Intrusion Detection and Classification using Deep Learning and Nature Inspired Optimization for IOT Based Network

A Deep Learning-Based Framework for Cyber Attack Detection in IoT Networks

An intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious, and demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.

Federated Learning-Enabled Intrusion Detection with Bio-Inspired Feature Optimization and Hybrid Deep Neural Classifier

The growth of Internet of Things (IoT) networks has drastically improved attack surface, requiring intrusion detection systems (IDS) to ensure accuracy and privacy protection. To overcome these obstacles, we introduce a federated learning (FL) based IDSW that incorporates state-of-the-art preprocessing, smart feature optimization, and a new classification paradigm. During preprocessing, raw traffic data is subject to scrubbing at a vigorous level, normalization through scaling, and label encoding to maintain consistency and reduce noise in heterogeneous local datasets. For feature selection, the Hybrid Emperor Penguin–Quokka Swarm Optimization (HEPQSO) approach is utilized which balances exploitation and exploration to find the most discriminative features while addressing the dimensionality problem. These features are then utilized by a deep hybrid classifier where the Spike Gated Linear Unit (SGLU) facilitates non-linear representation learning, and a Vision Transformer-Temporal Convolutional Network (ViT–TCN) hybrid discovers both global spatial relationships and local temporal dynamics of intrusion patterns. Experimental analyses performed using benchmark intrusion detection datasets show that the system has a high performance compared to baseline models at all times, with an accuracy of 97.88%, precision of 96.16%, recall of 97.54%, F1-score of 97.39%, specificity of 97.62%, and MCC of 97.04%, thus proving its efficiency for safe IoT settings. This combination of state-of-the-art preprocessing, hybrid feature selection, and deep federated classification forms a robust IDS that can tackle the changing landscape of cyber intrusions.

An Effective Hierarchical Anomaly Based Intrusion Detection System for IoT Using Ant Colony Optimization Based Feature Selection

Artificial Intelligence-Based Intrusion Detection Systems for Cloud and Internet of Things (IoT) Environments

The exponentially increasing number of IoT devices and their corresponding cloud infrastructures increases the attack surface․ Classic rule-based schemes and cryptographic solutions are not well adapted to dynamic‚ heterogeneous‚ distributed‚ and resource-constrained IoT-cloud infrastructures․ Artificial intelligence (AI) based techniques such as machine learning (ML)‚ deep learning (DL) and federated learning (FL)‚ considered as a new model for intrusion detection systems (IDS) to assess the threats in real time and respond to the threats effectively in the dynamic environment․ This paper thoroughly reviews the state-of-the-art AI-based IDS in a layer-wise manner which consists of IoT and cloud stacks․ It categorizes popular cyber-attacks associated with each layer (perception‚ network‚ transport‚ processing and application) and correlate the attacks to machine learning classifiers (SVM‚ KNN‚ Random Forest‚ CNN‚ RNN/LSTM‚ Autoencoder) at shallow and deep learning levels․ The research incorporates federated IDS‚ graph neural networks‚ transformer architecture and explainable artificial intelligence as models of machine learning and further discusses prominent research issues (data scarcity‚ adversarial robustness‚ latency‚ transferability) and the future direction of next-generation IoT-cloud security․

An Intelligent Hybrid Deep Learning Framework for Intrusion Detection in 5G-Based IIoT Networks

An intelligent hybrid deep learning framework based on a combination of deep neural networks (DNNs) and random forests (RF) to ensure the security of 5G IIoT networks for use in critical areas such as smart factories, cyber-physical systems, power grids, and industrial automation.

A Hybrid Deep Learning Model for Efficient IOT Botnet Attack Detection

The proposed hybrid framework provides a robust, scalable, and reliable solution for real-time botnet attack detection, enhancing the security and resilience of modern IoT networks against evolving cyber threats.