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Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering
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Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 16406

Special Issue Editors

Dr. Hyoungshick Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, College of Computing, Sungkyunkwan University, Seoul 06351, Republic of Korea
Interests: usable security; blockchain; security vulnerability analysis; data-driven security
Special Issues, Collections and Topics in MDPI journals
Dr. Jin B. Hong

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, University of Western Australia, Perth, Australia
Interests: moving target defense; security assessment; dynamic security metrics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ji Won Yoon

E-Mail Website
Guest Editor
School of Cyber Security, Korea University, Seoul 02841, Korea
Interests: signal intelligence; cryptanalysis

Special Issue Information

Dear Colleagues,

Sensing technologies, artificial intelligence (AI), and machine learning have a great impact on human life and industry. Millions of smart devices are connected and communicate to deliver a lot of data related to humans, devices, and communications, but they are at risk of various attacks. Cybersecurity systems engineering is the most significant, urgent task to build a trustworthy and dependable infrastructure for human life and industry.

This Special Issue aims to present various cybersecurity solutions using innovative emerging technologies, including sensing techniques, blockchain, artificial intelligence, and machine learning.

Topics of interest include but are not limited to:

  • Cybersecurity issues in IoT, IIoT, ad hoc and sensor networks;
  • Cybersecurity of robotics and autonomous systems;
  • Blockchain for cybersecurity;
  • Sensor identity protection;
  • Cybersecurity for digital manufacturing;
  • Sensing techniques for secure computing;
  • Artificial intelligence for secure computing;
  • Invasion attacks and defenses against machine learning;
  • Poisoning attacks and defenses against machine learning;
  • Privacy attacks against machine learning;
  • Challenges of machine learning for cyber security;
  • Machine-learning-based threat intelligence;
  • Malware detection and prevention;
  • Autonomous applications for cyber security;
  • Cyber security applications using machine learning;
  • Privacy-preserving machine learning;
  • Decentralized machine learning.

Dr. Hyoungshick Kim
Dr. Jin B. Hong
Prof. Dr. Ji Won Yoon
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. Sensors 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 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.

Published Papers (5 papers)

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Research

29 pages, 1622 KiB  
Article
A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems
by Weiyan Xu, Jack Sun, Rachel Cardell-Oliver, Ajmal Mian and Jin B. Hong
Sensors 2023, 23(10), 4746; https://doi.org/10.3390/s23104746 - 14 May 2023
Cited by 5 | Viewed by 2049
Abstract
Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. [...] Read more.
Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy. Full article
(This article belongs to the Special Issue Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering)
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19 pages, 3810 KiB  
Article
A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach
by Qasem Abu Al-Haija, Manar Alohaly and Ammar Odeh
Sensors 2023, 23(7), 3489; https://doi.org/10.3390/s23073489 - 27 Mar 2023
Cited by 11 | Viewed by 2462
Abstract
The Domain Name System (DNS) protocol essentially translates domain names to IP addresses, enabling browsers to load and utilize Internet resources. Despite its major role, DNS is vulnerable to various security loopholes that attackers have continually abused. Therefore, delivering secure DNS traffic has [...] Read more.
The Domain Name System (DNS) protocol essentially translates domain names to IP addresses, enabling browsers to load and utilize Internet resources. Despite its major role, DNS is vulnerable to various security loopholes that attackers have continually abused. Therefore, delivering secure DNS traffic has become challenging since attackers use advanced and fast malicious information-stealing approaches. To overcome DNS vulnerabilities, the DNS over HTTPS (DoH) protocol was introduced to improve the security of the DNS protocol by encrypting the DNS traffic and communicating it over a covert network channel. This paper proposes a lightweight, double-stage scheme to identify malicious DoH traffic using a hybrid learning approach. The system comprises two layers. At the first layer, the traffic is examined using random fine trees (RF) and identified as DoH traffic or non-DoH traffic. At the second layer, the DoH traffic is further investigated using Adaboost trees (ADT) and identified as benign DoH or malicious DoH. Specifically, the proposed system is lightweight since it works with the least number of features (using only six out of thirty-three features) selected using principal component analysis (PCA) and minimizes the number of samples produced using a random under-sampling (RUS) approach. The experiential evaluation reported a high-performance system with a predictive accuracy of 99.4% and 100% and a predictive overhead of 0.83 µs and 2.27 µs for layer one and layer two, respectively. Hence, the reported results are superior and surpass existing models, given that our proposed model uses only 18% of the feature set and 17% of the sample set, distributed in balanced classes. Full article
(This article belongs to the Special Issue Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering)
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14 pages, 2358 KiB  
Article
Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
by Sang Ho Oh, Min Ki Jeong, Hyung Chan Kim and Jongyoul Park
Sensors 2023, 23(6), 3000; https://doi.org/10.3390/s23063000 - 10 Mar 2023
Viewed by 3845
Abstract
Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has shown great potential in solving [...] Read more.
Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has shown great potential in solving complex decision-making problems in various domains, including cybersecurity. However, there are significant challenges to overcome, such as the lack of sufficient training data and the difficulty of modeling complex and dynamic attack scenarios hindering researchers’ ability to address real-world challenges and advance the state of the art in RL cyber applications. In this work, we applied a deep RL (DRL) framework in adversarial cyber-attack simulation to enhance cybersecurity. Our framework uses an agent-based model to continuously learn from and adapt to the dynamic and uncertain environment of network security. The agent decides on the optimal attack actions to take based on the state of the network and the rewards it receives for its decisions. Our experiments on synthetic network security show that the DRL approach outperforms existing methods in terms of learning optimal attack actions. Our framework represents a promising step towards the development of more effective and dynamic cybersecurity solutions. Full article
(This article belongs to the Special Issue Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering)
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8 pages, 2150 KiB  
Communication
CASPER: Covert Channel Using Internal Speakers
by Hyeongjun Choi, Ji Hyuk Jung and Ji Won Yoon
Sensors 2023, 23(6), 2970; https://doi.org/10.3390/s23062970 - 09 Mar 2023
Viewed by 2216
Abstract
In recent years, researchers have studied various methods for transferring data in a network-separated environment, and the most representative method is the use of inaudible frequency signals like ultrasonic waves. This method has the advantage of being able to transfer data without other [...] Read more.
In recent years, researchers have studied various methods for transferring data in a network-separated environment, and the most representative method is the use of inaudible frequency signals like ultrasonic waves. This method has the advantage of being able to transfer data without other people noticing, but it has the disadvantage that speakers must exist. In a laboratory or company, external speakers may not be attached to each computer. Therefore, this paper presents a new covert channel attack that transfers data using internal speakers on the computer’s motherboard. The internal speaker can also produce a sound of the desired frequency, and, therefore, data can be transferred using high frequency sounds. We encode data into Morse code or binary code and transfer it. Then we record it using a smartphone. At this time, the location of the smartphone can be any distance within 1.5 m when the length per bit is longer than 50 ms, such as on the computer body or on the desk. Data are obtained by analyzing the recorded file. Our results show that data is transferred from a network-separated computer using an internal speaker with 20 bits/s in maximum. Full article
(This article belongs to the Special Issue Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering)
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21 pages, 871 KiB  
Article
A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems
by Bedeuro Kim, Mohsen Ali Alawami, Eunsoo Kim, Sanghak Oh, Jeongyong Park and Hyoungshick Kim
Sensors 2023, 23(3), 1310; https://doi.org/10.3390/s23031310 - 23 Jan 2023
Cited by 18 | Viewed by 5127
Abstract
Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to [...] Read more.
Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real-world situation. In other words, there still needs to be a comprehensive comparison of state-of-the-art anomaly detection models with common experimental configurations. To address this problem, we conduct a comparative study of five representative time series anomaly detection models: InterFusion, RANSynCoder, GDN, LSTM-ED, and USAD. We specifically compare the performance analysis of the models in detection accuracy, training, and testing times with two publicly available datasets: SWaT and HAI. The experimental results show that the best model results are inconsistent with the datasets. For SWaT, InterFusion achieves the highest F1-score of 90.7% while RANSynCoder achieves the highest F1-score of 82.9% for HAI. We also investigate the effects of the training set size on the performance of anomaly detection models. We found that about 40% of the entire training set would be sufficient to build a model producing a similar performance compared to using the entire training set. Full article
(This article belongs to the Special Issue Sensing Techniques and Artificial Intelligence in Cybersecurity Systems Engineering)
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