An Intrusion Detection Algorithm Based on Decision Tree Technology

Wang, Juan; Qin, Yang; Ren, Dasen

doi:10.1109/apcip.2009.218

Cited by 27 publications

(16 citation statements)

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“…At present, most studies use standard public datasets KDD-Cup 99 and NSL-KDD for research; meanwhile, a variety of detection schemes were proposed using traditional machine learning algorithms. Wang et al [8] proposed a decision tree-based intrusion detection algorithm. In their experiments, the C4.5 algorithm could achieve great detection accuracy, but there was still a certain error rate.…”

Section: Nids Based On Traditional Machine Learningmentioning

confidence: 99%

Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis

Gao

Liu

et al. 2020

Sensors

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Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional anomaly detection systems to effectively analyze and identify abnormal traffic. At present, deep neural network (DNN) technology achieved great results in terms of anomaly detection, and it can achieve automatic detection. However, there still exists misclassified traffic in the prediction results of deep neural networks, resulting in redundant alarm information. This paper designs a two-level anomaly detection system based on deep neural network and association analysis. We made a comprehensive evaluation of experiments using DNNs and other neural networks based on publicly available datasets. Through the experiments, we chose DNN-4 as an important part of our system, which has high precision and accuracy in identifying malicious traffic. The Apriori algorithm can mine rules between various discretized features and normal labels, which can be used to filter the classified traffic and reduce the false positive rate. Finally, we designed an intrusion detection system based on DNN-4 and association rules. We conducted experiments on the public training set NSL-KDD, which is considered as a modified dataset for the KDDCup 1999. The results show that our detection system has great precision in malicious traffic detection, and it achieves the effect of reducing the number of false alarms.

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Section: Nids Based On Traditional Machine Learningmentioning

confidence: 99%

Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis

Gao

Liu

et al. 2020

Sensors

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“…Juan Wang et.al, [4] introduced an intrusion detection system based on decision tree technology. In the procedure of building intrusion rules, data gain ratio is utilized as a part of information gain.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Performance of Network Intrusion Detection System by Combining Naïve Bayes, Decision Tree and K-Nearest Neighbors Algorithms

Befekadu¹

2018

IJCA

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Protecting the hostile network environment is a very difficult task. Although, there is no way to protect the network for hundred percent accuracy, so many researches tried to achieve the best security mechanisms for long time. Among the security mechanisms, network intrusion detection system is one of the well-known. The performances of the network intrusion detection systems that are developed have produce so many false alarm. To improve this false alarm rate this research combines three algorisms which are Naïve Bayes, Decision Tree and k-NN. The results found from the experiment showed that the combined algorithm improve the accuracy of the network intrusion detection system by up to 5%. General TermsSecurity, Algorithm

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“…Juan Wang et al, in their work [17] proposed a decision tree based algorithm for intrusion detection, even if during their experiments the C4.5 algorithm was achieving a good detection accuracy, the error rate was remaining identical.…”

Section: Description Of Nsl-kddmentioning

confidence: 99%

A Subset Feature Elimination Mechanism for Intrusion Detection System

Nkiama¹,

Said²,

Saidu³

2016

ijacsa

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Abstract-several studies have suggested that by selecting relevant features for intrusion detection system, it is possible to considerably improve the detection accuracy and performance of the detection engine. Nowadays with the emergence of new technologies such as Cloud Computing or Big Data, large amount of network traffic are generated and the intrusion detection system must dynamically collected and analyzed the data produce by the incoming traffic. However in a large dataset not all features contribute to represent the traffic, therefore reducing and selecting a number of adequate features may improve the speed and accuracy of the intrusion detection system. In this study, a feature selection mechanism has been proposed which aims to eliminate non-relevant features as well as identify the features which will contribute to improve the detection rate, based on the score each features have established during the selection process. To achieve that objective, a recursive feature elimination process was employed and associated with a decision tree based classifier and later on, the suitable relevant features were identified. This approach was applied on the NSL-KDD dataset which is an improved version of the previous KDD 1999 Dataset, scikit-learn that is a machine learning library written in python was used in this paper. Using this approach, relevant features were identified inside the dataset and the accuracy rate was improved. These results lend to support the idea that features selection improve significantly the classifier performance. Understanding the factors that help identify relevant features will allow the design of a better intrusion detection system.

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An Intrusion Detection Algorithm Based on Decision Tree Technology

Cited by 27 publications

References 0 publications

Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis

Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis

Enhancing the Performance of Network Intrusion Detection System by Combining Naïve Bayes, Decision Tree and K-Nearest Neighbors Algorithms

A Subset Feature Elimination Mechanism for Intrusion Detection System

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