Network Intrusion Detection System with Stream Machine Learning in Fog Layer and Online Labeling in Cloud Layer
Journal
2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021
Pages
53-59
Date Issued
2021
Author(s)
Abstract
We proposed a network intrusion detection system that combines a stream machine learning model in the fog layer and an online labeling model in the cloud layer. The stream learning model is based on the Adaptive XGBoost machine learning algorithm, aiming to detect anomaly network traffic. The online labeling model is a batch machine learning model based on the Random Forest algorithm and is responsible to label unknown traffic and provide updates to the stream learning model in the fog layer. The proposed solution effectively detects abnormal traffic in the fog layer that is connected with IoT devices. The stream learning model updates the model at a lower cost as compared to the batch learning approach. To evaluate the proposed system, contemporary datasets are used to test the accuracy of the models. The experiment results show that the proposed scheme effectively achieves good classification accuracy with the cloud layer providing updates to the fog layer. The result is about 17.6% and 9.0% better than the baseline method for the UNSW-NB15 dataset and CIC-IDS2017 dataset, respectively. In addition, the stream learning approach can provide higher throughput than the batch learning approach. © 2021 IEEE.
Subjects
cloud layer; cybersecurity; fog layer; Internet of Things (IoT)
SDGs
Other Subjects
Computer crime; Decision trees; E-learning; Fog computing; Internet of things; Intrusion detection; Learning algorithms; Machine learning; Batch learning approach; Cloud layers; Cyber security; Fog layer; Internet of thing; Machine learning models; Machine-learning; Network intrusion detection systems; Online labeling; Fog
Type
conference paper