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Multiagent Anti-Malware Model, a Paradigm Shift from Mere Detection to Prevention of Cyber Threats
Subject area: Science,Engineering and Technology · Area of research: Cyber Threats
Abstract
The security of information and data over the internet is one of the top challenges facing most business organization?s today as all businesses rely on internet services for their day-to-day operations. malware is one of the topmost challenging threats as systems are being locked up, business disrupted and even closed down as a result of this deadly threat. This research work deployed the activities of Mobile Agents. The agents were trained using Random Forest, Support Vector Machine and Decision Tree Machine Learning Algorithms to develop an Anti-Malware Model. In this research paper authentication scheme, the agents monitor all downloads including users? behaviour, scan all entries into the system, check for attachments in all external files and emails, eject external device, block all advertisements and flag up links and websites that are not registered into the Threat Intelligent Database (TID)as suspicious activities. With this the probability of success for all attempts to sneak into the system reaches near to zero. This practice will also solve the problem of False Positive Detection Rate (FPDR) during data training process as this model will serve as a mitigation apparatus to all kinds of malware activities as information are stored in the systems? Threats Intelligent Database (TID) for references and thereby cushion the effects and ugly activities of malware to our promising organizations.
Keywords
Mobile Agents, Anti-Malware, Machine Learning Algorithms, Threats Intelligent Database.
References
[1] Zahra J, Khushboo M, Mohit K, and Binay K (2024) Malware Detection Using Artificial Intelligence: Techniques, Research Issues and Future Directions. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249-8958 (Online), Volume-14 Issue-1, DOI:10.35940/ijeat. A4531.14011024 Journal Website: www.ijeat.org. October 2024. Retrieval Number: 100.1/ijeat.A453114011024.
[2] Azaabi C, Alex, and Benjamin A (2024) An Evaluation of Current Malware Trends and Defense Techniques: A Scoping Review with Empirical Case Studies. Journal of Advances in Information Technology, Vol. 15, No. 5, 2024.doi: 10.12720/jait.15.5.649-671.
[3] Ayodeji S, Gupta, G. P., and Kumar, S. (2024). Android malware detection and identification frameworks by leveraging machine and deep learning techniques: A comprehensive review. Telematics and Informatics Reports, 12, 100130.
[4] Elvis N, Ololade R and Chukwujekwu D (2025) Machine learning techniques for real-time malware classification and threat detection in distributed systems. World Journal of Advanced Research and Reviews,26(03), 2378-2398.https://doi.org/10.30574/wjarr.2025.26.3.2433.
[5] Sarah B, Shancang L and Lida X (2023) Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security. Electronics 2023, 12, 3927. https://doi.org/10.3390/ electronics12183927. https://www.mdpi.com/journal/electronics.
[6] Olivia O (2022) Ransomware Attacks in Nigeria: Case Studies and Mitigation Strategies, all content following this page was uploaded on 03 February 2025. The user has requested enhancement of the downloaded file. https://www.researchgate.net/publication/388634112.
How to cite this paper
@article{1712391,
author = {Ibeneme-Sabinus, Ifeoma Livina, Agbakwuru Onyekachi Alphonsus, Eleberi Leticia Ebele},
title = {Multiagent Anti-Malware Model, a Paradigm Shift from Mere Detection to Prevention of Cyber Threats},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {2596-2600},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712391.pdf},
abstract = {The security of information and data over the internet is one of the top challenges facing most business organization?s today as all businesses rely on internet services for their day-to-day operations. malware is one of the topmost challenging threats as systems are being locked up, business disrupted and even closed down as a result of this deadly threat. This research work deployed the activities of Mobile Agents. The agents were trained using Random Forest, Support Vector Machine and Decision Tree Machine Learning Algorithms to develop an Anti-Malware Model. In this research paper authentication scheme, the agents monitor all downloads including users? behaviour, scan all entries into the system, check for attachments in all external files and emails, eject external device, block all advertisements and flag up links and websites that are not registered into the Threat Intelligent Database (TID)as suspicious activities. With this the probability of success for all attempts to sneak into the system reaches near to zero. This practice will also solve the problem of False Positive Detection Rate (FPDR) during data training process as this model will serve as a mitigation apparatus to all kinds of malware activities as information are stored in the systems? Threats Intelligent Database (TID) for references and thereby cushion the effects and ugly activities of malware to our promising organizations.},
keywords = {Mobile Agents, Anti-Malware, Machine Learning Algorithms, Threats Intelligent Database.},
month = {November},
}