With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%.

凭借简单的连接性以及对智能设备和网络的快速增长的需求，物联网变得更容易受到网络攻击。为了检测和预防IoT网络中的网络攻击，入侵检测系统（IDS）扮演着至关重要的角色。但是，大多数现有的IDS具有维度诅咒，从而降低了整个IoT系统的效率。因此，重要的是在设计有效的IDS时删除重复的和不相关的功能。基于上述挑战，本文提出了一种使用新型混合特征缩减方法的物联网网络智能网络攻击检测系统。该技术首先使用相关系数，随机森林均值降低精度和增益比执行特征排名，以获得三个不同的特征集。然后，使用适当设计的机制（“与”运算）组合要素，以获得单个优化的要素集。最后，将获得的缩减特征集馈送到三种著名的机器学习算法（例如随机森林，K近邻和XGBoost）中，以检测网络攻击。使用NSL-KDD和两个最新的基于IoT的数据集，即BoT-IoT和DS2OS，评估了建议的网络攻击检测框架的效率。在准确性，检测率（DR），精度和F1得分方面，对提出的框架的性能进行了评估，并与文献中发现的一些最新技术进行了比较。使用这三个数据集的性能分析表明，对于大多数与正常行为非常相似且准确度高于99％的攻击向量，该模型已实现高达90％–100％的DR。