Hybrid Technique Based on DBSCAN for Selection of Improved Features for Intrusion Detection System

Saxena, Akash; Saxena, Khushboo; Goyal, Jayanti

doi:10.1007/978-981-13-2285-3_43

Cited by 14 publications

(9 citation statements)

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“…Then the feature selection method was generated the inputs and also analyzed the whole data. 8 Shahriar Mohammadi and Fatemeh Amiri in 2019 suggested a hybrid approach with the combination of Self-Organizing Map (SOM) approach, Radial Base Function (RBF) method and perceptron methods as well. Calculating the neural network parameters, the imperialist competitive algorithm was used in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…This has a high detection rate and greater precision in the identification of attacks relative to legendary approaches. 8,18 Tanjila Mawla et al in 2019 proposed the idea of applying a structure to define the hidden patterns of events related to non-negative factorization of matrix and which is based on the co-evolutionary matrix of doubly sparse. The two non-negative matrixes were showed by the convergence of the system.…”

Section: Related Workmentioning

confidence: 99%

“…This is the greatest used scheme for removing unwanted data from the results. Then the feature selection method was generated the inputs and also analyzed the whole data 8 …”

Section: Related Workmentioning

confidence: 99%

“…Data mining can help to segment the data so that it is possible to recognize different classes based on the grouping of parameters to seem for an inspired say that to show data. 8 For mining the data use many algorithms to collect data bases information and patterns extracted from the process of knowledge discovery. Classification can be called the process by which predefined instances or classes are assigned or given the task.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

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Machine learning process is an important part in intrusion detection system (IDS) and plays an essential role in security information. Analysts operate IDS for the study of server logs or packets of data to identify network traffic malware. IDS automates this process with machine learning-based techniques that allow packet detection on a continuous basis without much human effort or intervention through network analyzing data. It has the following limitations on low rate of detection, false alarm rate is high, and so on, to which the conventional IDS has indicated. We proposed a stacking model that is more accurate to detect the attacks than other techniques of Random Forest, Naïve Bayes, and Decision Tree C4.5. Different researchers are exploring various techniques to classify the rules for associations. But the measure of confidence itself, as investigated in various literature, produces many times inaccurate results. So based on a recent value test called the significant rule power factor (SRPF), this paper discusses classifier. The experiments are carried out by implementation in Weka's method. Results indicate that the proposed classifier is a newly created, light weight classifier with better accuracy performance. Detection of different attacks uses NSL-KDD dataset to train and test the data in this technique. This proposed work is measured using different performance metrics to other intrusion detection models. Compared to existing methods, our proposed model offers prominent increase in detection accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…This is the greatest used scheme for removing unwanted data from the results. Then the feature selection method was generated the inputs and also analyzed the whole data 8 …”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

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show abstract

“…The aim of dimension reduction is to find a minimum set of attributes that preserves all the essential information of the training sample for pattern recognition or data mining domains [23], [40], [45], [48], [49].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Computing Minimum-Length Irreducible Testors

et al. 2020

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In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length. INDEX TERMS Feature selection, MDL principle, minimum-length irreducible testors, testor.

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A Survey of Network Intrusion Detection Using Machine Learning Techniques

Rincy¹,

Gupta²

2021

Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics

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Hybrid Technique Based on DBSCAN for Selection of Improved Features for Intrusion Detection System

Cited by 14 publications

References 10 publications

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

An Algorithm for Computing Minimum-Length Irreducible Testors

A Survey of Network Intrusion Detection Using Machine Learning Techniques

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