Machine learning for malware and ransomware detection

Machine Learning for Malware and Ransomware Detection

Machine learning has emerged as a powerful technology for combating the growing sophistication of malware and ransomware attacks. Traditional signature-based detection methods often struggle to identify polymorphic malware, fileless attacks, zero-day exploits, and rapidly evolving ransomware variants. By leveraging supervised, unsupervised, deep learning, and reinforcement learning techniques, machine learning models can analyze file characteristics, network traffic, system behavior, memory patterns, and user activities to detect malicious software with greater accuracy and speed. These intelligent systems continuously adapt to emerging threats, enabling proactive threat identification and minimizing the impact of cyberattacks.

The International Conference on Cyber Security in Artificial Intelligence (ICCSAI 2027) invites researchers, academicians, cybersecurity professionals, industry practitioners, and technology innovators to present cutting-edge research and practical solutions in machine learning-based malware and ransomware detection. This track focuses on intelligent malware classification, behavioral analysis, ransomware prediction, anomaly detection, dynamic and static malware analysis, AI-powered sandboxing, threat intelligence integration, adversarial machine learning, explainable AI for malware detection, and automated incident response. Emphasis is also placed on securing cloud environments, Internet of Things (IoT) devices, mobile platforms, industrial control systems, and critical infrastructure against increasingly sophisticated cyber threats.

Participants will explore innovative algorithms, scalable detection frameworks, real-time monitoring systems, privacy-preserving machine learning models, and AI-driven cyber defense strategies that strengthen organizational resilience against malware attacks. The track aims to foster collaboration between academia, industry, and government agencies to address emerging challenges in cyber defense while promoting trustworthy, explainable, and resilient AI solutions. Through keynote sessions, technical presentations, and interdisciplinary discussions, ICCSAI 2027 provides a premier platform for advancing next-generation machine learning techniques that enhance malware detection, ransomware mitigation, and automated cybersecurity operations in an increasingly connected digital world.

1. Deep Learning-Based Malware Classification

2. AI-Powered Ransomware Detection and Prevention

3. Static Malware Analysis Using Machine Learning

4. Dynamic Malware Analysis and Behavioral Monitoring

5. Hybrid Malware Detection Techniques

6. Zero-Day Malware Detection

7. Fileless Malware Detection

8. Polymorphic and Metamorphic Malware Analysis

9. AI-Based Malicious Code Detection

10. Behavioral Analytics for Malware Identification

11. Feature Engineering for Malware Classification

12. Explainable AI (XAI) for Malware Detection

13. Adversarial Machine Learning in Malware Analysis

14. Malware Family Classification

15. AI-Based Malware Variant Detection

16. Intelligent Ransomware Prediction Models

17. Early-Stage Ransomware Detection

18. Ransomware Encryption Behavior Analysis

19. Automated Malware Reverse Engineering

20. AI-Assisted Malware Signature Generation

21. Malware Detection Using Graph Neural Networks (GNNs)

22. Federated Learning for Malware Detection

23. Transfer Learning in Malware Classification

24. Reinforcement Learning for Adaptive Cyber Defense

25. Malware Detection in Cloud Computing Environments

26. AI-Based Endpoint Malware Protection

27. Mobile Malware Detection Using Machine Learning

28. IoT Malware Detection and Analysis

29. Industrial Control Systems (ICS) Malware Detection

30. SCADA Malware Security

31. AI-Driven Threat Intelligence for Malware Analysis

32. Malware Detection Using Network Traffic Analysis

33. Memory-Based Malware Detection

34. AI for Botnet Detection and Mitigation

35. Intelligent Phishing and Malware Correlation Analysis

36. AI-Powered Sandboxing Techniques

37. Malware Detection in Containerized and Virtualized Environments

38. AI-Based Digital Forensics for Malware Investigation

39. Security Analytics for Malware Detection

40. Privacy-Preserving Machine Learning for Malware Analysis

41. Large Language Models (LLMs) for Malware Analysis

42. Generative AI for Malware Detection and Threat Intelligence

43. Real-Time Malware Detection Systems

44. Automated Incident Response to Malware Attacks

45. AI for Cyber Threat Hunting

46. Malware Detection Benchmarking and Evaluation

47. AI-Based Cyber Risk Assessment

48. Secure and Trustworthy AI Models for Malware Detection

49. Case Studies and Industrial Applications of AI-Driven Malware Defense