Guest Editors
Assoc. Prof. Ammar Odeh
Email: a.odeh@psut.edu.jo
Affiliation: King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan
Homepage:
Research Interests: computer security, steganography, sensor network, quantum computing
Prof. Walid Salameh
Email: walid@psut.edu.jo
Affiliation: School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan
Homepage:
Research Interests: computer networks, neural networks, machine learning, robotics, and data science
Prof. Arafat Awajan
Email: awajan@psut.edu.jo
Affiliation: School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan
Homepage:
Research Interests: artificial intelligence, natural language processing, text compression, and image understanding
Summary
The increasing complexity and scale of cyber threats have made malware one of the most significant challenges in modern computer networks, IoT environments, and cloud infrastructures. Artificial intelligence techniques provide powerful tools for analyzing large-scale security data and enabling accurate malware classification and cyber threat detection.
This Special Issue aims to explore recent advances in artificial intelligence, machine learning, and data analysis techniques for malware classification and cyber threat detection. With the rapid growth of connected systems, including cloud platforms, IoT networks, and large-scale distributed infrastructures, traditional signature-based detection methods are no longer sufficient to combat sophisticated and evolving malware. AI-driven approaches can analyze massive datasets, identify complex attack patterns, and enhance automated threat intelligence. This Special Issue invites contributions that propose innovative AI-based frameworks, algorithms, and data-driven solutions to improve malware detection, classification accuracy, scalability, and security across modern networked environments.
Suggested Themes
• Artificial intelligence and machine learning techniques for malware classification
• Deep learning approaches for cyber threat detection in computer networks
• Big data analytics for malware detection and security intelligence
• Malware analysis and classification in IoT and edge computing environments
• AI-based behavioral malware analysis and anomaly detection
• Privacy-preserving and federated learning approaches for malware detection
• Explainable and trustworthy AI for cybersecurity applications
Keywords
artificial intelligence, malware classification, cybersecurity, machine learning, deep learning, big data analytics, network security, internet of things security, behavioral malware analysis, threat intelligence
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