Comparative_AI_in_CS

1. Comparative Study of AI in Cyber
Security & Healthcare
Summarized Research Papers (2023–
2024)

2. AI, Machine Learning and Deep
Learning in Cyber Risk
Management
• Year: 2023
• Problem: Static/manual cyber risk
management struggles against evolving
threats in healthcare and critical
infrastructures.
• Methods: Survey of AI/ML/DL (SVM, neural
networks, Bayesian, hybrid models).

3. Anomaly Detection Model in
Network Security Situational
Awareness
• Year: 2024
• Problem: Traditional IDS fails to adapt to
evolving/zero day threats in healthcare
‑
networks.
• Methods: Survey of supervised, unsupervised,
and deep learning (CNN/RNN, autoencoders).

4. Cryptographic Primitives in
Privacy Preserving Machine
‑
Learning: A Survey
• Year: 2024
• Problem: Balancing patient data privacy with
ML utility in sensitive healthcare settings.
• Methods: Survey of HE, MPC, DP, ZK proofs for
‑
ML pipelines.
• Results: HE strongest privacy but costly; DP

5. Deep Reinforcement Learning for
Cyber Security
• Year: 2023
• Problem: Static defenses can’t keep up with
adaptive attackers in healthcare/IoT networks.
• Methods: Survey + case studies of DRL agents
for IDS/IPS and automated response.
• Results: Higher detection/adaptability vs

6. Detection of DoS Attack in Wireless
Sensor Networks: A Lightweight
ML Approach
• Year: 2023
• Problem: Resource constrained healthcare
‑
WSNs vulnerable to DoS; need efficient
detection.
• Methods: Decision Trees, Logistic Regression
on WSN datasets.

7. Ensemble Adaptive Online ML in
Data Stream: Case Study in
Intrusion Detection
• Year: 2024
• Problem: Batch IDS struggles with continuous
healthcare data streams (IoMT/hospital
networks).
• Methods: EnsAdp_CIDS—ensemble adaptive
online learning on CICIDS 2017, CIC IoT 2023,
‑ ‑ ‑
CIC MalMem 2022.
‑ ‑

8. IP2FL: Interpretation Based
‑
Privacy Preserving Federated
‑
Learning for ICPS
• Year: 2024
• Problem: Need privacy, efficiency, and
interpretability in FL for sensitive data
(applicable to healthcare).
• Methods: Additive HE + dual feature selection
+ Shapley value explanations in FL.
‑

9. ML Based Cyber Threat Detection
‑
with Explainable AI Insights
• Year: 2024
• Problem: Lack of transparency in ML models
for malware/ransomware detection in
healthcare.
• Methods: SVM/DT/KNN/RF with SHAP & LIME
on malware dataset.