The rapid growth of the Internet of Things (IoT) has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks, anomalous traffic, and data manipulation due to decentralized architectures and limited resources. To address these issues, this paper proposes an advanced anomaly detection framework with three main phases. First, data preprocessing is performed using the Median KS Test to remove noise, handle missing values, and balance datasets for cleaner input. Second, a feature selection phase employs a Genetic Algorithm combined with eagle inspired search strategies to identify the most relevant features, reduce dimensionality, and improve efficiency without sacrificing accuracy. Finally, an ensemble classifier integrates Decision Tree, Random Forest, and XGBoost algorithms to achieve accurate and reliable anomaly detection. The proposed model demonstrates high adaptability and scalability across diverse IoT environments. Experimental results show that it outperforms existing methods by achieving 98 percent accuracy, 95 percent detection rate, and reductions in false positive (10 percent) and false negative (5 percent) rates. These results confirm the framework effectiveness and robustness in improving IoT network security against evolving cyber threats.

翻译：物联网的快速发展通过实现互联设备间的无缝数据交换，正在改变各行各业。然而，由于去中心化的架构和有限的资源，物联网网络仍然容易受到拒绝服务攻击、异常流量和数据篡改等安全威胁。为解决这些问题，本文提出了一种包含三个主要阶段的先进异常检测框架。首先，使用中位数KS检验进行数据预处理，以去除噪声、处理缺失值并平衡数据集，从而获得更干净的输入。其次，在特征选择阶段，采用遗传算法结合鹰启发搜索策略，以识别最相关的特征、降低维度并在不牺牲准确性的前提下提高效率。最后，集成分类器融合了决策树、随机森林和XGBoost算法，以实现准确可靠的异常检测。所提出的模型在不同物联网环境中展现出高度的适应性和可扩展性。实验结果表明，该模型以98%的准确率、95%的检测率以及更低的误报率和漏报率，优于现有方法。这些结果证实了该框架在提升物联网网络安全以应对不断演变的网络威胁方面的有效性和鲁棒性。