TrustEdgeAI: A Lightweight, Calibrated, and Secure Deep Learning Framework for Intrusion Detection in Edge-IoT Environments - S Karthika; Juliana K. Gnanaselvi; Sivakumar N. Sellappan; M Gokilavani; Hasan Koten (Indonesian Artificial Neural Network Society, 2026)
The fast development of Edge-IoT systems has posed serious security threats because of its distributed and resource-limited characteristics. The conventional intrusion detection systems are normally ineffective in offering real time and accurate security against the changing cyber threats. To overcome this challenge, this paper presents TrustEdgeAI, a lean and safe deep learning system, which is specialized in effective intrusion detection in Edge-IoT systems. The proposed model will combine a hybrid CNN-LSTM to learn spatial and temporal information of network traffic as well as an adaptive pruning mechanism of features to achieve a reduction in the computational complexity. Moreover, a calibration method based on temperature scaling is also included in order to enhance the credibility of prediction probability in order to make reliable decisions. The experiment outcomes prove that TrustEdgeAI has a high detection rate of 97.8 and much less inference latency of 12 ms and resource use. The framework also has low rates of false positive on various types of attacks and can be very applicable in real time and secure edges deployment.