IoT based cyber forensics in big data optimization and privacy using deep neural anomaly detection with Hadoop clustering and convolution based Adam optimizer

The reliability of the digital forensic system mainly lies in its ability to detect the attack on time and minimize the security risk associated with the Internet of Things (IoT) device. The existing machine learning architectures mainly suffered from different complexities while processing the attack features such as increased processing time, high manual intervention, complexity to handle large datasets, and low prediction accuracy. To overcome these issues, this article proposes a novel deep learning architecture for data forensics. The low generalization capability of the neural network is handled via the Hadoop clustering and convolution-based ADAM (CADAM) optimizer approach for improving the optimization capability of the model. In this way, the privacy of the model is improved. The efficiency of the proposed CADAM optimized deep neural network architecture is evaluated by using the UNSW-NB15 and Bot-IoT datasets. The proposed model offers improved performance in terms of runtime, precision, sensitivity, specificity, latency, cost, ROCAUC curve, and anomalous traffic when compared with the existing techniques. From the experiments carried out, we can conclude that the proposed framework revealed higher performance to discover and prevent cyber-attacks against other conventional approaches.