Anomaly Detection to Counter DDoS Attacks on Smart Electric Meter Systems

Abstract

Bidirectional communication infrastructure of smart systems, such as, smart grids, are vulnerable to network attacks like Distributed Denial of Services (DDoS) and can be a major concern in the present competitive market. In DDoS attack, multiple compromised nodes in a communication network flood connection request, bogus data packets or incoming messages to targets like database servers, resulting in denial of services for legitimate users. Recently, machine learning based techniques have been explored by researchers to secure the network from DDoS attacks. Under different attack scenarios on a system, measurements can be observed either in an online manner or batch mode and can be used to build predictive learning systems. In this paper, a hybrid deep learning model is developed for detecting replay and DDoS attacks in a real-life smart city platform. The performance of the proposed hybrid model is evaluated using real life smart city datasets (environmental, smart river and smart soil), where DDoS and replay attacks were simulated. The proposed model reported high accuracy rates: 98.37% for the environmental dataset, 98.13% for the smart river dataset, and 99.51% for the smart soil dataset. The results demonstrated an improved performance of the proposed model over other machine learning and deep learning models from the literature.