The number of Internet of Things (IoT) device connections is increasing rapidly as IoT applications are vital in any operation. IoT must maintain safe internet access that withstands various malicious attacks for instance Recon, Mirai, Distributed Denial of Service (DDoS), and Spoofing which has gained much attention. Intelligently changing and zero-day attacks are emerging every day. This highlights the need for intelligent security solutions tailored specifically to this technology. Various Machine Learning (ML) based approaches have been utilized for intrusion detection to tackle IoT attacks. However, the flaws of current attack detection and feature extraction techniques result in low detection accuracy. Thus, it hindered their real-world applications and highlighted the need for a lightweight and computationally robust model trained and assessed on a recent datasets. Therefore, this work proposed an attack detection model trained and validated using the CICIoT2023 and CICIDS2017 datasets. Initially, data preprocessing is done then features are extracted by using an unsupervised Elastic Deep Autoencoder (EDA) with optimum hyperparameters. Further, the Extreme Gradient Boosting (XGBoost) binary classifier is tuned by the Grey Wolf Optimizer (GWO) and fed extracted feature sets to classify attacks. The results of the experiments show the effectiveness of our model with a higher detection accuracy in both datasets. Finally, the performance comparison confirmed that the results of the proposed work is competitive with other state-of-the-art method in securing IoT infrastructures.

中文翻译：

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基于机器学习的物联网攻击检测


由于 IoT 应用程序在任何操作中都至关重要，因此物联网 （IoT） 设备连接的数量正在迅速增加。物联网必须保持安全的互联网访问，以抵御各种恶意攻击，例如 Recon、Mirai、分布式拒绝服务 （DDoS） 和欺骗，这些攻击已引起广泛关注。智能变化的攻击和零日攻击每天都在出现。这凸显了对专门为这项技术量身定制的智能安全解决方案的需求。各种基于机器学习 （ML） 的方法已被用于入侵检测，以应对 IoT 攻击。然而，当前攻击检测和特征提取技术的缺陷导致检测准确性低。因此，它阻碍了他们的实际应用，并突出了对在最近的数据集上训练和评估的轻量级和计算稳健模型的需求。因此，这项工作提出了一种使用 CICIoT2023 和 CICIDS2017 数据集进行训练和验证的攻击检测模型。最初，完成数据预处理，然后使用具有最佳超参数的无监督弹性深度自动编码器 （EDA） 提取特征。此外，Extreme Gradient Boosting （XGBoost） 二元分类器由 Grey Wolf Optimizer （GWO） 进行调整，并馈送提取的特征集以对攻击进行分类。实验结果表明，我们的模型在两个数据集中都具有更高的检测准确性。最后，性能比较证实，在保护 IoT 基础设施方面，拟议工作的结果与其他最先进的方法相比具有竞争力。