Rihan Shaza Dawood Ahmed, Anbar Mohammed, Alabsi Basim Ahmad
Applied College, Najran University, King Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
National Advanced IPv6 (NAv6) Centre, Universiti Sains Malaysia (USM), Gelugor 11800, Penang, Malaysia.
Sensors (Basel). 2023 Aug 23;23(17):7342. doi: 10.3390/s23177342.
The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models. Ensemble feature selection combines filter techniques such as variance threshold, mutual information, Chi-square, ANOVA, and L1-based methods. By leveraging the strengths of each technique, the ensemble is formed by the union of selected features. However, this union operation may overlook redundancy and irrelevance, potentially leading to a larger feature set. To address this, a wrapper algorithm called Recursive Feature Elimination (RFE) is applied to refine the feature selection. The impact of the selected feature set on the performance of Deep Learning (DL) models (CNN, RNN, GRU, and LSTM) is evaluated using the IoT-Botnet 2020 dataset, considering detection accuracy, precision, recall, F1-measure, and False Positive Rate (FPR). All DL models achieved the highest detection accuracy, precision, recall, and F1 measure values, ranging from 97.05% to 97.87%, 96.99% to 97.95%, 99.80% to 99.95%, and 98.45% to 98.87%, respectively.
物联网（IoT）改变了我们与技术的交互方式，并带来了安全挑战。物联网攻击数量的不断增加对组织和个人构成了重大威胁。本文提出了一种使用集成特征选择和深度学习模型来检测物联网网络攻击的方法。集成特征选择结合了诸如方差阈值、互信息、卡方检验、方差分析等过滤技术以及基于L1的方法。通过利用每种技术的优势，由所选特征的并集形成集成。然而，这种并集操作可能会忽略冗余和不相关信息，从而可能导致更大的特征集。为了解决这个问题，应用了一种名为递归特征消除（RFE）的包装算法来优化特征选择。使用物联网僵尸网络2020数据集，从检测准确率、精确率、召回率、F1值和误报率（FPR）等方面评估所选特征集对深度学习（DL）模型（CNN、RNN、GRU和LSTM）性能的影响。所有DL模型分别实现了最高的检测准确率、精确率、召回率和F1值，范围从97.05%到97.87%、96.99%到97.95%、99.80%到99.95%以及98.45%到98.87%。
