iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem

Abstract

The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware attacks and threats security solution is becoming predominant in the current cyberspace. An integrated and scalable IoT malware detection framework called iDRP framework with deep learning method was proposed as a solution to current IoT malware attacks that are largely obfuscated. The novel framework utilized systematic pre-processing and post-processing techniques and methods on the BoTNetIoT malware datasets that contains both benign and malicious IoT traffic data infected by modern day IoT attacks such as Mirai and Gafgyt etc. IoT malware variants in an IoT ecosystem. The raw IoT malware binaries were converted to image files (Gray-scaled) and computed statistically with synthesised sparsed and differential evolutionary hidden feature structures techniques, which were cyclically trained, tested, and cross-validated to establish empirical anomalies with precision in the detection, recognizing, and prediction of malware anomalies in a modern IoT ecosystem. Preliminary experiments were conducted with standardized image binary files such as the datasets as sound scientific exploratory experiments with profound results. The comparative results of the performance of our integrated techniques and methods on the BoTNetIoT IoT malware datasets achieved a 99.98% accuracy, 99.99% ROC/AUC, 99.95% precision, and 99.93 recall rate etc. utilizing the integrated iDRP framework mechanisms for effectively detecting IoT malware in an IoT ecosystem.