Nazir Anjum, Memon Zulfiqar, Sadiq Touseef, Rahman Hameedur, Khan Inam Ullah
Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75123, Pakistan.
Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway.
Sensors (Basel). 2023 Sep 28;23(19):8153. doi: 10.3390/s23198153.
The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system's overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.
物联网（IoT）和支持网络的智能设备对于当今数字化互联社会至关重要。然而，对物联网设备的依赖增加，使其在网络流量中更容易受到恶意活动的影响，给网络安全带来了重大挑战。因此，系统管理员和终端用户都会受到这些恶意行为的负面影响。入侵检测系统（IDS）通常作为一种网络攻击防御机制来部署，以减轻此类风险。IDS在识别和预防物联网网络中的网络危害方面起着至关重要的作用。然而，开发一种高效快速的用于检测网络攻击的IDS系统仍然是一个具有挑战性的研究领域。此外，IDS数据集包含多个特征，因此需要实施特征选择（FS）来设计一个有效且及时的IDS。FS过程旨在从大型IDS数据集中消除不相关和冗余的特征，从而提高入侵检测系统的整体性能。在本文中，我们提出了一种基于混合包装器的特征选择算法，该算法基于元胞自动机（CA）引擎和基于禁忌搜索（TS）的期望准则的概念。我们使用随机森林（RF）集成学习分类器来评估所选特征的适用性。所提出的算法CAT-S在TON_IoT数据集上进行了测试。仿真结果表明，所提出的算法CAT-S提高了分类准确率，同时减少了特征数量和误报率。
