Machine Learning and Cybersecurity—Trends and Future Challenges

We are pleased to present this Special Issue of Electronics, which explores the dynamic intersection of machine learning (ML) and cybersecurity. With the rapid expansion of digital ecosystems, cyber threats have grown in sophistication, requiring equally advanced defensive mechanisms. Machine learning has become a cornerstone of this evolution, enabling anomaly detection, intrusion prevention, behavior modeling, and adversarial resilience across diverse domains—from cloud-native environments to automotive networks.

This collection of research contributions highlights both innovative applications of ML in cybersecurity and the challenges of building secure, transparent, and ethical systems. The works featured here address problems ranging from phishing detection, intrusion monitoring, and adversarial resilience to securing vehicular and automotive networks, containerized infrastructures, and critical public-sector systems. Together, they provide a comprehensive overview of the state of the art and point toward critical future directions.

1. Research Highlights

1.1. Intrusion Detection and Adversarial Resilience

• Contribution 1 develops a genetic algorithm-driven feature selection method for automated network incident identification, improving intrusion detection accuracy.
• Contribution 2 introduces a robust intrusion detection system for 5G networks, based on Extremely Randomized Trees and Infinite Feature Selection, designed to withstand adversarial attacks.
• Contribution 3 evaluates multi-class SVM approaches for network traffic classification on the UWF-ZeekData dataset, offering insights into efficient ML deployment for cybersecurity.

1.2. Phishing, Web Security, and Application-Layer Threats

• Contribution 4 presents a systematic review of deep learning techniques for phishing email detection, outlining the strengths and challenges of applying DL to large-scale, real-time email defense.
• Contribution 5 proposes machine learning models for SQL injection detection, advancing web application security with predictive and adaptive defenses.

1.3. Cyber–Physical and Automotive Security

• Contribution 6 employs unsupervised learning to detect false information attacks in vehicular ad hoc networks (VANETs), enhancing trust in connected vehicle ecosystems.
• Contribution 7 reviews the cybersecurity of automotive wired networking systems, highlighting evolving threats to Ethernet and CAN buses and proposing countermeasures supported by ML-driven monitoring.
• Contribution 8 applies CNN-based image deep learning for detecting DNS amplification distributed reflection denial-of-service (DRDoS) attacks, advancing automated mitigation of large-scale disruptions.

1.4. Cloud and Container Security

• Contribution 9 introduces an eBPF-based runtime security framework combined with ML to detect cryptojacking in containerized environments, offering a lightweight and real-time defense mechanism for cloud-native infrastructures.

1.5. Large-Scale Risk and Public-Sector Security

• Contribution 10 applies ML to quantify cyber risk across 7000 municipalities, providing a nationwide perspective on vulnerabilities in local public administrations and their potential role in compromising national infrastructure.

2. Future Outlook

• Resilient ML models: Developing algorithms resistant to adversarial attacks, spoofing, and poisoning is critical for safeguarding trust.
• Multi-modal and cross-domain integration: Extending ML applications from biometrics to networks, automotive systems, and cloud containers underscores the need for adaptable, multi-layered defenses.
• Transparent and explainable AI: Interpretability of ML-based security decisions remains essential for compliance, ethics, and operational trust.
• Privacy-by-design frameworks: Future systems must balance powerful data-driven learning with strong guarantees of user privacy and regulatory compliance.
• Cyber–physical security: Emerging areas such as VANETs, automotive Ethernet, and municipal cyber-risk analysis highlight the convergence of physical and digital security challenges.