Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan.
Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.
Sensors (Basel). 2022 Jun 29;22(13):4926. doi: 10.3390/s22134926.
The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks. To date, numerous intrusion detection systems (IDS) have been designed for IoT networks, using various optimization techniques. However, with the increase in data dimensionality, the search space has expanded dramatically, thereby posing significant challenges to optimization methods, including particle swarm optimization (PSO). In light of these challenges, this paper proposes a method called improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection, introducing a dynamic search space reduction strategy and a number of dynamic parameters to enhance the searchability of sticky binary particle swarm optimization (SBPSO). Through this approach, an IDS was designed to detect malicious data traffic in IoT networks. The proposed model was evaluated using two IoT network datasets: IoTID20 and UNSW-NB15. It was observed that in most cases, IDSBPSO obtained either higher or similar accuracy even with less number of features. Moreover, IDSBPSO substantially reduced computational cost and prediction time, compared with conventional PSO-based feature selection methods.
物联网（IoT）通过创建智能环境来支持人类的努力。尽管物联网为许多人类带来了便利并增加了商机，但它也为入侵者或攻击者打开了大门，他们可以通过攻击或逃避攻击来利用这项技术。因此，安全性和隐私性是物联网网络的关键关注点。迄今为止，已经使用各种优化技术为物联网网络设计了许多入侵检测系统（IDS）。然而，随着数据维度的增加，搜索空间已经大大扩展，从而对包括粒子群优化（PSO）在内的优化方法提出了重大挑战。有鉴于此，本文提出了一种称为改进动态粘性二进制粒子群优化（IDSBPSO）的特征选择方法，引入了动态搜索空间缩小策略和许多动态参数，以增强粘性二进制粒子群优化（SBPSO）的搜索能力。通过这种方法，设计了一种入侵检测系统来检测物联网网络中的恶意数据流量。使用两个物联网网络数据集：IoTID20 和 UNSW-NB15 对所提出的模型进行了评估。结果表明，在大多数情况下，IDSBPSO 即使使用较少的特征，也能获得更高或相似的准确性。此外，与基于传统 PSO 的特征选择方法相比，IDSBPSO 大大降低了计算成本和预测时间。
