Sustainable Ensemble Learning Driving Intrusion Detection Model

Li, Xinghua; Zhu, Mengyao; Yang, Laurence T.; Xu, Mengfan; Ma, Zhuo; Zhong, Cheng; Li, Hui; Xiang, Yang

doi:10.1109/tdsc.2021.3066202

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…Over a past few years, many Intrusion Detection Systems for various communication technologies have been proposed to detect the threats more accurately based on ensemble learning [34], [35], [36], [37], [38], [39], [40], [41].…”

Section: B Ensemble Methodsmentioning

confidence: 99%

“…This approach has been tested on various datasets like KDD Cup 99, NSK-KDD, and Kyoto 2006+ and is able to classify around 95% of the incoming traffic correctly [38]. In [39], the authors propose sustainable ensemble learning to improve the detection rate by aggregating multiclass regression models such that ensemble learning adapts to different attacks. Cloudbased solutions for distributed anomaly detection systems can be found in [40].…”

Section: B Ensemble Methodsmentioning

confidence: 99%

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Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model With Majority Vote Ensemble Algorithm

Upadhyay

Manero

Zaman

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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We propose an integrated framework for an intrusion detection system for SCADA (Supervisory Control and Data Acquisition)-based power grids. Our scheme combines RFEXGBoost (Recursive Feature Elimination -eXtreme Gradient Boosting) based feature selection with a majority vote ensemble method. RFE selects features recursively based on Weighted Feature Importance (WFI) scores during training process, while the majority vote ensemble method predicts the output label based on a total of nine heterogeneous classifiers -three bagging ensembles, namely, Random Forest (RF), Extra Tree (ET), and Decision Tree (DT), three boosting ensembles, namely, XGBoost (XGB), Gradient Boosting (GB), and AdaBoost-Decision Tree (AdB-DT) along with artificial neural network (ANN), Naive Bayes (NB), and k-nearest neighbors (KNN). This leads to a more accurate solution as a result of the combination of the most useful features and prediction from multiple heterogeneous classifiers. Experimental results show that our approach increases the accuracy, precision, recall, F1 score, and decreases the miss rate as compared to previous approaches. The model is also evaluated for four different class categories, namely binary, threeclass, seven class and multi-class, using Precision Recall (PR) and Receiver Operating Characteristic (ROC) plot. In addition, an end-to-end IDS framework is proposed for efficient and accurate detection of intrusions.

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Section: B Ensemble Methodsmentioning

confidence: 99%

Section: B Ensemble Methodsmentioning

confidence: 99%

Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model With Majority Vote Ensemble Algorithm

Upadhyay

Manero

Zaman

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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“…The authors demonstrated the effectiveness of their approach using the well-known dataset, namely CIC-IDS 2018. Li et al [43] designed a novel sustainable EL scheme based on an incremental learning process for multi-class regression models to detect abnormal/malicious behavior in the network. The authors demonstrated the feasibility/effectiveness of their approach using the well-known dataset, namely NSL-KDD.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems

Houda

Brik

Khoukhi

2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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Software-defined network (SDN) is widely deployed on Smart Grid (SG) systems. It consists in decoupling control and data planes, to automate the monitoring and management of the communication network, and thus enabling zero touch management of SG systems. However, SDN-based SG is prone to several security threats and varios type of new attacks. To alleviate these issues, various Machine/Deep learning (ML/DL)based intrusion detection systems (IDS) were designed to improve the detection accuracy of conventional IDS. However, they suffer from high variance and/or bias, which may lead to an inaccurate security threat detection. In this context, ensemble learning is an emerging ML technique that aims at combining several ML models; the objective is to generate less data-sensitive (i.e., less variance) and more flexible (i.e., less bias) machine learning models. In this paper, we design a novel framework, called BoostIDS, that leverages ensemble learning to efficiently detect and mitigate security threats in SDN-based SG system. BoostIDS comprises two main modules: (1) A data monitoring and feature selection module that makes use of an efficient Boosting Feature Selection Algorithm to select the best/relevant SG-based features; and (2) An ensemble learning-based threats detection moel that implements a Lightweight Boosting Algorithm (LBA) to timely and effectively detects SG-based attacks in a SDN environment. We conduct extensive experiments to validate BoostIDS on top of multiple real attacks; the obtained results using NSL-KDD and UNSW-NB15 datasets, confirm that BoostIDS can effectively detect/mitigate security threats in SDN-based SG systems, while optimizing training/test time complexity.

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“…Even though numerous models have been proposed, we observe that only few of them [9], [24] are designed for multi-class attack detection. However, both of them are not considered to be scalable for other cyberattack detection tasks.…”

Section: Tree-based Nidssmentioning

confidence: 99%

“…Third, FED-FOREST chooses Gradient Boosting Decision Tree (GBDT) model as the core classification algorithm. GBDT, as a treebased ML method, is interpretable and efficient compared with the other ML-based approaches like NNs [8], [9]. In the end, for scalability, FEDFOREST can achieve high accuracy on different intrusion detection tasks without modifying the system architecture and model structures.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Federated Learning-based Network Intrusion Detection Framework

Tian¹,

Li²,

Han³

et al. 2022

Preprint

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Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality and quantity of cyberattack data. Such NN-based approaches are also hard to interpret for improving efficiency and scalability. In this paper, we design a new local-global computation paradigm, FEDFOREST, a novel learning-based NIDS by combining the interpretable Gradient Boosting Decision Tree (GBDT) and Federated Learning (FL) framework. Specifically, FEDFOREST is composed of multiple clients that extract local cyberattack data features for the server to train models and detect intrusions. A privacy-enhanced technology is also proposed in FEDFOREST to further defeat the privacy of the FL systems. Extensive experiments on 4 cyberattack datasets of different tasks demonstrate that FEDFOREST is effective, efficient, interpretable, and extendable. FEDFOREST ranks first in the collaborative learning and cybersecurity competition 2021 for Chinese college students.

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Sustainable Ensemble Learning Driving Intrusion Detection Model

Cited by 18 publications

References 38 publications

Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model With Majority Vote Ensemble Algorithm

Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model With Majority Vote Ensemble Algorithm

Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems

An Interpretable Federated Learning-based Network Intrusion Detection Framework

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