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MLTIIDS 2020 : Special Issue on Machine Learning Techniques for Intelligent Intrusion Detection Systems


When N/A
Where N/A
Submission Deadline Jun 30, 2020
Categories    intrusion detection   machine learning   cybersecurity   intelligent methods

Call For Papers

Security and privacy of data is one of the major concerns in today’s world, and intrusion detection systems (IDS) play an important role in cybersecurity. Industry 4.0 ecosystems are able to collect data, interconnect between each other, and process and make decisions without any human interaction. Currently, the amount of data traveling through networks is overwhelming from the perspective of the veracity and variety of the data that are transmitted, the volume of the information, and velocity of the Internet links. This resembles well-known paradigm Big Data in addition to the omnipresent usage of the encryption and creates multiple challenges when it comes to effective detection of distributed denial of service (DDoS) attacks, advanced persistent threats (APT), and distribution of malware infection. Conventional intrusion detection systems utilize the signature-based approach that helps to identify known attacks and protect the network. However, those are less efficient when it comes to tailored attacks, APT, Zero-Day attack, encryption, and distributed reconnaissance, due to the large volume and sophistication. Fortunately, machine learning can aid in solving the most common tasks, including regression, prediction, and classification. Machine learning techniques have been effectively used in multiple applications in intelligent intrusion detection systems, including network traffic analysis, access logs analysis, spam, and malware detection. However, current machine learning methods and their implementations are designed to handle tens of thousands of data yet have complexity issues with bigger datasets. Big Data analytics require new and enhanced models to handle complex problems as network attacks detection. Future intelligent intrusion detection systems require faster and more accurate machine learning models. Therefore, it is important to improve the existing and find proper ways of designing new machine learning methods suitable to detect indicators of compromise and find malicious connections even if the network traffic is encrypted. This Special Issue provides a platform for discussing new developments in the intersection of security and privacy with machine learning and deep learning.


Manuscripts should be submitted online at 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. Electronics is an international peer-reviewed open access monthly 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 1400 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.

- Cybersecurity
- Cybercrime
- Security, trust, and privacy
- Anomaly intrusion detection
- Distributed intrusion detection
- Hybrid intrusion detection
- Adversarial attacks
- Machine learning
- Deep learning
- Big Data analytics
- IoT
- Blockchain
- Cloud computing

-Assoc. Prof. Dr. Mamoun Alazab
Charles Darwin University, Casuarina, NT, Australia

- Dr. Andrii Shalaginov
Norwegian University of Science and Technology, Gjøvik, Norway

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