Exploring Deep Learning Architectures for Enhanced Cyber Threat Detection: A Survey

As new organizational innovations and ideal model implementations take place; current methods of interruption detection and protection are becoming outdated. Due to the next flexible innovation, which will transmit incredibly rapid data volumes, new problems will arise for network safety frameworks. Apps on mobile devices are gradually displacing more conventional methods of managing data and information that depend on the web. As a result, there are several threats that could compromise or steal these data and information. A complete system shutdown is possible in the event of one of these attacks. This study introduces a deep learning-based cyber-attack detection system for wireless sensor networks (WSN). This method considers both the actions of the WSN nodes, and the data transmissions made possible by the MQTT protocol. By switching from a traditional machine learning model to a deep learning one, this strategy achieves much better detection accuracy. Using deep learning models that combine real information gathered from network streams, cyberthreats can be identified as digital risks in the network.