Analyzing IoT Attack Feature Association with Threat Actors

Abstract

Internet of Things (IoT) refers to the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. These devices can be controlled remotely, which makes them susceptible to exploitation or even takeover by an attacker. The lack of security features on many IoT devices makes them easy to access confidential information, issue commands from a distance, or even use the compromised device as part of a DDoS attack against another network. Feature selection is an important part of problem formulation in machine learning. To overcome the above problems, this paper proposes a novel feature selection framework RFS for IoT attack detection using machine learning (ML) techniques. The RFS is based on the concept of effective feature selection and consists of three main stages: feature selection, modeling, and attacks detection. For feature selection, three different models are proposed. Based on these approaches, three different algorithms are proposed. A set of 40 features was included in the model, derived from combinatorial optimization and statistical analysis methods. Our experimental study shows that the proposed frame work significantly improves over state-of-the-art cyberattacks techniques for time series data with outliers.