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D. R

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Open access Jul 2026

NEXT-GEN-IDS: A DISTRIBUTED SMART NETWORK USING HYBRID AI MODELS

The rapid expansion of Internet of Things (IoT) devices has introduced significant security challenges, creating a growing need for advanced Intrusion Detection Systems (IDS) capable of identifying cyber threats in real time. Traditional IDS solutions often struggle to cope with the dynamic and heterogeneous nature of modern IoT environments. As a result, Artificial Intelligence (AI)-based approaches have emerged as promising alternatives due to their ability to learn complex patterns and adapt to evolving threats. This study investigates the integration of Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy, efficiency, and adaptability of IDS in IoT networks. A Next Generation AI-based IDS is proposed to detect and classify various types of cyberattacks. The framework combines anomaly detection, behavioral analysis, and malicious pattern recognition to identify and mitigate security threats with minimal latency. To improve attack detection, a pre-trained Adaptive Recurrent Neural Network (A-RNN) is employed to effectively extract attack patterns from network traffic data. These extracted patterns are then processed using a hybrid Stacked Long Short-Term Memory (S-LSTM) and Convolutional Neural Network (CNN) architecture for accurate attack classification. The effectiveness of the proposed model is evaluated using two real-world datasets, namely the BETH Dataset and the IoT-23 Dataset. Experimental results demonstrate that the proposed AI-driven IDS achieves superior performance compared to existing approaches, highlighting its potential as a robust and efficient solution for securing IoT environments against emerging cyber threats.

D. G, D. R, Sushmitha J et al. · 0 citations
Conference Jul 2026

An Efficient EEG-based System for Multi-Class Neurological Disorder Diagnosis using EMPA Optimized Bi-LSTM

Neurological illness detection using electroencephalograms (EEGs) is not an easy task due to the nature of brain signals, which are not stationary and noisy. In order to obtain the correct multi-class classification, the paper develops a hybrid deep learning framework that includes pre-processing, graph-based feature generation and efficient time modelling. The initial step of cleaning the data is to eliminate any missing data, or artefacts in EEG signals to obtain higher resulting output. A Graph Convoluted Network (GCN) is then used to approximate the functional connectivity between the EEG electrodes and each of the channels is represented as a node in a modelled graph. The GCN models spatial relationships and gets trained on features that are of relevance to the neurological patterns. The Bidirectional Long Short-Term Memory (BiLSTM) network receives features to acquire forward and backward statistical relationships. To enhance the convergence and speed of the weight selection process, the Enhanced Marine Predators Algorithm (EMPA) is applied to avoid falling into the local minima. Proposed model is more effective with higher accuracy of precision ,recall value and F1-score value. The experimental results demonstrating a sufficient recall of spatio-temporal behaviour and classification performance make the proposed model useful to be used in clinical decision support systems.

Dency Flora G, D. R, M. S. et al. · 0 citations