Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.

翻译：物联网环境中的恶意软件检测需要稳健的方法论。本研究提出一种基于CNN-LSTM混合模型的物联网恶意软件识别方法，并与现有方法进行性能评估。通过K折交叉验证，所提方法达到95.5%的准确率，优于现有方法。CNN算法构建了更优的学习模型，LSTM分类器在分类任务中展现出更高的准确性。与主流技术的对比分析验证了模型的有效性，凸显其在增强物联网安全方面的潜力。研究建议未来探索支持向量机作为备选方案，强调分布式检测策略的必要性，并指出预测分析对构建更强物联网安全体系的重要性。本研究为物联网生态系统中开发更具韧性的安全防护措施提供了基础平台。