Cyber forensics framework for big data analytics in IoT environment using machine learning

Forensic analyst skills are at stake for processing of growing data from IoT based environment platforms. Tangible sources often have the size limits, but that's not the case for communication traffic source. Hence, increasing the thirst for an efficient benchmarking for big data analysis. Available solutions to date have used an anomaly-based approach or have proposed approaches based on the deviation from a regular pattern. To tackle the seized bytes, authors have proposed an approach for big data forensics, with efficient sensitivity and precision. In the presented work, a generalized forensic framework has been proposed that use Google's programming model, MapReduce as the backbone for traffic translation, extraction, and analysis of dynamic traffic features. For the proposed technique, authors have used open source tools like Hadoop, Hive, and Mahout and R. Apart from being open source, these tools support scalability and parallel processing. Also, comparative analysis of globally accepted machine learning models of P2P malware analysis in mocked real-time is presented. Dataset from CAIDA was taken and executed in parallel to validate the proposed model. Finally, the forensic performance metrics of the model shows the results with the sensitivity of 99%.