A Comprehensive Analysis of Security Mechanisms and Threat Characterization in Mobile Ad Hoc Networks

Abstract: Mobile Ad Hoc Networks (MANETs) represent a critical advancement in wireless communication, enabling dynamic, infrastructure-less networking suitable for various applications, including military, disaster recovery, and remote sensing. However, their decentralized architecture, dynamic topology, and constrained resources expose them to a wide range of security challenges. This research paper presents a comprehensive evaluation of existing security mechanisms designed to protect MANETs, encompassing cryptographic protocols, intrusion detection systems, secure routing techniques, and trust-based frameworks. By systematically analyzing their effectiveness, limitations, and adaptability, the study highlights gaps in current approaches. Additionally, this work identifies and characterizes MANET-specific security threats, including wormhole, black hole, sybil, and denial-of-service attacks. Through this dual-focused investigation, the paper not only maps the current landscape of MANET security solutions but also provides a structured threat taxonomy, offering insights into the evolving threat model. The findings aim to guide the development of more robust, adaptive, and context-aware security frameworks tailored for MANET environments.

Intrusion detection, Secure routing, Decentralized networks, Network vulnerabilities, Attack taxonomy

1. P. C. S. Reddy, P. Shirley Muller, S. N. Koka, V. Sharma, N. Sharma, and S. Mukherjee, “Detection of Encrypted and Malicious Network Traffic using Deep Learning,” in Proc. 2023 Int. Conf. Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, 2023, pp. 1–6, doi: 10.1109/AIKIIE60097.2023.10390386. [Google Scholar] [Crossref]

2. V. Sharma, K. K. Sharma, T. K. Vashishth, R. Panwar, B. Kumar, and S. Chaudhary, “Brain-Computer Interface: Bridging the Gap Between Human Brain and Computing Systems,” in Proc. 2023 Int. Conf. Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), Chennai, India, 2023, pp. 1–5, doi: 10.1109/RMKMATE59243.2023.10369702. [Google Scholar] [Crossref]

3. F. Sultana, R. Bhardwaj, R. N. Kumar, R. Gaur, S. P. Shankar, and A. Bharadwaj, “Smart Traffic Management System for Efficient Mobility and Emergency Response,” in Proc. 2024 Int. Conf. Knowledge Engineering and Communication Systems (ICKECS), 2024, pp. 1–6, doi: 10.1109/ICKECS61492.2024.10617376. [Google Scholar] [Crossref]

4. P. Kavitha, K. P. Teja, S. G. Rahul, K. R. K. Reddy, and V. S. C. Reddy, “IoT-Based Traffic Control System for Emergency Vehicles,” in Proc. Int. Conf. Intelligent Algorithms for Computational Intelligence Systems (IACIS 2024), 2024, pp. 1–6, doi: 10.1109/IACIS.2024.10112694. [Google Scholar] [Crossref]

5. S. Han, Q. Wu, H. Zhang, and B. Qin, “Light-Weight Unsupervised Anomaly Detection for Encrypted Malware Traffic,” in Proc. 2022 7th IEEE Int. Conf. Data Science in Cyberspace (DSC), Guilin, China, 2022, pp. 206–213, doi: 10.1109/DSC55232.2022.00042. [Google Scholar] [Crossref]

6. Z. Zhao et al., “ERNN: Error-Resilient RNN for Encrypted Traffic Detection Towards Network-Induced Phenomena,” IEEE Trans. Dependable Secure Comput., vol. 20, no. 1, pp. 1–18, Jan. 2023, doi: 10.1109/TDSC.2023.3245678. [Google Scholar] [Crossref]

7. Y. Zeng, H. Gu, W. Wei, and Y. Guo, “Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework,” IEEE Access, vol. 7, pp. 45182–45190, Apr. 2019, doi: 10.1109/ACCESS.2019.2908225. [Google Scholar] [Crossref]

8. T. Yu, F. Zou, L. Li, and P. Yi, “An Encrypted Malicious Traffic Detection System Based on Neural Network,” in Proc. 2019 Int. Conf. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Guilin, China, 2019, pp. 62–70, doi: 10.1109/CyberC.2019.00020. [Google Scholar] [Crossref]

9. W. Wang, M. Zhu, J. Wang, X. Zeng, and Z. Yang, “End-to-End Encrypted Traffic Classification with One-Dimensional Convolution Neural Networks,” in Proc. 2017 IEEE Int. Conf. Intelligence and Security Informatics (ISI), Beijing, China, 2017, pp. 43–48, doi: 10.1109/ISI.2017.8004872. [Google Scholar] [Crossref]

10. L. Yang and A. Shami, “A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles,” in Proc. 2022 IEEE Int. Conf. Communications (ICC), Seoul, South Korea, 2022, pp. 2774–2779, doi: 10.1109/ICC46239.2022.9838729. [Google Scholar] [Crossref]

11. V. Sharma and S. Kumar, “Role of Artificial Intelligence (AI) to Enhance the Security and Privacy of Data in Smart Cities,” in Proc. 2023 3rd Int. Conf. Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 596–599, doi: 10.1109/ICACITE57410.2023.10182455. [Google Scholar] [Crossref]

12. A. T. Somnathe, I. A. Tayubi, P. C. S. Reddy, N. Sharma, V. Sharma, and M. Yesubabu, “Brain Computer Interaction Framework for Speech and Motor Impairment Using Deep Learning,” in Proc. 2023 Int. Conf. Power Energy, Environment & Intelligent Control (PEEIC), Greater Noida, India, 2023, pp. 1008–1013, doi: 10.1109/PEEIC59336.2023.10450481. [Google Scholar] [Crossref]

13. R. Kant, S. Sharma, V. Vikas, S. Chaudhary, A. K. Jain, and K. K. Sharma, “Blockchain – A Deployment Mechanism for IoT Based Security,” in Proc. 2023 Int. Conf. Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India, 2023, pp. 739–745, doi: 10.1109/CICTN57981.2023.10140715. [Google Scholar] [Crossref]

14. Vikas, R. P. Daund, D. Kumar, P. Charan, R. S. K. Ingilela, and R. Rastogi, “Intrusion Detection in Wireless Sensor Networks using Hybrid Deep Belief Networks and Harris Hawks Optimizer,” in Proc. 2023 4th Int. Conf. Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2023, pp. 1631–1636, doi: 10.1109/ICESC57686.2023.10193270. [Google Scholar] [Crossref]

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