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 resourceconstrained
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.