A Comparative Study on a Multi-Source Hybrid Machine Learning Framework Integrating Deep Learning and Swarm Intelligence for Intelligent IoT Forensics and Threat Classification

The exponential rise of the Internet of Things (IoT) has led to the interchange of vast amounts of data between network-connected devices. There are many security lapses and breaches as a result of the wide-ranging connectivity between IoT devices. Numerous benefits come with the quick expansion of IoT devices, but new security and forensics difficulties also arise. Digital investigators and practitioners face significant obstacles when interacting with IoT devices to probe cybercrimes in a timely and forensically sound manner, as a result of the vast amount of evidences generated by the billions of devices in the IoT system. The study's primary goal is to create a framework that performs forensic investigation on resource-constrained IoT using a combination of forensic technologies and machine learning to detect various kinds of attacks. The feasibility of Deep Neural networks (DNNs) in IoT Forensics (IoTF) is examined in this study to detect the attacks using the operating system logs. This work suggests utilizing an optimum set of parameters to train a Salps Swarm Optimization Algorithm (SSOA) for DNN. The suggested SSOA-DNN method is compared with the ML classifiers including KNN, RF, SVM, DT, LDA and NB Classifiers. The following metrics are used to evaluate how effective ML models are: (1) Accuracy, (2) Precision, (3) Recall, and (4) F-Measure. The results show that the SSOA-DNN outperforms with an accuracy of 96.37% than the other ML classification algorithms in IoTF Analysis.