A Cognitive Framework to Secure Smart Cities

Abstract

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.

Intelligent Mirai Malware Detection for IoT Nodes

“…Based on the features in the dataset, the number of neurons in the input layer can be identified and depending on the output class, the output layer can also be summarized. The main challenge to perform is to identify the number of hidden layers and neurons present in it [30].…”

“…The neuron is a computational unit, which obtains the number of inputs through input wires, performs computation and sends the output via its axon to other nodes or neurons in the brain. In this model, the neuron consists of hidden layers because hidden layers prevent the formation of non-linearity and over-fitting and to achieve computational efficiency, specific neurons are added to hidden layer [30].…”

“…Random Forest comes under the category of Supervised Learning Algorithm. The novelty of Random Forest is that it can be applied to both Classification and Regression approach [30]. In this research, Random Forest is used for Target Prediction, i.e., the amount of accuracy of Mirai malware present in the dataset similar to ANN and additionally, the precision, recall and F-1 score are also predicted and compared with the ANN model.…”