The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks because of the expanded attack surface from distributed edge and cloud services, the vulnerability of IoT devices, and challenges in managing security across interconnected systems leading to oversights. This led to the rise of ML-based solutions for intrusion detection systems (IDSs), which have proven effective in enhancing network security and defending against diverse threats. However, ML-based IDS in IoT systems encounters challenges, particularly from noisy, redundant, and irrelevant features in varied IoT datasets, potentially impacting its performance. Therefore, reducing such features becomes crucial to enhance system performance and minimize computational costs. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy through the creation of informative features in a two-tier edge-user IoT environment. A hybrid Binary Quantum-inspired Artificial Bee Colony and Genetic Programming algorithm is utilized for this purpose. Three IoT intrusion detection datasets, namely NSL-KDD, UNSW-NB15, and BoT-IoT, are used for the evaluation of the proposed approach.

翻译：物联网应用融入日常生活的趋势导致数据流量激增，带来了显著的安全挑战。采用云计算和边缘计算的物联网应用因分布式边缘与云服务扩大了攻击面、物联网设备的脆弱性、跨互联系统安全管理易出现疏漏等问题，面临更高的网络攻击风险。这促使基于机器学习的入侵检测系统解决方案兴起，其在增强网络安全和防御多样化威胁方面已证明卓有成效。然而，物联网系统中的机器学习入侵检测面临挑战，尤其受限于多样化物联网数据集中存在的噪声、冗余及无关特征，可能影响检测性能。因此，减少此类特征对提升系统性能、降低计算成本至关重要。本文聚焦于提升边缘层机器学习入侵检测系统的有效性，通过在两层级边缘-用户物联网环境中创建信息性特征，提出一种在成本与精度间实现平衡权衡的新方法。为此，采用混合二进制量子启发人工蜂群与遗传编程算法，并利用NSL-KDD、UNSW-NB15和BoT-IoT三个物联网入侵检测数据集对提出的方法进行评估。