Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan.
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK.
Sensors (Basel). 2022 May 10;22(10):3607. doi: 10.3390/s22103607.
The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.
物联网 (IoT) 是一种在全球自动化网络系统中广泛使用的技术。近年来，物联网对不同行业的影响已经显现。许多物联网节点收集、存储和处理个人数据，这是攻击者的理想目标。许多研究人员已经研究了这个问题，并提出了许多入侵检测系统 (IDS)。现有系统在提高性能和识别网络攻击子类方面存在困难。本文提出了一种基于深度卷积神经网络 (DCNN) 的 IDS。DCNN 由两个卷积层和三个全连接密集层组成。该模型旨在提高性能和降低计算能力。实验使用了 IoTID20 数据集。通过准确性、精度、召回率和 F1 分数等多种指标对所提出模型的性能进行了分析。对所提出的模型应用了许多优化技术，其中 Adam、AdaMax 和 Nadam 的性能是最优的。此外，还将所提出的模型与各种先进的深度学习 (DL) 和传统机器学习 (ML) 技术进行了比较。所有的实验分析都表明，所提出方法的准确性很高，比现有的基于 DL 的算法更稳健。
