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Electronics
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Artificial Intelligence in Cyberspace Security
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Artificial Intelligence in Cyberspace Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 15211

Special Issue Editors

Dr. Yuanzhang Li

E-Mail Website
Guest Editor
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: AI security; data security; software protection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu-an Tan

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: network security; secure data sharing; AI security
Special Issues, Collections and Topics in MDPI journals
Dr. Chen Liang

E-Mail Website
Guest Editor
School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China
Interests: data security; secure data sharing; steganography
Dr. Hongfei Zhu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: network security; blockchain security; cryptographic protocol

Special Issue Information

Dear Colleagues,

AI (Artificial Intelligence) has made progress in many scientific and technological fields, such as unmanned system, pattern recognition, natural language processes, expert systems, etc.  Many researchers insist that the technological singularity may soon occur when machines may acquire intelligence similar to or exceeding that of humans. While many researchers believe that a technological singularity is an arm's length away, many argue against the same possibility due to a lack of concrete evidence. The rapid development of AI not only facilitates people's daily life and work, but also introduces new cyberspace security challenges. AI security has become a hot topic and has attracted wide attention from scholars.

Security issues keep emerging since AI technology was introduced into cyberspace. On the one hand, the explosive growth of data has made it impractical to manually manage network data security, and the threat of new and rapidly iterative network attack methods are becoming increasingly common. On the other hand, existing network attacks have gradually adapted to various existing defense technologies in terms of transmission, infection, and evasion, and are constantly iteratively emerging new variants, making them increasingly difficult to detect and predict.To ensure the security of new information technologies in scenarios such as smart lives, smart cities, smart networks, etc., and to promote and enhance the development of network security, we have organized a Special Issue topic on "Artificial Intelligence in Cyberspace Security". In this Special Issue, the new generation of network attack and defense technology, new secure cryptographic algorithms, data security and privacy protection technology, network and communication security protocols, security analyses, and the evaluation of new application scenarios are discussed. We call for papers in this Special Issue to provide a platform to discuss, exchange insights, and share experiences among researchers, industry specialists, and application developers.

Authors are invited to submit original research on both theoretical and practical aspects of security and privacy in networks. We especially welcome submissions that present implementation and deployment results. Topics of interest for submission include, but are not limited to:

  • AI for cyberspace security and safety;
  • AI-driven communication network security and privacy protection;
  • adversarial machine learning;
  • applications of AI for cyberspace security and safety;
  • attack and defense methods with adversarial examples;
  • big data security;
  • cyber physical systems security;
  • cyberspace security and safety for IoT;
  • database security;
  • digital forensics;
  • explainable machine learning for cyberspace security and safety;
  • firmware security;
  • human machine intelligence for cyberspace security and safety;
  • malware and botnet;
  • network-intrusion detection and safety;
  • operation system security;
  • privacy and data protection;
  • public-key techniques in MPC or other protocols;
  • secure AI modeling and architecture;
  • secure and resilient communication and control architecture;
  • secure data provenance;
  • secure data sharing, digital signature, and multi-party secure computing;
  • security issues of federated learning;
  • security protocols for AI;
  • self-healing for cyberspace security and safety;
  • social networking security and privacy;
  • software security;
  • trust computing;
  • trust management and safety;
  • vulnerability and risk assessment;
  • Web security

Dr. Yuanzhang Li
Prof. Dr. Yu-an Tan
Dr. Chen Liang
Dr. Hongfei Zhu
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

  • AI-driven network security
  • AI-driven privacy protection
  • adversarial machine learning
  • cyberspace security
  • malware detection
  • secure communication
  • secure data sharing
  • security protocol
  • software security
  • vulnerability

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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.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

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Research

22 pages, 872 KiB  
Article
Effective ML-Based Android Malware Detection and Categorization
by Areej Alhogail and Rawan Abdulaziz Alharbi
Electronics 2025, 14(8), 1486; https://doi.org/10.3390/electronics14081486 - 8 Apr 2025
Viewed by 567
Abstract
The rapid proliferation of malware poses a significant challenge regarding digital security, necessitating the development of advanced techniques for malware detection and categorization. In this study, we investigate Android malware detection and categorization using a two-step machine learning (ML) framework combined with feature [...] Read more.
The rapid proliferation of malware poses a significant challenge regarding digital security, necessitating the development of advanced techniques for malware detection and categorization. In this study, we investigate Android malware detection and categorization using a two-step machine learning (ML) framework combined with feature engineering. The proposed framework first performs binary categorization to detect malware and then applies multi-class categorization to categorize malware into types, such as adware, banking Trojans, SMS malware, and riskware. Feature selection techniques such as chi-squared testing and select-from-model (SFM) were employed to reduce dimensionality and enhance model performance. Various ML classifiers were evaluated, and the proposed model achieved outstanding accuracy, at 97.82% for malware detection and 96.09% for malware categorization. The proposed framework outperforms existing approaches, demonstrating the effectiveness of feature engineering and random forest (RF) models in addressing computational efficiency. This research contributes a robust and interpretable framework for Android malware detection that is resource-efficient and practical for use in real-world applications. It also offers a scalable approach via which practitioners can deploy efficient malware detection systems. Future work will focus on real-time implementation and adaptive methodologies to address evolving malware threats. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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13 pages, 288 KiB  
Article
Adversarial Sample Generation Based on Model Simulation Analysis in Intrusion Detection Systems
by Jiankang Sun and Shujie Yang
Electronics 2025, 14(5), 870; https://doi.org/10.3390/electronics14050870 - 22 Feb 2025
Viewed by 549
Abstract
The explosive development of artificial intelligence technology is profoundly affecting the strategic landscape of cyberspace security, demonstrating enormous potential in the field of intrusion detection. Recent research has found that machine learning models have serious vulnerabilities, and adversarial samples derived from this vulnerability [...] Read more.
The explosive development of artificial intelligence technology is profoundly affecting the strategic landscape of cyberspace security, demonstrating enormous potential in the field of intrusion detection. Recent research has found that machine learning models have serious vulnerabilities, and adversarial samples derived from this vulnerability can significantly reduce the accuracy of model detection by adding slight perturbations to the original samples. In our article, we propose a novel method called adversarial sample generation based on model simulation that quickly generates adversarial samples in a short period of time and improves the model’s generalization and robustness after adversarial training. Extensive experiments on the CICIDS-2017 dataset demonstrated that the method consistently outperforms other current research methods. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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24 pages, 3385 KiB  
Article
An Improved Binary Simulated Annealing Algorithm and TPE-FL-LightGBM for Fast Network Intrusion Detection
by Yafei Luo, Ruihan Chen, Chuantao Li, Derong Yang, Kun Tang and Jing Su
Electronics 2025, 14(2), 231; https://doi.org/10.3390/electronics14020231 - 8 Jan 2025
Viewed by 705
Abstract
With the rapid proliferation of the Internet, network security issues that threaten users have become increasingly severe, despite the widespread benefits of Internet access. Most existing intrusion detection systems (IDS) suffer from suboptimal performance due to data imbalance and feature redundancy, while also [...] Read more.
With the rapid proliferation of the Internet, network security issues that threaten users have become increasingly severe, despite the widespread benefits of Internet access. Most existing intrusion detection systems (IDS) suffer from suboptimal performance due to data imbalance and feature redundancy, while also facing high computational complexity in areas such as feature selection and optimization. To address these challenges, this study proposes a novel network intrusion detection method based on an improved binary simulated annealing algorithm (IBSA) and TPE-FL-LightGBM. First, by integrating Focal Loss into the loss function of the LightGBM classifier, we introduce cost-sensitive learning, which effectively mitigates the impact of class imbalance on model performance and enhances the model’s ability to learn difficult-to-classify samples. Next, significant improvements are made to the simulated annealing algorithm, including adaptive adjustments of the initial temperature and Metropolis criterion, the incorporation of multi-neighborhood search strategies, and the integration of an S-shaped transfer function. These improvements enable the IBSA method to achieve efficient optimal feature selection with fewer iterations. Finally, the Tree-structured Parzen Estimator (TPE) algorithm is employed to optimize the structure of the FL-LightGBM classifier, further enhancing its performance. Through comprehensive visual analysis, ablation studies, and comparative experiments on the NSL-KDD and UNSW-NB15 datasets, the reliability of the proposed network intrusion detection method is validated. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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29 pages, 2674 KiB  
Article
Intrusion Detection System Based on Multi-Level Feature Extraction and Inductive Network
by Junyi Mao, Xiaoyu Yang, Bo Hu, Yizhen Lu and Guangqiang Yin
Electronics 2025, 14(1), 189; https://doi.org/10.3390/electronics14010189 - 5 Jan 2025
Cited by 1 | Viewed by 1104
Abstract
With the rapid development of the internet, network security threats are becoming increasingly complex and diverse, making traditional intrusion detection systems (IDSs) inadequate for handling the growing variety of sophisticated attacks. In particular, traditional methods based on rule matching and manual feature extraction [...] Read more.
With the rapid development of the internet, network security threats are becoming increasingly complex and diverse, making traditional intrusion detection systems (IDSs) inadequate for handling the growing variety of sophisticated attacks. In particular, traditional methods based on rule matching and manual feature extraction demonstrate significant limitations in dealing with small samples and unknown attacks. This paper proposes an intrusion detection system based on multi-level feature extraction and inductive learning (MFEI-IDS) to address these challenges. The model innovatively integrates Fully Convolutional Networks (FCNs) with the Transformer architecture (FCN–Transformer) for feature extraction and utilizes an inductive learning component for efficient classification. The FCN–Transformer Encoder extracts multi-level features from raw network traffic, capturing local spatial patterns and global temporal dependencies, significantly enhancing the representation of network traffic while reducing reliance on manual feature engineering. The inductive learning module employs a dynamic routing mechanism to map sample feature vectors into robust class vector representations, achieving superior generalization when detecting unseen attack types. Compared to existing FCN–Transformer models, MFEI-IDS incorporates inductive learning to handle data imbalance and small-sample scenarios. Experiments on ISCX 2012 and CIC-IDS 2017 datasets show that MFEI-IDS outperforms mainstream IDS methods in accuracy, precision, recall, and F1-score, excelling in cross-dataset validation and demonstrating strong generalization capabilities. These results validate the practical potential of MFEI-IDS in small-sample learning, unknown attack detection, and dynamic network environments. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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15 pages, 4070 KiB  
Article
Hierarchical Security Authentication with Attention-Enhanced Convolutional Network for Internet of Things
by Xiaoying Qiu, Guangxu Zhao, Jinwei Yu, Wenbao Jiang, Zhaozhong Guo and Maozhi Xu
Electronics 2024, 13(23), 4699; https://doi.org/10.3390/electronics13234699 - 28 Nov 2024
Viewed by 911
Abstract
As security authentication issues continue to arise in future wireless communication networks, researchers are working hard to further improve authentication techniques. Recently, physical layer authentication (PLA) has received widespread attention for its lightweight nature compared to traditional encryption methods based on keys and [...] Read more.
As security authentication issues continue to arise in future wireless communication networks, researchers are working hard to further improve authentication techniques. Recently, physical layer authentication (PLA) has received widespread attention for its lightweight nature compared to traditional encryption methods based on keys and blockchain. However, the existing PLA mechanisms based on a fixed decision threshold have low reliability in dynamic environments. Moreover, PLA solutions are typically based on binary authentication, and these binary-type schemes cannot provide different levels of access control. To address these challenges, this article introduces the concept of hierarchical security authentication, aiming to achieve multi-level secure authorization access. In order to further improve the accuracy of identity verification, we design an Attention-Enhanced Convolutional Network (AECN) model that integrates the attention mechanism. Specifically, by introducing a confidence score branch, the proposed AECN-based PLA scheme completes authentication without a threshold, thus avoiding the issues stemming from inappropriate threshold settings in conventional PLA schemes. The simulation results show that our proposed AECN framework outperforms existing algorithms at different levels of security authentication. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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15 pages, 3119 KiB  
Article
Deep Learning for Cybersecurity Classification: Utilizing Depth-Wise CNN and Attention Mechanism on VM-Obfuscated Data
by Sicheng Han, Heeheon Yun and Yongsu Park
Electronics 2024, 13(17), 3393; https://doi.org/10.3390/electronics13173393 - 26 Aug 2024
Cited by 3 | Viewed by 1680
Abstract
With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks [...] Read more.
With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks (CNNs) combined with a spatial attention mechanism to tackle VM-protected cybersecurity datasets. To address the scarcity of obfuscated malware samples, the dataset was generated using VMProtect to ensure the models were trained on real examples of modern obfuscated malware. The effectiveness of our approach is demonstrated through extensive experiments on both regular malware and obfuscated malware, where our model achieved accuracies of nearly 100% and 93.55% in classifying the regular malware and the obfuscated malware, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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17 pages, 5137 KiB  
Article
Research on the Teaching of Laser Chaotic Communication Based on Optisystem and Matlab Software
by Xuefang Zhou, Minjun Li, Meihua Bi, Guowei Yang and Miao Hu
Electronics 2024, 13(16), 3274; https://doi.org/10.3390/electronics13163274 - 18 Aug 2024
Viewed by 1513
Abstract
The utilization of chaotic optical communication, a physical layer security technology, has the potential to enhance the security of optical fiber networks. In this paper, we take knowledge acquired while teaching “A chaotic security system based on phase-intensity (P-I) electro-optic feedback” as an [...] Read more.
The utilization of chaotic optical communication, a physical layer security technology, has the potential to enhance the security of optical fiber networks. In this paper, we take knowledge acquired while teaching “A chaotic security system based on phase-intensity (P-I) electro-optic feedback” as an example and, in detail, introduce a teaching implementation process based on the combination of Optisystem and Matlab. Firstly, based on the Lang–Kobayashi (L-K) laser equation, the generation mechanism of electro-optic feedback chaos was explained. Secondly, the P-I electro-optic feedback chaos was analyzed theoretically with the help of Matlab. Finally, a laser chaotic optical communication system based on electro-optic feedback was built with the help of Optisystem (15.0.0) software, and the performance of the communication was simulated and analyzed through the design of system parameters. The teaching design model and facilitate the concretization of the abstract theory of “the principle of chaos generated by electro-optic feedback, the composition of chaotic optical communication system and the performance index of chaotic communication system”. Through after-class exercises and questionnaire surveys, it was verified that the teaching method is widely recognized by students and that it effectively improves the teaching effect of the course of laser chaotic communication and the students’ academic research ability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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23 pages, 1334 KiB  
Article
A Secure Data-Sharing Model Resisting Keyword Guessing Attacks in Edge–Cloud Collaboration Scenarios
by Ye Li, Mengen Xiong, Junling Yuan, Qikun Zhang and Hongfei Zhu
Electronics 2024, 13(16), 3236; https://doi.org/10.3390/electronics13163236 - 15 Aug 2024
Viewed by 1118
Abstract
In edge–cloud collaboration scenarios, data sharing is a critical technological tool, yet smart devices encounter significant challenges in ensuring data-sharing security. Attribute-based keyword search (ABKS) is employed in these contexts to facilitate fine-grained access control over shared data, allowing only users with the [...] Read more.
In edge–cloud collaboration scenarios, data sharing is a critical technological tool, yet smart devices encounter significant challenges in ensuring data-sharing security. Attribute-based keyword search (ABKS) is employed in these contexts to facilitate fine-grained access control over shared data, allowing only users with the necessary privileges to retrieve keywords. The implementation of secure data sharing is threatened since most of the current ABKS protocols cannot resist keyword guessing attacks (KGAs), which can be launched by an untrusted cloud server and result in the exposure of sensitive personal information. Using attribute-based encryption (ABE) as the foundation, we build a secure data exchange paradigm that resists KGAs in this work. In our paper, we provide a secure data-sharing framework that resists KGAs and uses ABE as the foundation to achieve fine-grained access control to resources in the ciphertext. To avoid malicious guessing of keywords by the cloud server, the edge layer computes two encryption session keys based on group key agreement (GKA) technology, which are used to re-encrypt the data user’s secret key of the keyword index and keyword trapdoor. The model is implemented using the JPBC library. According to the security analysis, the model can resist KGAs in the random oracle model. The model’s performance examination demonstrates its feasibility and lightweight nature, its large computing advantages, and lower storage consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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27 pages, 1323 KiB  
Article
SDP-MTF: A Composite Transfer Learning and Feature Fusion for Cross-Project Software Defect Prediction
by Tianwei Lei, Jingfeng Xue, Duo Man, Yong Wang, Minghui Li and Zixiao Kong
Electronics 2024, 13(13), 2439; https://doi.org/10.3390/electronics13132439 - 21 Jun 2024
Cited by 1 | Viewed by 1351
Abstract
Software defect prediction is critical for improving software quality and reducing maintenance costs. In recent years, Cross-Project software defect prediction has garnered significant attention from researchers. This approach leverages transfer learning to apply the knowledge from existing projects to new ones, thereby enhancing [...] Read more.
Software defect prediction is critical for improving software quality and reducing maintenance costs. In recent years, Cross-Project software defect prediction has garnered significant attention from researchers. This approach leverages transfer learning to apply the knowledge from existing projects to new ones, thereby enhancing the universality of predictive models. It provides an effective solution for projects with limited historical defect data. Nevertheless, current methodologies face two main challenges: first, the inadequacy of feature information mining, where code statistical information or semantic information is used in isolation, ignoring the benefits of their integration; second, the substantial feature disparity between different projects, which can lead to insufficient effect during transfer learning, necessitating additional efforts to narrow this gap to improve precision. Addressing these challenges, this paper proposes a novel methodology, SDP-MTF (Software Defect Prediction using Multi-stage Transfer learning and Feature fusion), that combines code statistical features, deep semantic features, and multiple feature transfer learning methods to enhance the predictive effect. The SDP-MTF method was empirically tested on single-source cross-project software defect prediction across six projects from the PROMISE dataset, benchmarked against five baseline algorithms that employ distinct features and transfer methodologies. Our findings indicate that SDP-MTF significantly outperforms five classical baseline algorithms, improving the F1-Score by 8% to 15.2%, thereby substantively advancing the precision of cross-project software defect prediction. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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22 pages, 12903 KiB  
Article
Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
by Siyi Chen, Yu-An Tan, Kefan Qiu, Zheng Zhang, Yuanzhang Li and Quanxin Zhang
Electronics 2023, 12(22), 4601; https://doi.org/10.3390/electronics12224601 - 10 Nov 2023
Viewed by 4251
Abstract
The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human [...] Read more.
The Unified Extensible Firmware Interface (UEFI) provides a specification of the software interface between an OS and its underlying platform firmware. UEFI UI is an interactive interface that allows users to configure and manage UEFI settings, which is closely related to HII (Human Interface Infrastructure). In practice, HII provides a mechanism that allows developers to create UI elements with HII-related protocols. In this paper, we provide a comprehensive analysis of the UEFI combined with a case study. We proposed a protocol-centered static analysis method to obtain UEFI’s password policy, using HII-related protocols to find password implementation. Existing static analyses are ineffective in detecting such password policy in stripped UEFI firmware images. By reverse-engineering the IFR (Internal Forms Representation) in HII, we located where much sensitive information is stored. Lastly, we studied hardware port configurations, using Secure Boot as a case in point. We analyzed how UEFI uses the HII protocol to set relevant information in the UEFI UI. This paper is the first to offer a reverse-engineering systematic analysis of exploring UEFI via HII, providing valuable insights into its structure and potential enhancements for firmware security. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cyberspace Security)
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