Hybrid Deep Learning Framework for Real-Time Cyberattack Detection in IoT-Enabled Smart Networks

Abstract

Internet of Things (IoT) technologies have revolutionized the world of communication by allowing a seamless integration of smart devices, sensors, and intelligent systems in present-day communication infrastructure. Critical areas like smart cities, energy, transportation, healthcare and industrial automation are adopting smart networks with IoT technology, generating fresh automation and data-driven decision opportunities. But the large-scale connectivity and varying nature of IoT environments has made them highly susceptible to attacks such as denial-of-service (DDoS), malware and botnet attacks, data breaches, and attempt to access without authorization. The volume, velocity and victimization of network traffic produced by IoT devices place a significant burden on traditional security mechanisms for detecting, realtime, advanced and evolving threats. In advanced AI/Deep Learning, the technology has shown its potential in detecting cyber attacks and improving network security.Thus, advanced AI/Deep learning has come up as a promising solution for intelligent cyberattack detection and network security enhancement. Multi-modal deep learning architectures, which leverage multiple learning paradigm (such as CNN, RNN, LSTM, and autoencoder) achieve stronger abilities to extract complex traffic patterns and detect malicious traffic with high accuracy. These tools enable you to detect threats in real-time, adaptively learn networks, identify anomalies, and perform predictive security analysis in a dynamic network environment. This research is to investigate hybrid deep learning methods to detect real-time cyberattacks in the IoT-enabled smart network. It examines the current security issues, analyzes various deep learning architectures, and assesses the ability of these architectures to enhance the accuracy of detection and   enhance the resilience of a network. The results provide valuable insights for understanding future implications of intelligent security architectures and their contribution to creating secure, adaptive and trustworthy smart IoT systems in an increasingly connected digital world.