Machine Learning: Applications for Cybersecurity

Dear Colleagues,

Cybersecurity is essential in today’s digital world as threats continue to grow from malware and ransomware to massive data breaches and state-sponsored attacks. Traditional security mechanisms, while effective in structured environments, often struggle against the complexity, scale, and adaptability of modern attacks. Machine Learning (ML) and Artificial Intelligence (AI) offer powerful capabilities for detecting, predicting, and mitigating cyber threats by learning from patterns, adapting to evolving risks, and automating responses in real time.

This Special Issue aims to bring together cutting-edge research and practical applications of ML in the cybersecurity domain. We invite contributions that explore theoretical advances, novel algorithms, system implementations, case studies, and interdisciplinary approaches that leverage ML to strengthen cyber defense.

Topics of interest include (but are not limited to), the following:

• Intrusion Detection and Prevention
  • ML-based network traffic analysis.
  • Anomaly and outlier detection for malicious behaviors.
  • Deep learning for detecting zero-day attacks.
• Malware and Ransomware Analysis
  • Automated malware classification and clustering.
  • Adversarial ML techniques for malware evasion and defense.
  • Behavioral analysis using ML models.
• Authentication and Access Control
  • ML for biometric and continuous authentication.
  • Risk-based adaptive authentication.
  • Insider threat detection using ML.
• Privacy-Preserving Machine Learning in Cybersecurity
  • Federated learning for distributed cyber defense.
  • Homomorphic encryption and secure ML training.
  • Differential privacy techniques for cyber data analytics.
• Adversarial Machine Learning in Security
  • Attack models against ML-based defenses.
  • Robust and resilient ML algorithms.
  • Red-teaming and adversarial testing for security ML models.
• Cybersecurity in Emerging Technologies
  • ML for IoT and edge security.
  • Smart grid and critical infrastructure protection.
  • Securing blockchain and decentralized applications.
• Human-Centered and Explainable AI for Cybersecurity
  • Interpretability of ML models in threat detection.
  • Human-in-the-loop ML for cyber defense.
  • Usability and trust in AI-driven security systems

Dr. Muhammad Mazhar Rathore
Guest Editor

Manuscript Submission Information

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• cybersecurity and privacy
• machine learning (ML)
• deep learning
• federated learning
• privacy-preserving data analytics
• intrusion detection
• malware analysis and detection
• adversarial machine learning
• anomaly detection
• threat analytics and intelligence