Adaptive Coverage Optimization of Wireless Sensor Nodes using Path Loss Estimation and Detection Accuracy Metrice
DOI:
https://doi.org/10.15662/IJEETR.2026.0802226Keywords:
AI-Based Intrusion Detection, Machine Learning, Deep Learning, Generative Adversarial Networks, Reinforcement Learning, Explainable AI, Industrial Cyber-Physical Systems, Adaptive Security, Cyber Attacks, Network Protection.Abstract
The rapid expansion of digital networks and cloud-based infrastructures has significantly increased exposure to sophisticated cyber attacks, demanding more intelligent and adaptive Intrusion Detection Systems (IDS). Conventional IDS mechanisms, primarily dependent on signature-based and rule-based detection, often fail to recognize zero-day exploits and evolving attack patterns. To overcome these limitations, Artificial Intelligence (AI) has been increasingly integrated into IDS frameworks to enhance detection accuracy and adaptability. AI-driven IDS utilize advanced Machine Learning (ML) and Deep Learning (DL) models to process large-scale network traffic data, uncover hidden patterns, and identify anomalous behaviors associated with malicious activities.
Recent developments in AI technologies have introduced adaptive and self-learning capabilities within IDS architectures, enabling continuous improvement in threat detection. Techniques such as Generative Adversarial Networks (GANs) are used to create synthetic attack samples, addressing data imbalance issues and strengthening model robustness against rare intrusions. Furthermore, Reinforcement Learning (RL) algorithms support dynamic security policy adjustments, allowing systems to respond proactively to real-time network threats.
The incorporation of Explainable AI (XAI) enhances interpretability by providing transparency into model decisions, thereby improving analyst confidence and operational trust. Additionally, AI-enabled IDS implementations in Industrial Cyber-Physical Systems (ICPS) demonstrate effective deployment in large-scale and mission-critical environments.
References
1. Li, X., Zhang, Y., & Chen, H. (2024). Hybrid CNN-LSTM Architecture for Facial Expression-Based Depression Detection. IEEE Transactions on Affective Computing, 15(2), 210–223.
2. Kumar, R., & Mehta, S. (2024). Generative Adversarial Networks for Data Augmentation in Facial Emotion Recognition Systems. Pattern Recognition Letters, 178, 45–56.
3. Sharma, P., & Verma, A. (2024). Explainable Artificial Intelligence in Mental Health Diagnosis: Methods and Applications. ACM Computing Surveys, 57(1), Article 12.
4. Chen, L., & Wang, Y. (2024). Reinforcement Learning-Based Optimization for Adaptive Emotion Classification Models. IEEE Access, 12, 45890–45905.
5. Rao, K., & Nair, V. (2024). Federated Learning for Privacy-Preserving Mental Health Monitoring Systems.
Computers in Biology and Medicine, 168, 107678.
6. Ahmed, S., & Kapoor, R. (2024). Robust Deep Learning Models for Facial Depression Recognition Under Adversarial Conditions. Information Sciences, 621, 389–404.
7. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
8. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
9. Rajasekar, M. (2025). Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks. International Journal of Computer Technology and Electronics Communication, 8(4), 11078-11086.
10. Anujaa, T., Thajudeen Ali Ahamed, A. F., Baranwal, V., Thanikaiselvan, V., Subashanthini, S., Sivaranjani Devi, C., & Rengarajan, A. (2025). A lightweight multi round confusion-diffusion cryptosystem for securing images using a modified 5D chaotic system. Scientific Reports, 15(1), 31986.
11. Ramanathan, U., & Rajendran, S. (2023). Weighted particle swarm optimization algorithms and power management strategies for grid hybrid energy systems. Engineering Proceedings, 59(1), 123.
