorized access, or data breaches.

Traditional network security methods are becoming less effective in the face of sophisticated threats, which can adapt and evolve to bypass conventional detection systems. Consequently, there is a growing need for advanced anomaly detection techniques that leverage machine learning, artificial intelligence, and big data analytics. These methods can improve the accuracy and speed of threat detection, enabling organizations to respond swiftly and mitigate potential damage.

The scientific community has made significant strides in developing novel algorithms and models for anomaly detection. These advances enhance security measures and contribute to a deeper understanding of the underlying principles of network behavior. This Special Issue aims to consolidate recent research efforts in this critical area, fostering collaboration and innovation.

Aim of the Special Issue

The Special Issue on “Recent Advances in Anomaly Detection and Network Security” seeks to provide a comprehensive platform for disseminating cutting-edge research in this dynamic field. The goal is to bring together contributions that address both theoretical and practical aspects of anomaly detection, with a particular focus on applications in network security.

This Special Issue aligns with the scope of the journal Electronics by exploring the intersection of electronics, information technology, and security systems. The issue will include papers that present novel methodologies, frameworks, and systems that enhance the security of electronic communications and networks. By doing so, it aims to push the boundaries of what is possible in electronic security and anomaly detection, making a meaningful contribution to the field.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Machine learning-enhanced hardware and AI for anomaly detection.
• Edge computing and big data analytics in network security.
• Hardware acceleration of machine learning for security applications.
• Embedded machine learning for IoT security.
• Energy-efficient ML algorithms in hardware.
• Intrusion detection systems (IDSs).
• Cyber–physical system security.
• Blockchain and distributed ledger technologies.
• Emerging threats and zero-day attacks.
• Signal processing techniques for security.
• ML-driven signal processing for anomaly detection.
• Security in hardware design.
• Cybersecurity for medical devices.
• Biomedical secure engineering.
• Integrated IoT and blockchain applications.
• Adversarial machine learning anomaly detection models.
• FPGA and ASIC solutions for anomaly detection.
• VLSI architecture for ML models.
• Reconfigurable hardware for adaptive anomaly detection.

We look forward to receiving your contributions.

Dr. Zag ElSayed
Dr. Murat Ozer
Dr. Basheer Qolomany
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.

• anomaly detection
• network security
• machine learning
• edge computing
• IoT security FPGA
• cyber–physical systems
• reconfigurable hardware
• trusted execution environments (TEEs)
• real-time security
• big data analytics