The immense growth in the cyber world has given birth to various types of cybercrimes in the Internet of things (IoT). Cybercrimes have breached the multiple levels of cybersecurity that is one of the major issues in the IoT networks. Due to the rise in IoT applications, both devices and services are prone to security attacks and intrusions. The intrusion breaches the data packet extracted from different nodes deployed in the IoT network. Most of the intrusive attacks are very near variants of previously marked cyberattacks containing many repetitive data and features. And to detect the intrusion, the data packet needs to be analyzed. This article presents a novel scheme, i.e., dual-axis dimensionality reduction, that utilizes Kalman filter and salp swarm algorithm (coded as KF-SSA) for analyzing and minimizing the data packet. The proposed data reduction scheme is utilized with KELM-based multiclass classifier to efficiently detect intrusion in the IoT network (KF-SSA with KELM). The proposed method’s overall results are evaluated using standard intrusion detection datasets, i.e., NSL-KDD, KYOTO 2006+ (2015), CICIDS2017, and CICIDS2018 (AWS). The result from the proposed data reduction technique obtains highly reduced data, i.e., 70.% for NSL-KDD and 86.43% for CICIDS2017. The analyzed result shows high detection accuracy of 99.9% for NSL-KDD and 95.68% for CICIDS2017 with decreased computational time.

网络世界的巨大增长催生了物联网（IoT）中各种类型的网络犯罪。网络犯罪已经突破了多层次的网络安全性，而这是物联网网络的主要问题之一。由于物联网应用的增长，设备和服务都容易受到安全攻击和入侵。入侵破坏了从物联网网络中部署的不同节点提取的数据包。大多数侵入式攻击非常接近先前标记的网络攻击的变体，其中包含许多重复的数据和功能。为了检测入侵，需要分析数据包。本文提出了一种新颖的方案，即双轴降维，该方案利用卡尔曼滤波器和salp swarm算法（编码为KF-SSA）来分析和最小化数据包。提出的数据缩减方案与基于KELM的多类分类器一起使用，可以有效地检测IoT网络（带有KELM的KF-SSA）中的入侵。使用标准入侵检测数据集，即NSL-KDD，KYOTO 2006+（2015），CICIDS2017和CICIDS2018（AWS），对提出的方法的总体结果进行了评估。所提出的数据缩减技术的结果获得了高度简化的数据，即对于NSL-KDD为70.％，对于CICIDS2017为86.43％。分析结果表明，NSL-KDD和CICIDS2017的检测准确率均高达99.9％，而计算时间却缩短了。CICIDS2017和CICIDS2018（AWS）。所提出的数据缩减技术的结果获得了高度简化的数据，即对于NSL-KDD为70.％，对于CICIDS2017为86.43％。分析结果表明，NSL-KDD和CICIDS2017的检测准确率均高达99.9％，而计算时间却缩短了。CICIDS2017和CICIDS2018（AWS）。所提出的数据缩减技术的结果获得了高度简化的数据，即对于NSL-KDD为70.％，对于CICIDS2017为86.43％。分析结果表明，NSL-KDD和CICIDS2017的检测准确率均高达99.9％，而计算时间却缩短了。