Special Issue "Advanced Intrusion Detection & Mitigation Systems in Wireless Sensor Networks"
Deadline for manuscript submissions: 30 September 2019
Prof. Dr. Salvatore Domenic Morgera
Wireless sensor network cybersecurity is a process of continuous improvement. As the number of wireless sensor networks grows, and as these networks increasingly connect to the Internet, they will become both more powerful data gathering agents and more vulnerable to malicious attacks. Encryption does not provide a sufficient level of protection from malicious attacks and, eventually, intrusion detection and mitigation (IDM) systems will be de-facto standard components of wireless sensor networks.
The complement of papers in this Special Issue will confirm the fact that designing an effective IDM system is both an engineering/computer science accomplishment and an art and requires a deep understanding of networking, combined with a realistic simulation and, if possible, field experience. Our use of the word “effective” implies that the reader will learn of advanced IDM systems that are robust and exhibit low false alarm behavior.
Advanced IDM systems having these characteristics generally focus on the intention of an attack, rather than just a specific type of attack or methodology. These systems generally run multiple intelligent algorithms or statistical anomaly-based detectors and use sophisticated cross-layer approaches. Instead of only looking for a specific attack signature, these advanced IDM systems check cross-layer data and establish a relationship between the information in that data and its potential impact on the wireless sensor network from the security viewpoint.
In this framework, we are very pleased to edit this Special Issue on “Advanced Intrusion Detection and Mitigation Systems in Wireless Sensor Networks”. The Special Issue is dedicated to presenting advanced IDM system designs that demonstrate superior performance in wireless sensor networks. IDM system designs for other networks, such as MANETs, military networks, and high value legacy networks, are also especially welcome, as are IDM system designs for specific wireless network scenarios that employ active and passive sensing from satellite, aerial or drone platforms, methods that secure biomedical networks and medical device networks from intrusion, and intrusion detection and mitigation methods based on those found in the human immune system.
Prof. Dr. Salvatore Domenic Morgera
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 papers will be 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. Sensors 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 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.
- Wireless sensor networks
- Mobile ad hoc networks
- Biomedical networks
- Anomaly intrusion detection
- Distributed intrusion detection
- Hybrid intrusion detection
- Cross-layer techniques
- Machine learning
- Deep learning
- Human immune system