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Applications of Artificial Intelligence for Information Security

Submission Deadline: 30 December 2024 (closed) View: 1205

Guest Editors

Dr. Shaozhang Niu, Beijing University of Posts and Telecommunications, China
Dr. Zhenguang Gao, Framingham State University, USA
Dr. Jiancheng Zou, North China University of Technology, China

Summary

Artificial intelligence (AI) and information security are two hot research areas today. AI is profoundly influencing and changing the patterns of our lives, work, and learning. It is widely used in various fields such as autonomous driving, medical diagnosis, and smart home, providing more accurate and efficient solutions. With the development of information technology, especially the breakthrough progress of next-generation AI technologies such as large models, information security issues are becoming increasingly prominent, and the data security of governments and enterprises and personal privacy protection are facing huge challenges. AI and information security technology are mutually penetrating, promoting, and constraining their respective development. Faced with a large number of key challenges emerging in the fields of AI and information security, researchers and technology developers are diligently working to provide proper solutions within these domains. This special issue aims to delve into the evolving landscape of AI and security concerns.  We invite academic and industrial communities to present their cutting-edge research, offering innovative solutions to meet these challenges. Together, let's embrace the AI promising future, fortify against various security threats, and unlock boundless opportunities.

Keywords

Machine learning algorithms.
Natural language processing (NLP).
Computer vision and pattern recognition.
Robotics.
Reinforcement learning.
Deep learning.
Explainable AI.
AI ethics and fairness.
Knowledge representation and reasoning.
AI in healthcare.
Threat detection and prevention.
Anomaly detection.
Intrusion detection and response.
Vulnerability assessment and management.
Authentication and access control.
Security analytics.
Threat intelligence and information sharing.
Privacy protection.
Cybersecurity risk assessment.
Secure network communication.

Published Papers

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  • Open Access

    ARTICLE

    Deepfake Detection Method Based on Spatio-Temporal Information Fusion

    Xinyi Wang, Wanru Song, Chuanyan Hao, Feng Liu
    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3351-3368, 2025, DOI:10.32604/cmc.2025.062922
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection… More >

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    260

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    131

  • Open Access

    ARTICLE

    A Deep Learning Framework for Arabic Cyberbullying Detection in Social Networks

    Yahya Tashtoush, Areen Banysalim, Majdi Maabreh, Shorouq Al-Eidi, Ola Karajeh, Plamen Zahariev
    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3113-3134, 2025, DOI:10.32604/cmc.2025.062724
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Social media has emerged as one of the most transformative developments on the internet, revolutionizing the way people communicate and interact. However, alongside its benefits, social media has also given rise to significant challenges, one of the most pressing being cyberbullying. This issue has become a major concern in modern society, particularly due to its profound negative impacts on the mental health and well-being of its victims. In the Arab world, where social media usage is exceptionally high, cyberbullying has become increasingly prevalent, necessitating urgent attention. Early detection of harmful online behavior is critical to… More >

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    150

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    49

  • Open Access

    ARTICLE

    A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

    Xiaoying Qiu, Xiaoyu Ma, Guangxu Zhao, Jinwei Yu, Wenbao Jiang, Zhaozhong Guo, Maozhi Xu
    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2025-2040, 2025, DOI:10.32604/cmc.2025.061869
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >

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    191

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    42

  • Open Access

    ARTICLE

    LogDA: Dual Attention-Based Log Anomaly Detection Addressing Data Imbalance

    Chexiaole Zhang, Haiyan Fu
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1291-1306, 2025, DOI:10.32604/cmc.2025.060740
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract As computer data grows exponentially, detecting anomalies within system logs has become increasingly important. Current research on log anomaly detection largely depends on log templates derived from log parsing. Word embedding is utilized to extract information from these templates. However, this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing. Currently, specialized research on data imbalance across log template categories remains scarce. A dual-attention-based log anomaly detection model (LogDA), which leveraged data imbalance, was proposed to address these issues in More >

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    400

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    126

  • Open Access

    ARTICLE

    Institution Attribute Mining Technology for Access Control Based on Hybrid Capsule Network

    Aodi Liu, Xuehui Du, Na Wang, Xiangyu Wu
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1471-1489, 2025, DOI:10.32604/cmc.2025.059784
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Security attributes are the premise and foundation for implementing Attribute-Based Access Control (ABAC) mechanisms. However, when dealing with massive volumes of unstructured text big data resources, the current attribute management methods based on manual extraction face several issues, such as high costs for attribute extraction, long processing times, unstable accuracy, and poor scalability. To address these problems, this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks. This technology leverages transfer learning ideas, utilizing Bidirectional Encoder Representations from Transformers (BERT) pre-trained language models to achieve vectorization of unstructured text… More >

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    357

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    138

  • Open Access

    ARTICLE

    Utilizing Fine-Tuning of Large Language Models for Generating Synthetic Payloads: Enhancing Web Application Cybersecurity through Innovative Penetration Testing Techniques

    Stefan Ćirković, Vladimir Mladenović, Siniša Tomić, Dalibor Drljača, Olga Ristić
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >

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    731

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    250

  • Open Access

    ARTICLE

    Unknown DDoS Attack Detection with Sliced Iterative Normalizing Flows Technique

    Chin-Shiuh Shieh, Thanh-Lam Nguyen, Thanh-Tuan Nguyen, Mong-Fong Horng
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4881-4912, 2025, DOI:10.32604/cmc.2025.061001
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity, capable of crippling critical infrastructures and disrupting services globally. As networks continue to expand and threats become more sophisticated, there is an urgent need for Intrusion Detection Systems (IDS) capable of handling these challenges effectively. Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics. This paper presents a novel approach for detecting unknown Distributed Denial of Service (DDoS) attacks by integrating Sliced Iterative Normalizing Flows (SINF) into IDS. SINF utilizes the… More >

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    137

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    200

  • Open Access

    ARTICLE

    Telecontext-Enhanced Recursive Interactive Attention Fusion Method for Line-Level Defect Prediction

    Haitao He, Bingjian Yan, Ke Xu, Lu Yu
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2077-2108, 2025, DOI:10.32604/cmc.2024.058779
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Software defect prediction aims to use measurement data of code and historical defects to predict potential problems, optimize testing resources and defect management. However, current methods face challenges: (1) Coarse-grained file level detection cannot accurately locate specific defects. (2) Fine-grained line-level defect prediction methods rely solely on local information of a single line of code, failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line, making it difficult to capture the interaction between global and local information. Therefore, this paper proposes a… More >

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    635

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    245

  • Open Access

    ARTICLE

    A Novel Approach for Android Malware Detection Based on Intelligent Computing

    Manh Vu Minh, Cho Do Xuan
    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4371-4396, 2024, DOI:10.32604/cmc.2024.058168
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity, in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices. In this paper, we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models. The proposed method combines two main techniques: (1) constructing a malware behavior profile and (2) extracting features from the malware behavior profile using graph neural networks. Specifically, to effectively construct an Android malware behavior profile,… More >

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    673

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    310

  • Open Access

    ARTICLE

    A Low Complexity ML-Based Methods for Malware Classification

    Mahmoud E. Farfoura, Ahmad Alkhatib, Deema Mohammed Alsekait, Mohammad Alshinwan, Sahar A. El-Rahman, Didi Rosiyadi, Diaa Salama AbdElminaam
    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

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    830

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    333

  • Open Access

    ARTICLE

    Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

    Abdullah Alabdulatif, Navod Neranjan Thilakarathne, Mohamed Aashiq
    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3655-3683, 2024, DOI:10.32604/cmc.2024.054610
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract The increasing prevalence of Internet of Things (IoT) devices has introduced a new phase of connectivity in recent years and, concurrently, has opened the floodgates for growing cyber threats. Among the myriad of potential attacks, Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic. As IoT devices often lack the inherent security measures found in more mature computing platforms, the need for robust DoS/DDoS detection systems tailored to IoT is paramount for… More >

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    1391

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    586

  • Open Access

    ARTICLE

    A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

    Yin Ni, Wu Zeng, Peng Xia, Guang Stanley Yang, Ruochen Tan
    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911
    （This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
    Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >

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    1042

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    581

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