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Multimedia Data and Network Security: Emerging Trends and AI-Driven Threats and Defenses

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Editor

Dr. Zahid Akhtar

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Guest Editor

Department of Network and Computer Security, State University of New York Polytechnic Institute, C135, Kunsela Hall, Utica, NY 13502, USA
Interests: machine learning and computer vision with applications to cybersecurity; biometrics; deepfakes; affect recognition; image and video processing; perceptual-based audiovisual multimedia quality assessment
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Special Issue Information

Dear Colleagues,

This growth in multimedia data types has led to the emergence of various cybersecurity threats that pose significant risks to the information security of networked systems. Multimedia content, including streaming videos, interactive multimedia, cloud storage, and new IoT devices, has changed how we communicate, entertain, and live. The emerging threats include automated and adversarial attacks, advanced persistent threats, deepfake-based misinformation, privacy infringements, and unauthorized alterations. Traditional countermeasures can rarely cope with the quantity, complexity, and originality of cyberattacks that rely on artificial intelligence (AI) for offense and defense. This Special Issue explores pressing challenges and innovations in securing multimedia data and the networks through which it is transmitted. This Special Issue invites high-quality, original research and review articles presenting cutting-edge methodologies, frameworks, and technologies to enhance multimedia data and network security. Topics of interest include deepfake detection and generation, AI-powered intrusion detection, multimedia forensics and authentication, steganography and digital watermarking, secure communication and streaming, encryption and cryptography, privacy-preserving processing, federated learning, and real-time defense mechanisms. By bringing together researchers and practitioners, this Special Issue aims to disseminate the latest findings, stimulate innovation, and shape future research directions in the field. We welcome your valuable contributions to this crucial discourse.

Dr. Zahid Akhtar
Guest Editor

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Keywords

multimedia security
network security
data encryption
digital watermarking
steganography
content protection
cryptography
secure multimedia communication
secure multimedia streaming
multimedia forensics
network intrusion detection
secure data transmission and storage
multimedia authentication
secure multimedia storage
multimedia privacy
AI-driven cybersecurity
deepfake detection
privacy-preserving machine learning
secure cloud multimedia
IoT multimedia security
adversarial attacks on multimedia
blockchain for multimedia security
perceptual-based multimedia quality assessment
biometric security
secure video analytics

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25 pages, 2842 KB

Open AccessArticle

Artificial Intelligence-Based Insider-Threat Detection: A Hybrid Explainable Framework with Automated Response and Privilege Containment

by Abdel Rahman Alkharabsheh, Ghaya Binsalma, Mahra Alharmi, Ruqia Alshateri, Shahad Altaee and Mousa Sweidan

Computers 2026, 15(7), 426; https://doi.org/10.3390/computers15070426 - 2 Jul 2026

Viewed by 903

Abstract

Insider threats continue to be the most persistent and most destructive threat to cybersecurity; malicious or negligent users work only in the real-time restricted area of the organization and are gradually breaking the boundaries of company norms. Conventional rule-based and statistical detection methods have difficulty detecting inconspicuous, context-dependent, and ever-changing behavior, leading to detection delays and high false-positive rates. Our paper introduces an explainable AI-based Insider-Threat Detection (AIB-ITD) model that integrates enterprise telemetry—including email, web, logon/VPN, and file events—into a unified behavioral framework. The effectiveness of combining heterogeneous behavioral indicators observed in AIB-ITD is consistent with recent behavioral analytics implementations that have demonstrated the value of multimodal user-behavior profiling for insider-threat identification in enterprise environments. The proposed AIB-ITD framework is based on anomaly-driven processing, unsupervised models (Isolation Forest, PCA reconstruction, and Autoencoder) are combined with sequential modeling (with an LSTM Autoencoder) to model both static and temporal deviations in behavior. An ensemble strategy is applied to combine the outputs of these models to yield a probabilistic insider risk score. To improve transparent analysis and to help the analyst gain trust, SHapley Additive Explanations (SHAP) is used to keep every detection outcome transparent and interpretable using the features. It also integrates feature correlation analysis, static vs sequential-model comparisons, and SHAP stability assessment to validate methodological robustness and reproducibility. An experimental review of the hybrid ensemble using the SEI/CMU CERT Insider Threat Dataset reveals that it performs better than single models for anomaly detection and stability, especially with the inclusion of temporal patterns. The assessment prioritizes anomaly score consistency and reliable risk ranking, rather than classification accuracy, to better reflect real deployment scenarios. In addition, an Automated Response and Privilege Containment (ARPC) feature automatically converts risk scores to multilevel mitigation actions that serve to protect the privacy of the user as the least privileged policies are enforced promptly. The proposed model showed superior robustness, stability, and operational effectiveness to classical methods, especially in the presence of scarce labeled data. Through hybrid anomaly recognition, explainable AI and automated response, AIB-ITD is a practical and scalable solution for next-generation insider-threat detection in enterprise systems. Full article

(This article belongs to the Special Issue Multimedia Data and Network Security: Emerging Trends and AI-Driven Threats and Defenses)

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