Adaptive Ensemble Learning with Concept Drift Detection for Intrusion Detection

Mulimani, Deepa; Totad, Shashikumar G.; Patil, Prakashgoud; Seeri, Shivananda V.

doi:10.1007/978-981-16-0171-2_31

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…In contrast, stacked ensembles use the individual model's predictions as inputs to a meta-model, which learns to combine their outputs in a more efficient way. 21,22 Figure 3 demonstrates the overall architecture of the ensemble learning based drift adaptive framework. The initial stage involves gathering time series data from the present to generate a historical dataset.…”

Section: Ensemble Learningmentioning

confidence: 99%

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Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery

Ganesan,

Kaur,

Mittal

et al. 2024

Computational Intelligence

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The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.

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Section: Ensemble Learningmentioning

confidence: 99%

“…Voting ensembles combine the output of each base classifier by taking a majority or weighted vote of their outputs to predict newly arrived data. In contrast, stacked ensembles use the individual model's predictions as inputs to a meta‐model, which learns to combine their outputs in a more efficient way 21,22 …”

Section: Ml‐ Based Drift Adaptive Frameworkmentioning

confidence: 99%

Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery

Ganesan,

Kaur,

Mittal

et al. 2024

Computational Intelligence

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“…Prior studies have looked into methods for identifying idea drift in malware families [8] and warning human analysts when it is found during malware detection. The efficiency of several machine learning properties for detecting fraudulent websites is examined in work [9] However, the use of Host and Content capabilities is the extent of their activity. Extract the Lexical features, as well as the Host and With features based on content, from each URL and then keep them in feature vector form.…”

Section: Related Workmentioning

confidence: 99%

“…Two equal-length sub windows made up sliding window W. From W L to W R , the Kullback-Leibler distance is given by Eq. (9).…”

Section: Secure Adaptive Windowing With Website Data Authentication P...mentioning

confidence: 99%

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Alsuwat¹,

Solaiman²,

Alsuwat³

2023

Computers, Materials &Amp; Continua

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Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers' (and/or benign equivalents') dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.

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“…To deal with Concept Drift, Mulimani et al [145] developed the Adaptive Extreme Gradient Booster (AXGB), an IDS classifier that uses ensemble learning. For the experiment, the KDD-Cup'99 dataset was used as streaming data.…”

Section: G Work Published In 2021mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Adaptive Ensemble Learning with Concept Drift Detection for Intrusion Detection

Cited by 10 publications

References 16 publications

Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery

Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

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