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Electronics
Special Issues
Emerging Technologies for Security Applications
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Emerging Technologies for Security Applications

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 8027

Special Issue Editors

Dr. Cédric Marchand

E-Mail Website
Guest Editor
Lyon Institute of Nanotechnology (INL) Ecole Centrale de Lyon, University of Lyon, 69007 Lyon, France
Interests: hardware security; cryptography; Emerging technologie
Prof. Laurent Sauvage

E-Mail Website
Guest Editor
LTCI, Télécom Paris, Institut polytechnique de Paris, Paris, France
Interests: cryptography; cybersecurity; side-channel attack; fault injection attack; embedded systems
Dr. David Navarro

E-Mail Website
Guest Editor
Université de Lyon, ECL, INSA Lyon, UCBL, CPE, INL, UMR5270, F-69134 Ecully, France
Interests: low power MCU; embedded electronics; communications; IoT; WSN
Special Issues, Collections and Topics in MDPI journals
Dr. Jérémy Postel-Pellerin

E-Mail Website
Guest Editor
CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Aix-Marseille University, 13451 Marseille, France
Interests: non-volatile memories; TCAD simulation; electrical characterization for reliability and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In recent years, the security of private and collected data has become a major topic in embedded systems and electronic circuit designs. With the classical Von Neumann and Harvard architecture bottleneck, there is increasing interest in emerging technologies such as new nonvolatile memories (RRAM, MRAM, FeRAM, etc.) and silicon photonics. These emerging technologies enable us to overcome this bottleneck and propose new architectures based on logic in memory, photonic phase change materials, new sensing technologies and so on.

These technologies change the computing system and bring new strengths and weaknesses to the security of the whole system. Their robustness against attacks is still a major concern and needs to be further studied, but they can also be used to create a specific piece of hardware to increase the security of a computing system such as physical unclonable functions (PUFs), true random number generators (TRNGs) or cryptographic primitives.

This Special Issue is dedicated to security aspects linked to emerging technologies and the list of possible topics includes, but is not limited to:

  • Challenges and opportunities of new technologies for secure and energy efficient embedded systems.
  • Emerging technology-based implementation of cryptographic algorithms.
  • Emerging technology-based design, implementation, and evaluation of secure primitives for electronic computing systems.
  • Software- and hardware-oriented attacks and evaluation of implementation based on emerging technologies.
  • Robustness of emerging devices or circuits against attacks and countermeasures.
  • Modeling of attacks in emerging technologies.
  • Secure architectures with emerging technologies (processors, microcontrollers, sensor networks, etc.)

Dr. Cédric Marchand
Prof. Laurent Sauvage
Dr. David Navarro
Dr. Jérémy Postel-Pellerin
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.

Keywords

  • Challenges and opportunities of new technologies for secure and energy efficient embedded systems
  • Emerging technology-based implementation of cryptographic algorithms
  • Emerging technology-based design, implementation, and evaluation of secure primitives for electronic computing systems
  • Software- and hardware-oriented attacks and evaluation of implementation based on emerging technologies
  • Robustness of emerging devices or circuits against attacks and countermeasures
  • Modeling of attacks in emerging technologies
  • Secure architectures with emerging technologies (processors, microcontrollers, sensor networks, etc.)

Published Papers (2 papers)

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Research

16 pages, 2965 KiB  
Article
Detection of Adversarial DDoS Attacks Using Symmetric Defense Generative Adversarial Networks
by Chin-Shiuh Shieh, Thanh-Tuan Nguyen, Wan-Wei Lin, Wei Kuang Lai, Mong-Fong Horng and Denis Miu
Electronics 2022, 11(13), 1977; https://doi.org/10.3390/electronics11131977 - 24 Jun 2022
Cited by 7 | Viewed by 1930
Abstract
DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the [...] Read more.
DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a critical component of the creation of an integrated defensive system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers have applied machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have continuously increased and evolved since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric generative adversarial network (GAN) architecture called CycleGAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized attack can easily penetrate ML-based detection systems, including RF (random forest), KNN (k-nearest neighbor), SVM (support vector machine), and naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversarial DDoS attacks. We present a novel DDoS detection framework that incorporates GAN with a symmetrically built generator and discriminator defense system (SDGAN) to deal with these problems. Both symmetric discriminators are intended to simultaneously identify adversarial DDoS traffic. As demonstrated by the experimental results, the suggested SDGAN can be an effective solution against adversarial DDoS attacks. We train SDGAN on adversarial DDoS data generated by CycleGAN and compare it to four previous machine learning-based detection systems. SDGAN outperformed the other machine learning models, with a TPR (true positive rate) of 87.2%, proving its protection ability. Additionally, a more comprehensive test was undertaken to evaluate SDGAN’s capacity to defend against unseen adversarial threats. SDGAN was evaluated using non-training data-generated adversarial traffic. SDGAN remained effective, with a TPR of around 70.9%, compared to RF’s 9.4%. Full article
(This article belongs to the Special Issue Emerging Technologies for Security Applications)
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17 pages, 2044 KiB  
Article
Classification of Firewall Log Data Using Multiclass Machine Learning Models
by Malak Aljabri, Amal A. Alahmadi, Rami Mustafa A. Mohammad, Menna Aboulnour, Dorieh M. Alomari and Sultan H. Almotiri
Electronics 2022, 11(12), 1851; https://doi.org/10.3390/electronics11121851 - 10 Jun 2022
Cited by 15 | Viewed by 4743
Abstract
These days, we are witnessing unprecedented challenges to network security. This indeed confirms that network security has become increasingly important. Firewall logs are important sources of evidence, but they are still difficult to analyze. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning [...] Read more.
These days, we are witnessing unprecedented challenges to network security. This indeed confirms that network security has become increasingly important. Firewall logs are important sources of evidence, but they are still difficult to analyze. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have emerged as effective in developing robust security measures due to the fact that they have the capability to deal with complex cyberattacks in a timely manner. This work aims to tackle the difficulty of analyzing firewall logs using ML and DL by building multiclass ML and DL models that can analyze firewall logs and classify the actions to be taken in response to received sessions as “Allow”, “Drop”, “Deny”, or “Reset-both”. Two sets of empirical evaluations were conducted in order to assess the performance of the produced models. Different features set were used in each set of the empirical evaluation. Further, two extra features, namely, application and category, were proposed to enhance the performance of the proposed models. Several ML and DL algorithms were used for the evaluation purposes, namely, K-Nearest Neighbor (KNN), Naïve Bayas (NB), J48, Random Forest (RF) and Artificial Neural Network (ANN). One interesting reading in the experimental results is that the RF produced the highest accuracy of 99.11% and 99.64% in the first and the second experiments respectively. Yet, all other algorithms have also produced high accuracy rates which confirm that the proposed features played a significant role in improving the firewall classification rate. Full article
(This article belongs to the Special Issue Emerging Technologies for Security Applications)
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