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Game Theory Analysis and Modeling of Sophisticated Multi-Collusion Attack in MANETs
Open accessAbstract
Mobile Adhoc Network (MANET) has been a core topic of research since the last decade. Currently, this form of networking paradigm is increasingly being construed as an integral part of upcoming urban applications of Internet-of-Things (IoT), consisting of massive connectivity of diverse types of nodes. There is a significant barrier to the applicability of existing routing approaches in conventional MANETs when integrated with IoT. This routing mismatch can lead to security risks for the MANET-based application tied with the IoT platform. This paper examines a pragmatic scenario as a test case wherein the mobile nodes must exchange multimedia signals for supporting real-time streaming applications. There exist two essential security requirements viz. i) securing the data packet and ii) understanding the unpredictable behavior of the attacker. The current study considers sophistication on the part of attacker nodes. They are aware of each other’s identity and thereby collude to conduct lethal attacks, which is rarely reflected in existing security modeling statistics. This research harnesses the potential modeling aspect of game theory to model the multiple-collusion attacker scenario. It contributes towards i) modeling strategies of regular/malicious nodes and ii) applying optimization principle using novel auxiliary information to formulate the optimal strategies. The model advances each regular node’s capability to carry out precise computation about the opponent player’s strategy prediction, i.e., malicious node. The simulation outcome of the proposed mathematical model in MATLAB ascertains that it outperforms the game theory’s baseline approach.
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“The attention mechanism boosts detection accuracy by letting models pay attention to the relevant features while disregarding less informative or noisy data.”
From A Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs (2025) · cites this paper as [38]
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Cited by
Sorted by citations
SGM: Strategic Game Model for Resisting Node Misbehaviour in IoT-Cloud Ecosystem
Khan, B. U. I., Anwar, F., Rahman, F. D. B. A., Olanrewaju, R. F., Goh, K. W., Janin, Z., & Rahman, M. A. (2022) · Information · doi:10.3390/info13110544 · Cited by 21
Self-citationA Hybrid Deep Learning Model for Detection and Mitigation of DDoS Attacks in VANETs
Jayakrishna, N., & Prasanth, N. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-15215-1 · Cited by 20
Metadata
- Authors
- Khan, B. U. I., Anwar, F., Olanrewaju, R. F., Kiah, M. L. B. M., & Mir, R. N.
- Affiliations
- International Islamic University Malaysia (MY)
- National Institute of Technology Srinagar (IN)
- ORCID
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 61778-61792
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3153580362
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 4 · 2023: 7 · 2022: 3
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- The International Islamic University Malaysia Repository (The International Islamic University Malaysia) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Higher Education Malaysia (Kementerian Pendidikan Tinggi) through the Fundamental Research Grant Scheme (FRGS) (FRGS19-137-0746)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- mobile ad hoc networks, malicious node detection, optimal strategies, network security
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