TY  - EJOU
AU  - Choi, Sang-Hoon 
AU  - Park, Ki-Woong 

TI  - GENOME: Genetic Encoding for Novel Optimization of Malware Detection and Classification in Edge Computing
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - The proliferation of Internet of Things (IoT) devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing. Nevertheless, the distributed nature of edge computing presents substantial security challenges, rendering it a prominent target for sophisticated malware attacks. Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources. This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation (GENOME) framework, a novel solution that is intended to improve the performance of malware detection and classification in peripheral computing environments. GENOME optimizes data storage and computational efficiency by converting malware artifacts into compact, structured sequences through a Deoxyribonucleic Acid (DNA) encoding mechanism. The framework employs two DNA encoding algorithms, standard and compressed, which substantially reduce data size while preserving high detection accuracy. The Edge-IIoTset dataset was used to conduct experiments that showed that GENOME was able to achieve high classification performance using models such as Random Forest and Logistic Regression, resulting in a reduction of data size by up to 42%. Further evaluations with the CIC-IoT-23 dataset and Deep Learning models confirmed GENOME’s scalability and adaptability across diverse datasets and algorithms. The potential of GENOME to address critical challenges, such as the rapid mutation of malware, real-time processing demands, and resource limitations, is emphasized in this study. GENOME offers comprehensive protection for peripheral computing environments by offering a security solution that is both efficient and scalable.
KW  - Edge computing; IoT security; malware; machine learning; malware classification; malware detection

DO  - 10.32604/cmc.2025.061267