Abstract
The rise in popularity of Internet of Things (IoT) devices has attracted hackers to develop IoT-specific attacks. The microservice architecture of IoT devices relies on the Internet to provide their intended services. An unguarded IoT network makes inter-connected devices vulnerable to attacks. It will be a tedious and ineffective process to manually detect the attacks in the network, as the attackers frequently upgrade their attack strategies. Machine learning (ML)-assisted approaches have been proposed to build intrusion detection for cybersecurity automation in IoT networks. However, most such approaches focus on training an ML model using a single view of the dataset, which often fails to build insightful knowledge and understand each feature’s impact on the ML model’s decision-making ability. As such, the model training with a single view may result in an incomplete understanding of patterns in large feature-set datasets. Moreover, the current approaches are mainly designed in a centralized manner in which the raw data is transferred from the edge devices to the central server for training. This, in turn, may expose the data to all kinds of attacks without adhering to the privacy-preserving of data security. Multi-view learning has gained popularity for its ability to learn from different data views and deliver efficient performance with more distinguished predictions. This paper proposes a federated learning-based intrusion detection approach, called MV-FLID, that trains on multiple views of IoT network data in a decentralized format to detect, classify, and defend against attacks. The multi-view ensemble learning aspect helps in maximizing the learning efficiency of different classes of attacks. The Federated Learning (FL) aspect, wherein the device’s data is not shared to the server, performs profile aggregation efficiently with the benefit of peer learning. Our evaluation results show that our proposed approach has higher accuracy compared to the traditional non-FL centralized approach.
Highlights
There has been a sharp growth in the usage of Internet of Things (IoT) devices in recent years
We proposed a Federated Learning methodology (MV-FLID) to train network data segmented into three views in IoT devices
We consider this set of Machine learning (ML) models as the base ML models of the corresponding views, and the trained ML models will be shared with IoT devices for inference of the attacks on the real-time network data of the device
Summary
There has been a sharp growth in the usage of Internet of Things (IoT) devices in recent years. As the features set can be reduced in multi-view, the training can be more efficient, which leads to detection of attack with more accuracy Having such view-level intelligence can be helpful in the better learning process of attacks and detect abnormal behavior in any view of network data. Considering privacy measures, the aggregation process is made, so the server has no access to devices’ training data Using this paradigm, in this work, we are proposing an intrusion detection technique called Multi-view Federated Learning based Intrusion Detection (MV-FLID) that uses decentralized data for performing training and inference procedures at the device’s end. Following is a list of contributions we made in this research: 1) Proposing an intrusion detection approach with multi-view information of IoT network data using federated learning methodology.
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