Managing Information Security Risks in the Age of IoT

Abstract

The advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.