Filter-based Attribute Selection Approach for Intrusion Detection using k-Means Clustering and Sequential Minimal Optimization Techniq

Chandra, Arijit; Khatri, Sunil Kumar; Simon, Rajbala

doi:10.1109/aicai.2019.8701373

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…This model provides important improvement in detection accuracy. 10 Ansam Khraisat et al in 2020 suggested the combination of efficient tree classifier C5 with the Support Vector Machine with One Class (OC-SVM). Hybrid IDS integrates Signature -Based Intrusion Detection System (SIDS) strengths.…”

Section: R E T R a C T E D 2 | Related Workmentioning

confidence: 99%

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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: R E T R a C T E D 2 | Related Workmentioning

confidence: 99%

“…K‐means agglomeration and Sequent marginal Improvement classification and MLTs are wont to determine totally different classes of attacks exploitation KDD99 information set for model training and testing. This model provides important improvement in detection accuracy 10 …”

Section: Related Workmentioning

confidence: 99%

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

“…Chandra et al proposed a hybrid model using the KDD Cup99 dataset in 2019 [12]. They used Filter-Based Attribute Selection to reduce the feature dimension of the dataset.…”

Section: Related Workmentioning

confidence: 99%

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

2020

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In recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don't contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.

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“…Need more resources when implemented in large scale network. [37] CfsSubset Evaluation + Best First Search Algorithm -13 attributes are selected from the 42; -Reduce calculations and processing time; -Improve the detection accuracy.…”

Section: Relevant Workmentioning

confidence: 99%

An Approach for Optimizing Ensemble Intrusion Detection Systems

et al. 2021

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Filter-based Attribute Selection Approach for Intrusion Detection using k-Means Clustering and Sequential Minimal Optimization Techniq

Cited by 21 publications

References 5 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

Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

An Approach for Optimizing Ensemble Intrusion Detection Systems

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