In February 2025, M1 student Sangawa-san made a presentation: “Parallel-processed hyper parameter tuning for higher accuracy of malware detection” on 2025 International Conference on Intelligent Information Technology (ICIIT 2025), in Hanoi, Vietnam. The study is focused on malware detection using machine learning techniques.
Malware, a collective term for malicious software, has traditionally been identified using signature-based or pattern-matching approaches. However, such methods often struggle to adapt to newly emerging threats. In this study, a novel approach was proposed to enhance detection accuracy by applying hyperparameter optimization through parallel processing. This strategy enabled the construction of a high-performance detection model within constrained computational time.
Experimental evaluations demonstrated that the proposed method significantly outperformed conventional techniques in terms of detection accuracy. These results underscore the practical effectiveness of the approach and its potential applicability in real-world cybersecurity systems. The findings were well received by the conference audience, highlighting the relevance of machine learning-based methods in modern malware detection.
