Machine Learning for Proactive and Reactive IoT Security

Internet trends analysis suggests that we are on the verge of entering a new era in which the Internet will no longer be dominated by traditional human to human interactions. The share of machine to machine (M2M) connections is predicted to reach 50 percent by 2023 and its popularity is growing fast. It is this shift that should be a sign to reconsider the analysis of Internet traffic. M2M communication is usually used by Internet of things devices.

IoT connects machines, people, data and processes using the Internet. Although its popularity and reliance put on these devices are constantly increasing, the nature of its traffic is still largely unknown. Although much effort is made to facilitate the security of the IoT devices and M2M connections by introducing new security standards and guidelines, many IoT manufacturers focus mainly on size, usability and cost of the IoT devices.

Because of their constraints (limited memory, computational power, battery supply), IoT devices are more prone to cyberattacks than the traditional ones. Therefore, it is crucial to create novel, efficient and accurate intrusion prevention systems and intrusion detection systems focused precisely on these vulnerable IoT devices.

Besides this reactive approach to security, effort should be made to improve the security of the IoT devices before their deployment. It can be done by delivering accurate software vulnerability prediction systems. Security vulnerabilities are often introduced during the coding stage of the software development life cycle (SDLC). Early detection of such issues can be used to remove or repair the vulnerabilities before they become apparent. Severe consequences of data breaches result in security being foundational and a top IT priority.

This Special Issue focuses on IoT data analysis, as well as new techniques, methodologies, and tools for software vulnerability prediction and attack detection, especially those using machine learning techniques, which exhibit the versatility and great power to recognise patterns in the heterogeneous data. Additionally, the contributions regarding the IoT standards and actual IoT deployment case studies are welcome. We also encourage the authors to provide novel, reliable IoT-specific datasets, which are necessary to accurately verify the security solutions, both the proactive and the reactive ones.

Dr. Joanna Domańska
Dr. Slawomir Nowak
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy 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 2600 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.