Optimized Deep Learning-Based Intrusion Detection and Secure, Energy-Efficient Routing in Wireless Sensor Networks

The growth of WSN and the Internet of Things (IoT) has been phenomenal as a consequence of recent technical developments. The supplier of cloud services experiences maximum delay due to the enormous amount of data processing required. The IoT is made possible by fog computing, which is an unsecured computing technology that supports programs on billions of connected gadgets in an efficiently and economical way. It has the potential to address issues with Internet of Things applications that need location awareness, geolocation, low latency, and movement support. When it involves fog-based WSNs, cybersecurity is of the utmost importance, and IDS (intrusion detection systems) are often built using Deep Learning (DL) models. This method's design allows applications to run on platforms that are near the network's edge, reducing service overhead. A cloud-like system that provides users with preservation, software service, and computer assets. As a new method, it encounters many problems with security and privacy due to the fact that fog gadgets are often located in areas with low levels of protection. The confidentiality of information may be jeopardized by cyberattacks such as port scanning, disruption of service, probes, and man-in-the-middle attacks. The Cuckoo optimization method has shown results that are slightly better, with a precision of 99%. In addition, the Behavior-based IDS approach has shown moderate findings with a 99% success rate.