Intrusion detection based on autoencoder and isolation forest in fog computing

Abstract

Fog Computing has emerged as an extension to cloud computing by providing an efficient infrastructure to support IoT. Fog computing acting as a mediator provides local processing of the endusers' requests and reduced delays in communication between the end-users and the cloud via fog devices. Therefore, the authenticity of incoming network traffic on the fog devices is of immense importance. These devices are vulnerable to malicious attacks. All kinds of information, especially financial and health information travel through these devices. Attackers target these devices by sending malicious data packets. It is imperative to detect these intrusions to provide secure and reliable service to the user. So, an effective Intrusion Detection System (IDS) is essential for the secure functioning of fog without compromising efficiency. In this paper, we propose a method (Auto-IF) for intrusion detection based on deep learning approach using Autoencoder (AE) and Isolation Forest (IF) for the fog environment. This approach targets only binary classification of the incoming packets as fog devices are more concerned about differentiating attack from normal packets in real-Time. We validate the proposed method on the benchmark NSL-KDD dataset. Our technique of intrusion detection achieves a high accuracy rate of 95.4% as compared to many other state-of-Art intrusion detection methods.