Network Intrusion Detection based of Semi-Supervised Ensemble Learning Algorithm for Imbalanced Data

Abstract Network intrusion detection, which takes the extraction and analysis of network traffic features as the main method, plays a vital role in network security protection. The current network traffic feature extraction and analysis for network intrusion detection mostly uses deep learning algorithms. Currently, deep learning requires a lot of training resources, and have weak processing capabilities for imbalanced data sets. In this paper, a deep learning model (MFVT) based on feature fusion network and Vision Transformer architecture is proposed, to which improves the processing ability of imbalanced data sets and reduces the sample data resources needed for training. Besides, to improve the traditional raw traffic features extraction methods, a new raw traffic features extraction method (CRP) is proposed, the CPR uses PCA algorithm to reduce all the processed digital traffic features to the specified dimension. On the IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that the performance of the proposed MFVT model is significantly better than other network intrusion detection models, and the detection accuracy can reach the state-of-the-art level. And, When MFVT model is combined with CRP algorithm, the detection accuracy is further improved to 99.99%.

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ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) ◽

10.1109/iot-siu.2019.8777504 ◽

2019 ◽

Cited By ~ 9

Author(s):

Abhishek Verma ◽

Virender Ranga

Keyword(s):

Internet Of Things ◽

Intrusion Detection ◽

Ensemble Learning ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Network Intrusion Detection System

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ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

10.36227/techrxiv.11454321 ◽

2020 ◽

Author(s):

Abhishek Verma ◽

Virender Ranga

Keyword(s):

Internet Of Things ◽

Intrusion Detection ◽

Ensemble Learning ◽

Intrusion Detection System ◽

Detection System ◽

Smart Devices ◽

Network Intrusion Detection ◽

The Internet ◽

Routing Attacks ◽

Network Intrusion

Internet of Things is realized by a large number of heterogeneous smart devices which sense, collect and share data with each other over the internet in order to control the physical world. Due to open nature, global connectivity and resource constrained nature of smart devices and wireless networks the Internet of Things is susceptible to various routing attacks. In this paper, we purpose an architecture of Ensemble Learning based Network Intrusion Detection System named ELNIDS for detecting routing attacks against IPv6 Routing Protocol for Low-Power and Lossy Networks. We implement four different ensemble based machine learning classifiers including Boosted Trees, Bagged Trees, Subspace Discriminant and RUSBoosted Trees. To evaluate proposed intrusion detection model we have used RPL-NIDDS17 dataset which contains packet traces of Sinkhole, Blackhole, Sybil, Clone ID, Selective Forwarding, Hello Flooding and Local Repair attacks. Simulation results show the effectiveness of the proposed architecture. We observe that ensemble of Boosted Trees achieve the highest Accuracy of 94.5% while Subspace Discriminant method achieves the lowest Accuracy of 77.8% among classifier validation methods. Similarly, an ensemble of RUSBoosted Trees achieves the highest Area under ROC value of 0.98 while lowest Area under ROC value of 0.87 is achieved by an ensemble of Subspace Discriminant among all classifier validation methods. All the implemented classifiers show acceptable performance results.

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