Machine Learning for Proactive and Reactive IoT Security
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 3580
Special Issue Editors
Interests: Internet of Things (IoT); attack detection; software vulnerability prediction; Markovian modelling; internet traffic modelling
Special Issue Information
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
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Keywords
- Machine learning
- Internet of things
- IoT data analysis
- Cyberattack detection
- Intrusion detection systems (IDS) and intrusion prevention systems (IPS)
- Malware mitigation
- Botnet detection and mitigation
- Security of artificial intelligence
- Software vulnerability prediction
- Software security
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