AdaBoost-Based Algorithm for Network Intrusion Detection

Abstract

Abstract-Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.Index Terms-AdaBoost, computational complexity, detection rate, false-alarm rate, intrusion detection.

Building an efficient intrusion detection system based on feature selection and ensemble classifier

“…our voting classifier. Second, we select some widely studied ensemble algorithms, such as AdaBoost (AB) [39] and Gradient Boosted Machine (GBM) [30] to make a comparison. Third, some single classifiers like k-Nearest Neighbor (kNN) [55], Classification and Regression Trees (CART) [17], and Multi-Layer Perceptron (MLP) [52] have been chosen as well.…”

Quantity Detection of Steel Bars Based on Deep Learning

“…The traditional target detection algorithm consists of the extraction of candidate regions of the target and the classification of the candidate regions. Therefore, the traditional target detection algorithm differs by the difference between the two parts, wherein for the target candidate region extraction part, It is divided into sliding window based detection such as DPM [1], and detection based on texture feature extraction, such as Selective Search [2], and the classification of the extracted candidate regions, such as AdaBoost [3], SVM [4], Decision Tree [5], Random Forest [6], etc. structure.…”

HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

“…Similar to the previous work, accuracy is used as performance evaluation and the proposed approach is implemented on the full features set of KDDCup 99 dataset. A classifier ensemble, called Adaboost is used to improve the performance of decision stump [7]. Two performance metrics, i.e.…”