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.

本文介绍了一种在边缘用户物联网环境下改进机器学习型入侵检测系统的方法，通过创建信息性特征来在成本和准确性之间实现平衡，并利用混合二进制量子启发式人工蜜蜂群算法和遗传规划算法来减少冗余特征。其中，NSL-KDD、UNSW-NB15 和 BoT-IoT 三个物联网入侵检测数据集被用于评估该方法。