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GTI-mSEMP Framework : A Proposed Framework to Stimulate Malware Propagation with Inclusion of Attacker-Defender Strategy

arXiv Security Archived Jun 29, 2026 ✓ Full text saved

arXiv:2606.28079v1 Announce Type: new Abstract: The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networks. Conventional epidemiological models often treat security defenses as static parameters, failing to capture the strategic, asymmetric maneuvers between an attacker and a defender. To address the gap, this paper proposes a Game-Theory-Integrated Modified Multi- Wireless Sensor Epidemic Malware Pro

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    Computer Science > Cryptography and Security [Submitted on 26 Jun 2026] GTI-mSEMP Framework : A Proposed Framework to Stimulate Malware Propagation with Inclusion of Attacker-Defender Strategy Shadeeb Hossain, Kristopher Wilson The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networks. Conventional epidemiological models often treat security defenses as static parameters, failing to capture the strategic, asymmetric maneuvers between an attacker and a defender. To address the gap, this paper proposes a Game-Theory-Integrated Modified Multi- Wireless Sensor Epidemic Malware Propagation (GTI-mSEMP) framework. This paper analyzed and compared the operational trajectories of Susceptible (S) and Recovered (R) node populations across three different operational regimes: Balanced Matchup, Exploit Surge and Hardened Defense. Numerical simulation results capture the real-time transient dynamics of the network state variables, demonstrating how the epidemic curve shifts when either the defensive or offensive scaling vectors hold an efficiency advantage. The proposed mathematical and numerical framework provides a rigorous foundation that can be deployed in highly adversarial network environments to evaluate dynamic malware propagation and predict localized node population states. Comments: 14 pages, 3 figures Subjects: Cryptography and Security (cs.CR); Computer Science and Game Theory (cs.GT); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2606.28079 [cs.CR]   (or arXiv:2606.28079v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.28079 Focus to learn more Submission history From: Shadeeb Hossain [view email] [v1] Fri, 26 Jun 2026 13:44:12 UTC (713 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.GT cs.NI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 29, 2026
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    Jun 29, 2026
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