A novel AI-driven intrusion detection framework for cloud-IoT sensor networks
Year: 2026
Type: article
Abstract: Securing network infrastructure against cyberattacks is essential in rapidly evolving digital landscapes. This paper proposes an AI-based real-time intrusion detection system (IDS) for wireless sensor network (WSN), cloud computing (CC), and Internet of things (IoT) environments. The framework employs classical machine learning (ML) models, including decision trees (DTs), K -nearest neighbors (KNN), and logistic regression, optimized using the energy valley optimization (EVO) algorithm on the Network Security Laboratory − Knowledge Discovery in Databases (NSL-KDD) dataset. Feature selection (FS) reduced the number of input features from 42 to 18 without compromising the detection performance. The system achieved high accuracy rates of 98.95% with a decision tree, 98.47% with KNN, and 88.84% with logistic regression, along with high precision, recall, and F1-scores. In addition, the training and testing times were significantly reduced, supporting real-time deployment. Class imbalance was addressed through downsampling to ensure balanced detection across attack types. The proposed model offers a scalable, low-latency, and accurate intrusion detection solution aligned with modern cybersecurity requirements.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7213596358