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.

提出了一种 CNN-LSTM 混合模型，用于物联网恶意软件识别，并使用 K 折交叉验证评估了其性能，其准确率达到 95.5%，超越了现有方法。研究表明，CNN 算法有助于构建学习模型，而 LSTM 分类器在分类方面表现出了更高的准确性。与普遍技术的比较分析证明了所提模型的有效性，突出了其提升物联网安全性的潜力。该研究主张进一步探索支持向量机作为替代方案，强调了分布式检测策略的必要性，并强调了预测分析对于更强大的物联网安全性的重要性。该研究为在物联网生态系统中制定更具韧性的安全措施提供了平台。