A Review of the Advancement in Intrusion Detection Datasets

Thakkar, Ankit; Lohiya, Ritika

doi:10.1016/j.procs.2020.03.330

Cited by 179 publications

(110 citation statements)

References 24 publications

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“…Despite not showing a significant difference between the results of the two algorithms that obtained a better performance, the authors pointed out that the classification speed was much higher after applying the PCA technique in reducing the dimensionality of the NSL-KDD dataset [9]. Thus, the need for optimizing the dataset is confirmed according to [16], [45], [52]- [54].…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

et al. 2021

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This work is supported by the Academy of Finland: (a) ee-IoT n.319009, (b) EnergyNet n.321265/n.328869 and (c) FIREMAN n.326270/CHIST-ERA-17-BDSI-003, and the Brazilian National Council for Scientific and Technological Development (CNPq). We would like to thank Hanna Niemelä for helping to proofread this paper.ABSTRACT Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

et al. 2021

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“…An intrusion detection dataset can be established by collecting network traffic features from different sources, such as network traffic flows containing information about the host, user behavior, and system configurations [81][82][83]. This information is required to study the attack patterns and abnormal activity of various network attacks.…”

Section: Intrusion Detection Datasetmentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection

Salih

Ameen

Zeebaree

et al. 2021

AJRCoS

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Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

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“…Input data are a crucial factor in producing a decent classification. Based on research surveys conducted in [1][2][3], the progress of optimizing the intrusion detection models with the concept of machine learning requires a reliable dataset. Malowidzki et al [1] argue that the lack of a proper dataset for research causes difficulty in evaluating methods and comparing the performance of research results.…”

Section: Introductionmentioning

confidence: 99%

“…The NSL-KDD dataset has comparable data and labels in its evaluation, but the amount of data is not balanced. Thakkar and Lohiya [3] also reveal that NSL-KDD has a few types of attacks, and the amount of data from each attack is not equal. In the machine learning environment, input is significant for improving classification results.…”

Section: Introductionmentioning

confidence: 99%

Data Reduction for Optimizing Feature Selection in Modeling Intrusion Detection System

Iman¹,

Ahmad²

2020

IJIES

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With the development and ease of access to internet networks, the potential for attacks and intrusions have increased. The intrusion detection system (IDS), an approach to overcome this problem, is grouped into two models: signature-based and anomaly-based. An anomaly-based IDS can be implemented by machine learning; one of the schemes in machine learning is data reduction. IDS datasets are usually obtained through a real-time process that has undefined proportional data. The purpose of data reduction is to speed up and optimize the process, improving accuracy, precision, and specifications. There are several methods to perform data reduction, one of which uses outlier detection techniques. Proper outlier detection has a positive impact on improving the classification results of machine learning. In this research, the outlier detection is done by a circle generated from the k -means clustering of all selected features. Two scenarios are designed for the evaluation: a circle generated from two points of the minimum and maximum cluster and median of all clusters. The formation of clusters conducted by k -means clustering determines the size and direction of the outlier circle so that it dynamically adjusts the distribution of data from the feature selection results. By employing the previous feature selection algorithms, the comparison is performed to evaluate the proposed method's performance. Our empirical results show that the second scenario can significantly improve the classification results in terms of accuracy, detection rate, and precision. The first and second experiments can increase the accuracy by 0.02%, and the third experiment is by 0.1%. The detection rate in the first, second, and third experiments increases by 0.01%, 0.02%, and 0.07. At the same time, precision increases by 0.04%, 0.02%, and 0.01%, correspondingly.

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A Review of the Advancement in Intrusion Detection Datasets

Cited by 179 publications

References 24 publications

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

Deep Learning Approaches for Intrusion Detection

Data Reduction for Optimizing Feature Selection in Modeling Intrusion Detection System

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