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Electronics
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Data-Driven Security
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Data-Driven Security

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 25801

Special Issue Editor

Prof. Dr. Huy Kang Kim

E-Mail Website
Guest Editor
1. Graduate School of Information Security (Graduate School), Korea University, Seoul, Republic of Korea
2. Department of Cyber Defense (Undergraduate School), Korea University, Seoul, Republic of Korea
Interests: automobile security; network and system security; online game security; fraud detection system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, big data are of great importance across all industries, such as marketing, manufacturing, smart cities, or healthcare, and the cybersecurity field is not an exception. A data-driven approach enables us to make an automated security system using abundant data. The system may analyze network traffic to prevent intrusion, detect fraud in transaction data, discover fake news distributed in social networking services, etc. Recent rapid advances in AI technology have the potential to improve accuracy, efficiency, and robustness of data-based cyber security systems. Such state-of-the-art technologies should be actively applied to deal with ever-increasing cyberthreats.

This Special Issue aims to cover the most recent data-driven security techniques in various fields, such as network, cyberphysical systems (CPS), payment, cryptocurrency, internet game, social media, etc. Both academic research and practical deployment of data-driven security systems are welcomed. The scope of this Special Issue includes but is not limited to the following keywords:

  • Data-driven network security;
  • Data-driven malware analysis;
  • Data-driven CPS security (power plant, water grid, automobile, drone, etc.);
  • Data-driven abnormal behavior detection in payment (fraud detection system in banking, mobile payment);
  • Data-driven abnormal behavior detection in cryptocurrency (fraud detection in block-chain, crypto-currency transaction, etc.);
  • Data-driven abnormal behavior detection in internet game (mobile and PC-based online game such as FPS, MMORPG, etc.);
  • Data-driven mis/dis/false information detection (fake news detection, propaganda detection, intentional opinion manipulation attempt detection, etc.).

Prof. Dr. Huy Kang Kim
Guest Editor

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.

Published Papers (6 papers)

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Research

17 pages, 2290 KiB  
Article
Image Forgery Detection Using Deep Learning by Recompressing Images
by Syed Sadaf Ali, Iyyakutti Iyappan Ganapathi, Ngoc-Son Vu, Syed Danish Ali, Neetesh Saxena and Naoufel Werghi
Electronics 2022, 11(3), 403; https://doi.org/10.3390/electronics11030403 - 28 Jan 2022
Cited by 38 | Viewed by 12884
Abstract
Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A [...] Read more.
Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%. Full article
(This article belongs to the Special Issue Data-Driven Security)
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18 pages, 519 KiB  
Article
SpotFuzz: Fuzzing Based on Program Hot-Spots
by Haibo Pang, Jie Jian, Yan Zhuang, Yingyun Ye and Zhanbo Li
Electronics 2021, 10(24), 3142; https://doi.org/10.3390/electronics10243142 - 17 Dec 2021
Cited by 1 | Viewed by 2679
Abstract
AFL is the most widely used coverage-guided fuzzer, which relies on rough execution information to assign seeds energy, which can lead to waste. We track the program executed by AFL and discover that the hit counts of each edge might vary greatly when [...] Read more.
AFL is the most widely used coverage-guided fuzzer, which relies on rough execution information to assign seeds energy, which can lead to waste. We track the program executed by AFL and discover that the hit counts of each edge might vary greatly when using different seeds as inputs. Some seeds, which are continuously given too much energy, experience very high hit counts of several edges without new crashes or edges being explored, which results in invalid execution and waste of performance. We also define time-consuming edges and discover that they only occupy a small part of the program. In this paper, we define invalid execution edges and time-consuming edges as hot-spots and propose a fuzzing solution SpotFuzz to solve energy waste caused by the above hot-spot phenomenon. It allocates seeds with more hot-spots during execution and uses less energy to reduce energy waste. Moreover, it preferentially selects seeds with less time-consuming edges as test cases, allowing for more edges to be explored in a limited time. We implement an SpotFuzz prototype based on AFL and test it on several real programs for 600 CPU days. The experimental results show that minimizing the invalid and time-consuming execution of edges can improve the fuzzing efficiency. On average, SpotFuzz could find 42.96% more unique crashes and 14.25% more edges than AFL on GNU Binutils and tcpdump. Full article
(This article belongs to the Special Issue Data-Driven Security)
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19 pages, 2207 KiB  
Article
CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method
by Cheongmin Ji, Taehyoung Ko and Manpyo Hong
Electronics 2021, 10(19), 2442; https://doi.org/10.3390/electronics10192442 - 08 Oct 2021
Cited by 1 | Viewed by 1581
Abstract
In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control [...] Read more.
In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods. Full article
(This article belongs to the Special Issue Data-Driven Security)
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18 pages, 2170 KiB  
Article
A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
by Neeraj Chugh, Geetam Singh Tomar, Robin Singh Bhadoria and Neetesh Saxena
Electronics 2021, 10(14), 1635; https://doi.org/10.3390/electronics10141635 - 09 Jul 2021
Cited by 9 | Viewed by 1850
Abstract
To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources [...] Read more.
To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources. Full article
(This article belongs to the Special Issue Data-Driven Security)
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21 pages, 793 KiB  
Article
PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data
by Ilok Jung, Jongin Lim and Huy Kang Kim
Electronics 2021, 10(10), 1148; https://doi.org/10.3390/electronics10101148 - 12 May 2021
Cited by 6 | Viewed by 3033
Abstract
The number of studies on applying machine learning to cyber security has increased over the past few years. These studies, however, are facing difficulties with making themselves usable in the real world, mainly due to the lack of training data and reusability of [...] Read more.
The number of studies on applying machine learning to cyber security has increased over the past few years. These studies, however, are facing difficulties with making themselves usable in the real world, mainly due to the lack of training data and reusability of a created model. While transfer learning seems like a solution to these problems, the number of studies in the field of intrusion detection is still insufficient. Therefore, this study proposes payload feature-based transfer learning as a solution to the lack of training data when applying machine learning to intrusion detection by using the knowledge from an already known domain. Firstly, it expands the extracting range of information from header to payload to accurately deliver the information by using an effective hybrid feature extraction method. Secondly, this study provides an improved optimization method for the extracted features to create a labeled dataset for a target domain. This proposal was validated on publicly available datasets, using three distinctive scenarios, and the results confirmed its usability in practice by increasing the accuracy of the training data created from the transfer learning by 30%, compared to that of the non-transfer learning method. In addition, we showed that this approach can help in identifying previously unknown attacks and reusing models from different domains. Full article
(This article belongs to the Special Issue Data-Driven Security)
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23 pages, 1003 KiB  
Article
MaxAFL: Maximizing Code Coverage with a Gradient-Based Optimization Technique
by Youngjoon Kim and Jiwon Yoon
Electronics 2021, 10(1), 11; https://doi.org/10.3390/electronics10010011 - 24 Dec 2020
Cited by 3 | Viewed by 2748
Abstract
Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. [...] Read more.
Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. The concolic execution fuzzers also have limitations in scalability. Recently, the gradient-based fuzzers that use a gradient to mutate inputs have been introduced. Gradient-based fuzzers can be applied to real-world programs and achieve high code coverage. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. In this paper, we propose a new type of gradient-based fuzzer, MaxAFL, to overcome the limitations of existing gradient-based fuzzers. Our approach constructs an objective function through fine-grained static analysis. After constructing a well-made objective function, we can apply the gradient-based optimization algorithm. We use a modified gradient-descent algorithm to minimize our objective function and propose some probabilistic techniques to escape local optimum. We introduce an adaptive objective function which aims to explore various paths in the program. We implemented MaxAFL based on the original AFL. MaxAFL achieved increase of code coverage per time compared with three other fuzzers in six open-source Linux binaries. We also measured cumulative code coverage per total execution, and MaxAFL outperformed the other fuzzers in this metric. Finally, MaxAFL can also find more bugs than the other fuzzers. Full article
(This article belongs to the Special Issue Data-Driven Security)
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