An Intelligent Framework for Malware Detection in Internet of Things (IoT) Ecosystem
Osaretin Eboya, Julia Binti Juremi, Maryam Shahpasand · 2020
The Heuristic-Based Analysis method, which is otherwise known as Behavioural-Based Analysis technique applied by several Anti-Virus and Anti-Malware vendors of computer network systems has not been sufficient. There are gaps in the field of research to develop a novel approach to solving the inherent and existential threats and attacks in the interconnected computer network systems, especially in the fast growing IoT Ecosystems that has now become ubiquitous to everyday life. We proposed and presented the experimental implementation of an intelligent framework, including the realization of the Multilayer Perceptron (MLP) with multiple hidden layers by exploiting the capabilities of Hierarchical Extreme Learning Machine (H-ELM). This has the preeminent underexplored potentials for accelerated speed, rapid feature learning, training, improved classification performance, and accuracy of Detecting, Recognizing, and Predicting (DRP) anomalies in an Internet of Things (IoT) ecosystem. The experimental implementation demonstrated the effectiveness of the proposed Intelligent DRP-Framework also known as the iDRP-Framework using the generalized MLP technique for demonstrating how a non-traditional malware detection mechanism can be applied to dynamically and intelligently Detect, Recognize, and Predict malwares in the generated big dataset of an IoT ecosystem through converted image file, such as the Log files, and Binary dataset to create machine learning model. The result indicates that the proposed iDRP-Framework delivered significant performance and accuracy in the detection of anomalies in an interconnected computer network system.