MASTERS COMPUTER ENGINEERING PROJECT TOPICS-CYBER SECURITY/ARTIFICIAL INTELLIGENCE PROJECT TOPICS AND MATERIALS
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MASTERS COMPUTER ENGINEERING PROJECT TOPICS-CYBER SECURITY/ARTIFICIAL INTELLIGENCE PROJECT TOPICS AND MATERIALS
1. AI-based intrusion detection system for network traffic anomaly detection in SMEs.
2. Deep-learning approach to zero-day malware detection using behavioural features.
3. Designing a federated-learning framework for malware detection across mobile devices.
4. Explainable AI (XAI) techniques for anomaly detection in enterprise networks.
5. Adversarial machine learning: defence mechanisms against poisoning attacks on IDS.
6. Generative-AI (deepfake) detection framework for voice and video in social engineering attacks.
7. Blockchain-based logging and audit trail for tamper-proof security event records in IoT systems.
8. Lightweight cryptographic protocol design for resource-constrained IoT devices in smart-city applications.
9. Real-time threat intelligence using AI to predict and prevent advanced persistent threats (APTs).
10. Natural language processing (NLP) methods to detect phishing emails and URLs via semantics.
11. Behavioural biometrics + AI for continuous authentication in mobile banking.
12. Reinforcement-learning based adaptive defence agent for cyber‐physical systems (CPS).
13. AI-powered vulnerability management tool: prioritising patches with risk scoring.
14. Secure AI model deployment: privacy-preserving machine learning for cybersecurity systems.
15. Secure federated anomaly detection system for distributed industrial control systems (ICS).
16. AI-based ransomware detection and mitigation strategies in enterprise clouds.
17. AI in supply-chain security: detecting malicious code injection in software supply chains.
18. Hybrid IDS combining signature-based and AI-based detection for improved performance in SMEs.
19. Deep learning for malware classification using dynamic analysis of executable behaviour.
20. AI-driven cyber threat hunting: automated log analysis and incident response.
21. Explainable adversarial ML for intrusion detection: making the “why” clear to analysts.
22. AI-powered deep-fake prevention system for safeguarding critical infrastructure communications.
23. Lightweight AI models for endpoint security in remote devices with limited compute.
24. Behaviour-based insider threat detection using machine learning in corporate networks.
25. AI-enhanced honeypot design for proactive deception and threat intelligence collection.
26. Privacy-first AI in cybersecurity: differential-privacy and homomorphic encryption in intrusion detection.
27. AI-guided security policy generation and enforcement in cloud environments.
28. Multi-agent AI simulation of cyber attacks for training and defence readiness.
29. Explainable reinforcement learning for dynamic network defence strategies.
30. AI-driven forensic readiness framework for digital investigations after breaches.
31. Deep-learning for anomaly detection in IoT sensor networks within a smart building.
32. AI-powered detection of targeted phishing using social media and behavioural data.
33. Automated security operations centre (SOC) assistant using AI to triage alerts.
34. AI-enabled edge device security: securing IoT with on‐device ML models.
35. Large language model (LLM) vulnerabilities in cybersecurity systems: prompt injection and defence.
36. AI for deception-based cybersecurity: designing moving-target defence with ML.
37. AI-based detection of cryptojacking attacks in cloud platforms.
38. Secure AI pipeline: ensuring integrity and confidentiality of AI models used in cybersecurity.
39. AI design for threat modelling in Industrial Internet of Things (IIoT).
40. Behavioural analytics for fraud detection in mobile money using machine learning.
41. AI-based network traffic classification for segmented network zones (e.g., OT vs IT).
42. Deep-learning for malware family classification using opcode sequence embeddings.
43. AI in biometric spoof detection: fingerprint/face anti-spoof using ML.
44. AI-powered cyber risk scoring model for small and medium enterprises.
45. Hybrid deep-learning + blockchain for secure firmware updates in IoT devices.
46. AI for securing cloud native applications: anomaly detection in microservices.
47. Machine learning for automated penetration testing and vulnerability exploitation simulation.
48. AI-based sandboxing environment for dynamic malware behaviour analysis.
49. Cybersecurity of AI: evaluating robustness of ML models in adversarial settings.
50. AI-driven incident response orchestration: automating containment and remediation.
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Topics (51 to 100: AI + broader Computer Engineering with Cyber-Security flavour)
51. Energy-efficient deep‐learning model deployment for intrusion detection in edge devices.
52. FPGA-accelerated hardware implementation of ML-based IDS for low-latency detection.
53. AI-driven predictive maintenance for network hardware to prevent security failures.
54. Secure hardware design for AI accelerators in cybersecurity systems (trusted execution).
55. Neural-network compression and quantisation for embedded cybersecurity applications.
56. Hardware-software co-design of AI‐powered firewall for IoT networks.
57. Transparent AI hardware monitoring modules to detect tampering or side-channel attacks.
58. AI in vehicular networks security: detecting malicious vehicle messages in V2X using ML.
59. AI-enabled drone network security: intrusion detection in autonomous aerial systems.
60. Blockchain + AI for secure data sharing in collaborative cybersecurity among organisations.
61. AI-driven energy-aware routing algorithms in wireless sensor networks with security constraints.
62. AI for anomaly detection in power grid SCADA systems: combining domain engineering & ML.
63. Designing a secure AI chat-bot for cybersecurity awareness training in enterprises.
64. AI-based image forensics: detecting tampered surveillance footage in smart campuses.
65. Machine learning for side-channel attack detection in cryptographic hardware.
66. AI-powered privacy-preserving system for facial recognition in access control systems.
67. Designing secure AI pipelines on heterogeneous systems (CPU + GPU + FPGA) for cybersecurity tasks.
68. AI for biometric key generation and management in secure communication systems.
69. Reinforcement-learning for dynamic resource allocation in secure cloud‐edge computing.
70. AI-based network slicing security in 5G/6G: detecting and isolating malicious slices.
71. AI-driven system for secure firmware anomaly detection in smart home devices.
72. Designing a hybrid AI-blockchain framework for tamper-resistant audit logs in critical systems.
73. AI-based speech recognition anomaly detection for secure voice‐activated systems.
74. AI for malware propagation modelling in connected devices and preventive strategies.
75. Deep‐reinforcement‐learning for autonomous network configuration defending against threats.
76. AI in quantum-resistant cryptography: using ML to help design/simulate resistant algorithms.
77. AI-driven smart contract vulnerability detection in blockchain systems.
78. AI for resource-constrained device authentication in large-scale IoT deployments.
79. AI‐enabled smart camera network security: anomaly detection and automatic alerting.
80. AI for digital twin security: protecting virtual replicas of industrial systems from cyber attacks.
81. AI‐based protocol anomaly detection in wireless sensor networks for agriculture.
82. Machine learning for detecting malicious firmware updates in networked devices.
83. AI in secure data compression/encryption for IoT telemetry streams.
84. AI-based predictive modelling for insider threat risk in corporate hardware systems.
85. Designing AI middleware for integrating threat detection in mixed hardware-software systems.
86. AI-powered security analytics on big data logs in telecom networks.
87. Secure federated multi-edge learning for anomaly detection in smart city infrastructure.
88. AI-based vulnerability prediction in software modules using historical patch data.
89. Hardware implementation of adversarial defence modules for ML models in cybersecurity.
90. AI-driven trust‐management in cooperative autonomous vehicle networks for secure communications.
91. AI for biometric gait-recognition anomaly detection in secure facility access.
92. Designing an AI based system for zero-trust architecture enforcement in enterprise networks.
93. AI-powered network function virtualization (NFV) security for 5G infrastructure.
94. AI-based cyber‐resilience modelling for critical infrastructure (water, energy) systems.
95. Human-in-the-loop AI system for cybersecurity incident triage and decision support.
96. AI for privacy-preserving multi-party computation in collaborative threat intelligence sharing.
97. AI‐based compression and steganography detection in multimedia for secure communications.
98. Designing an AI‐driven end-to-end security framework for edge computing in smart agriculture.
99. AI for anomaly detection in unmanned aerial system (UAS) communication links.
100. AI-enabled secure orchestration of microservices in cloud-native applications under cyber threats.