AdaBoost-Based Algorithm for Network Intrusion Detection

By Weiming Hu, Wei Hu, S. Maybank. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2008

425 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/736558e7bdc8ebe20b538e4edc227db85235a6b9

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Citations in the corpus

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “AdaBoost has a wide range of applications in several domains such as intrusion detection Hu et al. [108], distributed and scalable online learning [109], bankruptcy forecasting [110], in health sciences [111], demand-driven acquisition (DDA) prediction [112], in sports [113], etc. Adaboost has been…”
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, background): “, bagging [23]–[25], boosting [25], [26], voting [27]–[29], random forest [10], [30], and other ensemble approaches [31]–[34].”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, methodology): “Second, we select some widely studied ensemble algorithms, such as AdaBoost (AB) [39] and Gradient Boosted Machine (GBM) [30] to make a comparison.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[198] tackled the challenge of devising an IDS with fast training time using an Adaboost algorithm.”
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[12] AdaBoost DT KDD-Cup 99 Anomaly detection DR, FA, Run time N/A No Xiang et al.”
Evaluation and Design of Robust Neural Network Defenses (2018, background): “And in the space of intrusion detection, there is a similar story of research on detecting intrusions [137, 138, 54, 47] (and again, even using neural networks [71]) and evading intrusion detection [120, 30].”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “In [24], an ID algorithm using AdaBoost technique was proposed that used decision stumps as weak classiﬁers.”