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Electronics
Special Issues
Prevention, Detection, Reaction and Mitigation of Physical and Cyber Threats
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Prevention, Detection, Reaction and Mitigation of Physical and Cyber Threats

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 9666

Special Issue Editors

Prof. Dr. Ana Rosa Cavalli

E-Mail Website
Guest Editor
Department of Mobile Networks and Services, Institute Mines-Telecom/Telecom Sud Paris, CEDEX, 91011 Evry, France
Interests: networks protocols; networks monitoring; network security; cybersecurity; Internet of Things; formal modelling and testing
Special Issues, Collections and Topics in MDPI journals
Dr. Wissam Mallouli

E-Mail Website
Guest Editor
Montimage, 75013 Paris, France
Interests: formal modelling and testing; network security; cybersecurity; security of distributed systems

Special Issue Information

Dear Colleagues,

The underpinning idea of this Special Issue is to present surveys, research work, and industrial experiences on the techniques that are developed to prevent, detect, react, and mitigate physical, cyberthreats, and their combination. This includes vulnerability and threat identification, different advanced testing and monitoring techniques, resilience techniques, root cause analysis, and deployment of security controls that can be applied to distributed systems and networks (e.g., cloud/Edge/Fog computing, CPS/IoT, 4G/5G and beyond, SDN/NFV, and other domains).

The expected papers can be surveys, research work, and industrial experiences.  

Topics can be:

  • Secure design of complex hardware and software systems;
  • Model checking for security properties;
  • Smart vulnerability scanning and threat identification;
  • Advanced Intrusion and attack detection techniques;
  • Smart testing techniques for cybersecurity;
  • Advanced monitoring techniques for cybersecurity;
  • Root cause analysis and reaction strategies;
  • AI/ML-based SIEM;
  • Correlation between physical and cyberthreats;
  • Automatic mitigation and reaction;
  • Resilience techniques;
  • Cost and impact of countermeasures;
  • Continuous risk assessment and risk management;
  • Cybersecurity in agile development process;
  • DevSecOps;
  • ML/AI-based cybersecurity;
  • Blockchain-based cybersecurity;
  • Prevention and protection in distributed systems and networks;
  • Innovative cybersecurity methods for cloud, edge, fog, IoT, CPS, 4G/5G, and SDN/NFV environments.

Prof. Dr. Ana Rosa Cavalli
Dr. Wissam Mallouli
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.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

29 pages, 1208 KiB  
Article
A Framework for the Attack Tolerance of Cloud Applications Based on Web Services
by Georges Ouffoué, Fatiha Zaïdi, Ana R. Cavalli and Huu Nghia Nguyen
Electronics 2021, 10(1), 6; https://doi.org/10.3390/electronics10010006 - 23 Dec 2020
Cited by 3 | Viewed by 2590
Abstract
Information systems of companies and organizations are increasingly designed using web services that allow different applications written in different programming languages to communicate. These systems or some parts of them are often outsourced on the cloud, first to leverage the benefits of cloud [...] Read more.
Information systems of companies and organizations are increasingly designed using web services that allow different applications written in different programming languages to communicate. These systems or some parts of them are often outsourced on the cloud, first to leverage the benefits of cloud platforms (e.g., scalability) and also to reduce operational costs of companies as well. However, web services as well as cloud platforms may be the target of attacks that alter their security, and the security of web services is not completely addressed. The solutions proposed in the literature are sometimes specific to certain types of attacks and they cannot ensure the attack tolerance of web services. Attack tolerance can be defined as the capability of a system to function properly with minimal degradation of performance, even if the presence of an attack is detected. As such, we claim that, to achieve attack tolerance, one should detect attacks by a continuous monitoring and mitigate the effects of these attacks by reliable reaction mechanisms. For this aim, an attack tolerance framework is proposed in this paper. This framework includes the risks analysis of attacks and is based on diversification and software reflection techniques. We applied this framework to cloud applications that are based on web services. After describing the core foundation of this approach, we express such cloud applications as choreographies of web services according to their distributed nature. The framework has been validated through an electronic voting system. The results of these experiments show the capability of the framework to ensure the required attack tolerance of cloud applications. Full article
(This article belongs to the Special Issue Prevention, Detection, Reaction and Mitigation of Physical and Cyber Threats)
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21 pages, 570 KiB  
Article
MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System
by Muhammad Ali, Stavros Shiaeles, Gueltoum Bendiab and Bogdan Ghita
Electronics 2020, 9(11), 1777; https://doi.org/10.3390/electronics9111777 - 26 Oct 2020
Cited by 41 | Viewed by 5958
Abstract
Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In [...] Read more.
Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used. Full article
(This article belongs to the Special Issue Prevention, Detection, Reaction and Mitigation of Physical and Cyber Threats)
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