Novel hybrid deep learning based cyber security threat detection model with optimization algorithm

In order to continuously provide services to the company, the Internet of Things (IoT) connects the hardware, software, storing data, and applications that could be utilized as a new port of entry for cyber-attacks. The privacy of IoT is presently very vulnerable to virus threats and software piracy. Threats like this have the potential to capture critical data, harming businesses' finances and reputations. We have suggested a hybrid Deep Learning (DL) strategy in this study to identify malware-infected programs and files that have been illegally distributed over the IoT environment. To detect illegal content utilizing Source code (SC) duplication, the Adaptive TensorFlow deep neural network with Improved Particle Swarm Optimization (IPSO) is suggested. This novel hybrid strategy improves cyber security by fusing cutting-edge DL with optimization methods, providing more effective and accurate detection. With a strong solution for real-time threat identification, the model handles the complexity of contemporary cyberthreats. To highlight the significance of the proxy regarding the SC duplication, the noisy data is filtered using the tokenization and weighting feature approaches. After that, duplication in SC is found using a DL method. To look into software piracy, the dataset was gathered via Google Code Jam (GCJ). Additionally, using the visual representation of color images, the Enhanced Long Short-Term Memory (E-LSTM) was employed to identify suspicious actions in the IoT environment. The Maling dataset is used to gather the malware samples required for testing. The experimental findings show that, in terms of categorization, the suggested method for evaluating cybersecurity threats in IoT surpasses conventional approaches.