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This research addresses a critical gap in cybersecurity by optimizing multi-class malware detection for resource-constrained IoT devices using lightweight machine learning architectures. By combining LightGBM and Random Forest classifiers with advanced data balancing techniques, the study achieves significant accuracy improvements over existing models without sacrificing computational efficiency. The work demonstrates how specialized ML engineering can enhance threat detection capabilities where standard high-compute solutions are infeasible.
Enhancing Multiclass Malware Classification in Resource-Constrained Environments
arXiv:2609.27950v1 Announce Type: cross Abstract: The emergence of multi-class malware attacks such as ransomware, spyware, trojans, etc., presents an increasing and serious threat to cybersecurity, particularly in resourceconstrained environments like IoT devices. Existing machine learning models have achieved nearly perfect accuracy in binary malware classification but fall short in terms of classifying malware families and individual malware. Additionally, the complexity of these multi-class malware attacks presents a significant challenge of detection in resource-constrained environments, as multi-class detection usually requires high computational capability. This research bridges the gap by enhancing the detection accuracy of multi-class malware classification as well as developing a lightweight model that can run efficiently on resource-constrained devices. In this paper, we propose a robust, lightweight machine learning model featuring LightGBM classifier with SMOTE oversampling and SOM-US undersampling techniques for data balancing, as well as well-engineered feature selection through Genetic Algorithm. The model performed better than the current state-of-the-art models developed on the same dataset in both malware family classification (4 classes) and individual malware type classification (16 classes) with accuracy of 89.1% and 76% respectively. Thus, maintaining a balance between classification accuracy and computational efficiency in resource-constrained environments. Furthermore, we propose another model using Random Forest classifier with an accuracy of 91.2% in malware family classification and 78.7% in individual malware classification. Demonstrating a significant enhancement in terms of accuracy from the current state-of-the-art models.
Abstract:The emergence of multi-class malware attacks such as ransomware, spyware, trojans, etc., presents an increasing and serious threat to cybersecurity, particularly in resourceconstrained environments like IoT devices. Existing machine learning models have achieved nearly perfect accuracy in binary malware classification but fall short in terms of classifying malware families and individual malware. Additionally, the complexity of these multi-class malware attacks presents a significant challenge of detection in resource-constrained environments, as multi-class detection usually requires high computational capability. This research bridges the gap by enhancing the detection accuracy of multi-class malware classification as well as developing a lightweight model that can run efficiently on resource-constrained devices. In this paper, we propose a robust, lightweight machine learning model featuring LightGBM classifier with SMOTE oversampling and SOM-US undersampling techniques for data balancing, as well as well-engineered feature selection through Genetic Algorithm. The model performed better than the current state-of-the-art models developed on the same dataset in both malware family classification (4 classes) and individual malware type classification (16 classes) with accuracy of 89.1% and 76% respectively. Thus, maintaining a balance between classification accuracy and computational efficiency in resource-constrained environments. Furthermore, we propose another model using Random Forest classifier with an accuracy of 91.2% in malware family classification and 78.7% in individual malware classification. Demonstrating a significant enhancement in terms of accuracy from the current state-of-the-art models.
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2609.27950 [cs.CR]
(or arXiv:2609.27950v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2609.27950 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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