Machine Learning and Deep Learning: Practical Implementations in Cybersecurity

Dear Colleagues,

As cybersecurity threats evolve in complexity and scale, traditional defense mechanisms face significant limitations. This Special Issue of Electronics (MDPI) explores the transformative potential of deep learning (DL) and machine learning (ML) in addressing real-world security challenges. We invite original research and review articles that demonstrate innovative applications of DL/ML methodologies—including, but not limited to, anomaly detection, intrusion prevention, malware classification, adversarial attack mitigation, and secure IoT systems. Emphasis is placed on practical implementations, scalability studies, and solutions demonstrating robustness in dynamic threat landscapes. Contributions may cover algorithmic advances, optimized deployment frameworks, or empirical validations of DL/ML-driven security systems. This collection aims to bridge theoretical research with operational cybersecurity needs, providing actionable insights for researchers, industry practitioners, and policymakers committed to next-generation cyber defense.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Intrusion Detection and Prevention Systems (IDS/IPS):

• Development of ML/DL models for network-based and host-based anomaly detection.
• Real-time classification of malicious traffic and zero-day attack identification.
• Hybrid models combining signature-based and anomaly-based detection.

2. Malware Analysis and Detection:

• Deep learning for static and dynamic malware analysis (e.g., using CNNs on binary files, RNNs on API call sequences).
• Image-based malware classification and obfuscation-resistant detection techniques.
• ML-powered analysis of malware behavior and provenance.

3. Network Security and Traffic Analysis:

• Application of ML for encrypted traffic analysis (TLS/SSL) to identify malicious flows.
• Botnet and Command & Control (C&C) server detection using traffic pattern recognition.
• Anomaly detection in IoT, SCADA, and vehicular networks.

4. Phishing, Fraud, and Social Engineering Detection:

• NLP and deep learning models for fraudulent email, website, and SMS detection.
• Behavioral biometrics and ML for real-time fraud prevention in financial transactions.
• Identification of disinformation campaigns and fake news propagation.

5. Vulnerability Management and Threat Intelligence:

• Predictive analytics for software vulnerability discovery and exploitability prediction.
• ML-driven analysis of threat feeds and security advisories for proactive defense.
• Automated generation and prioritization of cyber threat intelligence.

6. Privacy-Preserving and Adversarial ML:

• Federated learning for collaborative security model training without sharing sensitive data.
• Defending against adversarial attacks (e.g., evasion, poisoning) on ML-based security systems.
• Developing robust and explainable AI models for cybersecurity applications.

7. Cloud, IoT, and Critical Infrastructure Security:

• Anomaly detection and access control in cloud environments and microservices architectures.
• Lightweight ML models for resource-constrained IoT device security.
• Securing industrial control systems (ICS) and critical national infrastructure.

8. Digital Forensics and Incident Response:

• Automation of forensic analysis (log, memory, disk) using ML for faster incident triage.
• Timeline reconstruction and attack attribution through intelligent data correlation.
• AI-assisted Security Orchestration, Automation, and Response (SOAR) platforms.

9. Large Language Models (LLMs) for Cybersecurity Applications:

• Leveraging LLMs for advanced penetration testing, automated vulnerability discovery in source code, and generating realistic attack scenarios for training and evaluation.
• Developing LLM-powered assistants for SOC analysts to automate alert triage, incident report summarization, and root cause analysis.
• Employing LLMs for real-time analysis of unstructured threat data from blogs, forums, and news sources to generate actionable intelligence and predictive insights.
• Using code-aware LLMs to identify security flaws, misconfigurations, and backdoors in software codebases (e.g., in DevOps pipelines).

10. Security and Privacy of Large Language Models:

• Investigating and mitigating novel attacks against LLMs, including prompt injection, jailbreaking, model extraction, and membership inference attacks.
• Exploring techniques for deploying LLMs in sensitive environments while protecting training data and model parameters (e.g., differential privacy, federated learning for LLMs).
• Ensuring LLMs used in cybersecurity applications are robust, reliable, and resistant to manipulation for malicious purposes.
• Developing methods to identify and attribute text, code, and other content generated by LLMs for forensic and fraud prevention purposes.

Dr. Liqun Yang
Dr. Jian Yang
Dr. Chaoge Liu
Guest Editors

Manuscript Submission Information

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• machine learning
• deep learning
• large language model
• cybersecurity