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Artificial Intelligence for Cybersecurity: Latest Advances and Prospects
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Artificial Intelligence for Cybersecurity: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 25539

Special Issue Editor

Dr. Shen Wang

E-Mail Website
Guest Editor
Department of Computer Sciences and Technology, Harbin Institute of Technology, Harbin 150080, China
Interests: data processing; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce our Special Issue, entitled "Artificial Intelligence for Cybersecurity: Latest Advances and Prospects", in which we aim to explore the cutting-edge research and advancements in this exciting field.

As the digital landscape evolves and becomes increasingly interconnected, the reliance on artificial intelligence for safeguarding sensitive information and defending against cyber threats has grown tremendously. This Special Issue provides an in-depth analysis of the current state of research in artificial intelligence for cybersecurity. We expect to highlight advanced techniques, such as machine learning, deep learning, big data, and cloud computing, which have revolutionized the way that we detect and respond to cyber-attacks. Furthermore, we hope to discuss the challenges faced by researchers in effectively harnessing the power of AI to combat cyber threats, addressing issues related to information security and privacy.

We invite researchers and experts in the field of cybersecurity to contribute to this Special Issue. We welcome original research articles, review papers, and perspectives that shed light on the latest advancements in the field, offer valuable insights, or propose new approaches to enhance the security and resilience of our digital systems.

Dr. Shen Wang
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. Applied Sciences 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.

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Published Papers (4 papers)

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Research

19 pages, 6034 KiB  
Article
GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention
by Zheng Zhao, Tianhao Zhang, Xiaoya Fan, Qian Mao, Dafeng Wang and Qi Zhao
Appl. Sci. 2024, 14(22), 10762; https://doi.org/10.3390/app142210762 - 20 Nov 2024
Viewed by 1314
Abstract
The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the [...] Read more.
The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity: Latest Advances and Prospects)
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20 pages, 406 KiB  
Article
Artificial Intelligence in Cybersecurity: A Review and a Case Study
by Selcuk Okdem and Sema Okdem
Appl. Sci. 2024, 14(22), 10487; https://doi.org/10.3390/app142210487 - 14 Nov 2024
Cited by 2 | Viewed by 15088
Abstract
The evolving landscape of cyber threats necessitates continuous advancements in defensive strategies. This paper explores the potential of artificial intelligence (AI) as an emerging tool to enhance cybersecurity. While AI holds widespread applications across information technology, its integration within cybersecurity remains a recent [...] Read more.
The evolving landscape of cyber threats necessitates continuous advancements in defensive strategies. This paper explores the potential of artificial intelligence (AI) as an emerging tool to enhance cybersecurity. While AI holds widespread applications across information technology, its integration within cybersecurity remains a recent development. We offer a comprehensive review of current AI applications in this domain, focusing particularly on their preventative capabilities against prevalent threats like phishing, social engineering, ransomware, and malware. To illustrate these concepts, the paper presents a case study showcasing a specific AI application in a cybersecurity context. This case study addresses a critical gap in securing communication within resource-constrained Internet of Things (IoT) networks using the IEEE 802.15.4 standard. We discussed the advantages and limitations of employing PN sequence encryption for this purpose. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity: Latest Advances and Prospects)
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18 pages, 946 KiB  
Article
SqliGPT: Evaluating and Utilizing Large Language Models for Automated SQL Injection Black-Box Detection
by Zhiwen Gui, Enze Wang, Binbin Deng, Mingyuan Zhang, Yitao Chen, Shengfei Wei, Wei Xie and Baosheng Wang
Appl. Sci. 2024, 14(16), 6929; https://doi.org/10.3390/app14166929 - 7 Aug 2024
Cited by 1 | Viewed by 4346
Abstract
SQL injection (SQLI) black-box detection, which simulates external attack scenarios, is crucial for assessing vulnerabilities in real-world web applications. However, existing black-box detection methods rely on predefined rules to cover the most common SQLI cases, lacking diversity in vulnerability detection scheduling and payload, [...] Read more.
SQL injection (SQLI) black-box detection, which simulates external attack scenarios, is crucial for assessing vulnerabilities in real-world web applications. However, existing black-box detection methods rely on predefined rules to cover the most common SQLI cases, lacking diversity in vulnerability detection scheduling and payload, suffering from limited efficiency and accuracy. Large Language Models (LLMs) have shown significant advancements in several domains, so we developed SqliGPT, an LLM-powered SQLI black-box scanner that leverages the advanced contextual understanding and reasoning abilities of LLMs. Our approach introduces the Strategy Selection Module to improve detection efficiency and the Defense Bypass Module to address insufficient defense mechanisms. We evaluated SqliGPT against six state-of-the-art scanners using our SqliMicroBenchmark. Our evaluation results indicate that SqliGPT successfully detected all 45 targets, outperforming other scanners, particularly on targets with insufficient defenses. Additionally, SqliGPT demonstrated excellent efficiency in executing detection tasks, slightly underperforming Arachni and SQIRL on 27 targets but besting them on the other 18 targets. This study highlights the potential of LLMs in SQLI black-box detection and demonstrates the feasibility and effectiveness of LLMs in enhancing detection efficiency and accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity: Latest Advances and Prospects)
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22 pages, 5604 KiB  
Article
Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks
by Faeiz Alserhani and Alaa Aljared
Appl. Sci. 2023, 13(24), 13310; https://doi.org/10.3390/app132413310 - 16 Dec 2023
Cited by 4 | Viewed by 3688
Abstract
With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models [...] Read more.
With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models or simple activity analysis. Moreover, Intelligent NIDS based on Machine Learning (ML) models are still in the early stages and often exhibit low accuracy and high false positives, making them ineffective in detecting emerging cyber-attacks. On the other hand, improved detection and prediction frameworks provided by ensemble algorithms have demonstrated impressive outcomes in specific applications. In this research, we investigate the potential of ensemble models in the enhancement of NIDS functionalities in order to provide a reliable and intelligent security defense. We present a NIDS hybrid model that uses ensemble ML techniques to identify and prevent various intrusions more successfully than stand-alone approaches. A combination of several distinct machine learning methods is integrated into a hybrid framework. The UNSW-NB15 dataset is pre-processed, and its features are engineered prior to being used to train and evaluate the proposed model structure. The performance evaluation of the ensemble of various ML classifiers demonstrates that the proposed system outperforms individual model approaches. Using all the employed experimental combination forms, the designed model significantly enhances the detection accuracy attaining more than 99%, while false positives are reduced to less than 1%. Full article
(This article belongs to the Special Issue Artificial Intelligence for Cybersecurity: Latest Advances and Prospects)
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