Special Issues
Table of Content

Applications of Artificial Intelligence for Information Security

Submission Deadline: 30 December 2024 View: 620 Submit to Special Issue

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


  • 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, 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 >

  • 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 >

  • 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 >

  • 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 >

  • 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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