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可靠的方法。本研究提出了一种用于物联网恶意软件识别的CNN-LSTM混合模型，并评估了其相较于已有方法的性能。通过采用K折交叉验证，所提出方法达到95.5%的准确率，超越了现有方法。CNN算法能够构建更优的学习模型，而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证明了该模型的有效性，突显了其增强物联网安全性的潜力。研究建议未来探索支持向量机作为替代方案，强调需要分布式检测策略，并指出预测分析对构建更强物联网安全体系的重要性。本研究为在物联网生态系统中开发更具韧性的安全措施提供了基础平台。