Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid

Khoei, Tala Talaei; Aissou, Ghilas; Hu, When Chen; Kaabouch, Naima

doi:10.1109/eit51626.2021.9491891

Cited by 52 publications

(27 citation statements)

References 25 publications

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“…A low detection rate and high false alarms are the current issues when employing IDS to detect malicious activities in any field. To improve the smart grid's security and reduce high false alarms, Khoei et al [164] investigate ensemble learning methods, i.e., bagging-based, boosting-based, and stacking-based, over the CIC-DDoS2019 benchmark dataset that contains a lot of DDoS attacks for anomaly IDS. The impact of two different methods of feature selection, namely Pearson's Correlation Coefficient, which computes the strength of the linear relationship between two characteristics whose values are between -1 and 1 and Extra Tree Classifier, which only rates the relevance of characteristics and deletes unnecessary ones from the dataset [165], is investigated.…”

Section: F Smart Grid Forensicsmentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

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Network forensics focuses on the identification and investigation of internal and external network attacks, the reverse engineering of network protocols, and the uninstrumented investigation of networked devices. It lies at the intersection of digital forensics, incident response and network security. Network attacks exploit software and hardware vulnerabilities and communication protocols. The scope of a network forensic investigation can range from Internet-wide down to a single device's network traffic. Network analysis tools (NATs) aid security professionals and law enforcement in the capturing, identification and analysis of network traffic. However, in most instances, the sheer volume of data to be analyzed is enormous and, despite some built-in NAT automation, the investigation of network traffic is often an arduous process. Furthermore, significant expert time remains wasted in the investigation of a high frequency of false positive alerting from automated systems. To address this globally impacting problem, artificial intelligence based approaches are becoming increasingly employed to automatically detect attacks and increase network traffic classification accuracy. This paper provides a comprehensive survey of the state-of-the-art in network forensics and the application of expert systems, machine learning, deep learning, and ensemble/hybrid approaches to a range of application areas in the field. These include network traffic analysis, intrusion detection systems, Internetof-Things devices, cloud forensics, DNS tunneling, smart grid forensics, and vehicle forensics. In addition, the current challenges and future research directions for each of the aforementioned application areas is discussed.

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Section: F Smart Grid Forensicsmentioning

confidence: 99%

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

et al. 2022

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“…There are instances where traditional machine learning models do not achieve high accuracy, [17] Several ensemble-based techniques have been developed over time, but the most popular are bagging, boosting, stacking generalization, and expert mixture [16].…”

Section: Ensemble Learningmentioning

confidence: 99%

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

Agbedanu¹,

Musabe²,

Rwigema³

et al. 2022

Preprint

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Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

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“…computes the trade-off between precision and recall. Mathematically, it is the harmonic mean of precision and recall as shown in Equation (10).…”

Section: P Recision = T P T P + F Pmentioning

confidence: 99%

“…[22], [23]. Many state-of-the-VOLUME 4, 2016 [6] KNN, NB, RF, SVM IDS 98.86 ≈ 99.54 (Accuracy) CICDDoS2017 Ullah et al [7] NB, LR, DT, RF IoT 99.99 ≈ 100 (F1-score) IoT Botnet Gohil et al [8] DT, NB, LR, SVM, KNN IDS 97.72 ≈ 99.99 (Accuracy) CICDDoS2019 Alamri et al [9] XGBoost SDN 99.9 (Accuracy) CICDDoS2019 Khoei et al [10] Stacking, Bagging, Boosting Smart Grid 92.2 ≈ 93.4 (Accuracy) CICDDoS2019 Parfenov et al [11] Gradient Boosting IDS 96.8 (F1-score) CICDDoS2019 Parfenov et al [11] CatBoost IDS 96.9 (F1-score) CICDDoS2019 Sanchez et al [12] Random Forest IoT 99.97 (Accuracy) CICDDoS2019 Varghese et al [13] D3 SDN 84.54 (Accuracy) CICDDoS2019 Pontes et al [14] EFC IDS 97.5 (F1-score) CICDDoS2019…”

Section: Introductionmentioning

confidence: 99%

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

Wei¹,

et al. 2021

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Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multilayer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.

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Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid

Cited by 52 publications

References 25 publications

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions

A Lightweight Intrusion Detection for Internet of Things Using Incremental Ensemble Learning

AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

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