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 June 2023) | Viewed by 14825

Special Issue Editors

Division of Information Technology & Management, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: information security; data security; privacy protection; applied cryptography
Special Issues, Collections and Topics in MDPI journals
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,

Advances in artificial intelligence and data mining technologies have led to a demand for more diverse data sharing. This is required not only to produce more accurate and effective artificial intelligence models using more data but also to verify and improve various data analysis and machine learning methods proposed in both academia and industry.

Unfortunately, data generally belong to different security domains, so there is difficulty in sharing them easily. In addition, if there is privacy-related information in the data, and thus sharing and federation become more difficult.

This Special Issue aims to bring together researchers and practitioners to discuss various aspects of cryptography for secure and private data science.

Topics of interest include, but are not limited to:

  • Private AI using crytographic techniques;
  • Cryptography for secure data federation;
  • Cryptographic tools for thwarting attacks on large-scale data sharing;
  • Cryptography for secure cloud data storage and deduplication;
  • Cryptography for lightweight data processing;
  • Cryptography for large-scale databases;
  • Fraud Detection Methods and Use cases in Internet services (Internet banking, Online game, Crypto currency, etc.);
  • Malware Detection by Data-driven analysis;
  • Intrusion Detection in Cyber physical systems (Drones, Connected Autonomous Vehicles) by Data-driven analysis.

Prof. Dr. Younho Lee
Prof. Dr. Huy Kang Kim
Guest Editors

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Keywords

  • private AI
  • data-driven security
  • cryptography

Published Papers (9 papers)

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Research

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22 pages, 669 KiB  
Article
Defense Mechanism to Generate IPS Rules from Honeypot Logs and Its Application to Log4Shell Attack and Its Variants
by Yudai Yamamoto and Shingo Yamaguchi
Electronics 2023, 12(14), 3177; https://doi.org/10.3390/electronics12143177 - 21 Jul 2023
Cited by 1 | Viewed by 915
Abstract
The vulnerability of Apache Log4j, Log4Shell, is known for its widespread impact; many attacks that exploit Log4Shell use obfuscated attack patterns, and Log4Shell has revealed the importance of addressing such variants. However, there is no research which focuses on the response to variants. [...] Read more.
The vulnerability of Apache Log4j, Log4Shell, is known for its widespread impact; many attacks that exploit Log4Shell use obfuscated attack patterns, and Log4Shell has revealed the importance of addressing such variants. However, there is no research which focuses on the response to variants. In this paper, we propose a defense system that can protect against variants as well as known attacks. The proposed defense system can be divided into three parts: honeypots, machine learning, and rule generation. Honeypots are used to collect data, which can be used to obtain information about the latest attacks. In machine learning, the data collected by honeypots are used to determine whether it is an attack or not. It generates rules that can be applied to an IPS (Intrusion Prevention System) to block access that is determined to be an attack. To investigate the effectiveness of this system, an experiment was conducted using test data collected by honeypots, with the conventional method using Suricata, an IPS, as a comparison. Experimental results show that the discrimination performance of the proposed method against variant attacks is about 50% higher than that of the conventional method, indicating that the proposed method is an effective method against variant attacks. Full article
(This article belongs to the Special Issue Data Driven Security)
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29 pages, 9062 KiB  
Article
A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation
by Vitalijus Gurčinas, Juozas Dautartas, Justinas Janulevičius, Nikolaj Goranin and Antanas Čenys
Electronics 2023, 12(13), 2894; https://doi.org/10.3390/electronics12132894 - 30 Jun 2023
Viewed by 1235
Abstract
Investigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks [...] Read more.
Investigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks have been an issue due to the limitations of hardware and software up until recently. This paper presents comprehensive research on keystroke-injection payload generation that proposes the use of deep learning to bypass the security of keystroke-based authentication systems focusing on both fixed-text and free-text scenarios. In addition, it specifies the potential risks associated with keystroke-injection attacks. To ensure the legitimacy of the investigation, a model is proposed and implemented within this context. The results of the implemented implant model inside the keyboard indicate that deep learning can significantly improve the accuracy of keystroke dynamics recognition as well as help to generate effective payload from a locally collected dataset. The results demonstrate favorable accuracy rates, with reported performance of 93–96% for fixed-text scenarios and 75–92% for free-text. Accuracy across different text scenarios was achieved using a small dataset collected with the proposed implant model. This dataset enabled the generation of synthetic keystrokes directly within a low-computation-power device. This approach offers efficient and almost real-time keystroke replication. The results obtained show that the proposed model is sufficient not only to bypass the fixed-text keystroke dynamics system, but also to remotely control the victim’s device at the appropriate time. However, such a method poses high security risks when deploying adaptive keystroke injection with impersonated payload in real-world scenarios. Full article
(This article belongs to the Special Issue Data Driven Security)
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12 pages, 4956 KiB  
Article
Enhancing Low-Pass Filtering Detection on Small Digital Images Using Hybrid Deep Learning
by Saurabh Agarwal and Ki-Hyun Jung
Electronics 2023, 12(12), 2637; https://doi.org/10.3390/electronics12122637 - 12 Jun 2023
Viewed by 916
Abstract
Detecting image manipulation is essential for investigating the processing history of digital images. In this paper, a novel scheme is proposed to detect the use of low-pass filters in image processing. A new convolutional neural network with a reasonable size was designed to [...] Read more.
Detecting image manipulation is essential for investigating the processing history of digital images. In this paper, a novel scheme is proposed to detect the use of low-pass filters in image processing. A new convolutional neural network with a reasonable size was designed to identify three types of low-pass filters. The learning experiences of the three solvers were combined to enhance the detection ability of the proposed approach. Global pooling layers were employed to protect the information loss between the convolutional layers, and a new global variance pooling layer was introduced to improve detection accuracy. The extracted features from the convolutional neural network were mapped to the frequency domain to enrich the feature set. A leaky Rectified Linear Unit (ReLU) layer was discovered to perform better than the traditional ReLU layer. A tri-layered neural network classifier was employed to classify low-pass filters with various parameters into two, four, and ten classes. As detecting low-pass filtering is relatively easy on large-dimension images, the experimental environment was restricted to small images of 30 × 30 and 60 × 60 pixels. The proposed scheme achieved 80.12% and 90.65% detection accuracy on ten categories of images compressed with JPEG and a quality factor 75 on 30 × 30 and 60 × 60 images, respectively. Full article
(This article belongs to the Special Issue Data Driven Security)
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13 pages, 1370 KiB  
Article
Balancing Password Security and User Convenience: Exploring the Potential of Prompt Models for Password Generation
by Afamefuna P. Umejiaku, Prastab Dhakal and Victor S. Sheng
Electronics 2023, 12(10), 2159; https://doi.org/10.3390/electronics12102159 - 09 May 2023
Cited by 1 | Viewed by 2836
Abstract
With the increasing prevalence of cyber attacks and data breaches, the importance of strong passwords cannot be overstated. Password generating software has been widely used to generate complex passwords that are difficult to crack, but it has its limitations. One of the main [...] Read more.
With the increasing prevalence of cyber attacks and data breaches, the importance of strong passwords cannot be overstated. Password generating software has been widely used to generate complex passwords that are difficult to crack, but it has its limitations. One of the main problems with this kind of software is that it often generates passwords that are difficult to remember, leading to users write them down or reuse them across multiple accounts. In recent years, prompt models such as ChatGPT have emerged as a promising solution for generating strong and memorable passwords. By leveraging machine learning algorithms, these models can generate unique and complex passwords tailored to individual users’ preferences, making them easier to remember and more secure. However, the use of prompt models to generate passwords also raises concerns about exposing vulnerable passwords. Hackers can potentially use these models to predict passwords by analyzing a user’s online activity and personal data. Additionally, the constant need to change passwords to stay secure poses a challenge for both password generating software and prompt models. As technology continues to evolve, finding a balance between password security and user convenience remains a complex issue. While prompt models such as ChatGPT can offer a promising solution, it is essential to consider the potential risks and challenges associated with their use, including the constant need for password changes and the potential vulnerability of the generated passwords. Full article
(This article belongs to the Special Issue Data Driven Security)
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17 pages, 483 KiB  
Article
Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
by Mahsa Mozaffari, Keval Doshi and Yasin Yilmaz
Electronics 2023, 12(9), 1971; https://doi.org/10.3390/electronics12091971 - 24 Apr 2023
Cited by 4 | Viewed by 1809
Abstract
In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose [...] Read more.
In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. Full article
(This article belongs to the Special Issue Data Driven Security)
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8 pages, 286 KiB  
Article
Approximating Max Function in Fully Homomorphic Encryption
by Hyunjun Lee, Jina Choi and Younho Lee
Electronics 2023, 12(7), 1724; https://doi.org/10.3390/electronics12071724 - 04 Apr 2023
Viewed by 1266
Abstract
This study focuses on efficiently finding the location of the maximum value for large-scale values encrypted by the CKKS (Cheon—Kim—Kim–Song) method. To find the maximum value, logM+1 comparison operations and logM rotation operations, and 2logM+3 [...] Read more.
This study focuses on efficiently finding the location of the maximum value for large-scale values encrypted by the CKKS (Cheon—Kim—Kim–Song) method. To find the maximum value, logM+1 comparison operations and logM rotation operations, and 2logM+3 additions and 2logM+1 multiplications are required. However, there is no known way to find a k-approximate maximum value, i.e., a value with the same most significant k-bits as the maximum value. In this study, when the value range of all data in each slot in the ciphertext is [0, 1], we propose a method for finding all slot positions of values whose most significant k-bits match the maximum value. The proposed method can find all slots from the input ciphertexts where their values have the same most significant k-bits as the maximum value by performing 2k comparison operations, (4k+2) multiplications, (6k+2klogM+3) additions, and 2klogM rotation operations. Through experiments and complexity analysis, we show that the proposed method is more efficient than the existing method of finding all locations where the k MSB is equal to the maximum value. The result of this can be applied to various privacy-preserving applications in various environments, such as IoT devices. Full article
(This article belongs to the Special Issue Data Driven Security)
25 pages, 2240 KiB  
Article
Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts
by Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee and Dongho Won
Electronics 2023, 12(5), 1217; https://doi.org/10.3390/electronics12051217 - 03 Mar 2023
Cited by 2 | Viewed by 1075
Abstract
Federated authentication, such as Google ID, enables users to conveniently access multiple websites using a single login credential. Despite this convenience, securing federated authentication services requires addressing a single point of failure, which can result from using a centralized authentication server. In addition, [...] Read more.
Federated authentication, such as Google ID, enables users to conveniently access multiple websites using a single login credential. Despite this convenience, securing federated authentication services requires addressing a single point of failure, which can result from using a centralized authentication server. In addition, because the same login credentials are used, anonymity and protection against user impersonation attacks must be ensured. Recently, researchers introduced distributed authentication schemes based on blockchains and smart contracts (SCs) for systems that require high availability and reliability. Data on a blockchain are immutable, and deployed SCs cannot be changed or tampered with. Nonetheless, updates may be necessary to fix programming bugs or modify business logic. Recently, methods for updating SCs to address these issues have been investigated. Therefore, this study proposes a distributed and federated authentication scheme that uses SCs to overcome a single point of failure. Additionally, an updatable SC is designed to fix programming bugs, add to the function of an SC, or modify business logic. ProVerif, which is a widely known cryptographic protocol verification tool, confirms that the proposed scheme can provide protection against various security threats, such as single point of failure, user impersonation attacks, and user anonymity, which is vital in federated authentication services. In addition, the proposed scheme exhibits a performance improvement of 71% compared with other related schemes. Full article
(This article belongs to the Special Issue Data Driven Security)
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24 pages, 4930 KiB  
Article
A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain
by Ali Aljofey, Abdur Rasool, Qingshan Jiang and Qiang Qu
Electronics 2022, 11(18), 2937; https://doi.org/10.3390/electronics11182937 - 16 Sep 2022
Cited by 10 | Viewed by 1860
Abstract
Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of [...] Read more.
Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics. Full article
(This article belongs to the Special Issue Data Driven Security)
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Review

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23 pages, 526 KiB  
Review
Applications and Technologies of Big Data in the Aerospace Domain
by Evgenia Adamopoulou and Emmanouil Daskalakis
Electronics 2023, 12(10), 2225; https://doi.org/10.3390/electronics12102225 - 13 May 2023
Cited by 1 | Viewed by 2158
Abstract
Over the last few years, Big Data applications have attracted ever-increasing attention in several scientific and business domains. Biomedicine, transportation, entertainment, and aerospace are only a few examples of sectors which are increasingly dependent on applications, where knowledge is extracted from huge volumes [...] Read more.
Over the last few years, Big Data applications have attracted ever-increasing attention in several scientific and business domains. Biomedicine, transportation, entertainment, and aerospace are only a few examples of sectors which are increasingly dependent on applications, where knowledge is extracted from huge volumes of heterogeneous data. The main goal of this paper was to conduct an academic literature review of prominent publications revolving around the application of BD in aerospace. A total of 67 publications were analyzed, highlighting the sources, uses, and benefits of BD. For categorizing the publications, a novel 6-fold approach was introduced including applications in aviation technology and aviation management, UAV-enabled applications, applications in military aviation, health/environment-related applications, and applications in space technology. Aiming to provide the reader with a clear overview of the existing solutions, a total of 15 subcategories were also utilized. The results indicated numerous benefits deriving from the application of BD in aerospace. These benefits referred to the aerospace domain itself as well as to many other sectors including healthcare, environment, humanitarian operations, network communications, etc. Various data sources and different Machine Learning models were utilized in the analyzed publications and the use of BD-based techniques enabled us to extract useful correlations and gain useful insights from large volumes of data. Full article
(This article belongs to the Special Issue Data Driven Security)
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