Smart Cities Driven by IoT: Improving Attack Detection with Cloud-Enabled Cybersecurity
Borse Pradnya Balasaheb · Advances in Nonlinear Variational Inequalities · 2024
The susceptibility of smart equipment to sophisticated cyber threats, such as Distributed Denial of Service (DDoS) assaults, makes anomaly detection in smart cities essential in the current era of information technology. A machine learning-driven method is presented to tackle this problem, which makes use of the power consumption patterns of Internet of Things (IoT) devices to spot unusual activity in smart home settings. The training model is implemented near IoT layer devices by utilizing a distributed fog network. To gather statistics on power utilization during daily activities, a Raspberry Pi prototype smart camera has been constructed. To produce power consumption patterns suggestive of DDoS assaults, the study also investigates anomaly identification using Artificial Neural Network (ANN) approaches. ANNs are used to simulate DDoS attacks on an experimental setup. With an average detection accuracy of 78.67% across a range of assault situations, the results demonstrate that a deep feed-forward neural network model works better than previous models. This strategy puts less demand on Internet of Things devices with limited resources, which qualifies it for use in smart city applications.