Decision tree based light weight intrusion detection using a wrapper approach

348 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “Sindhu et al. (2012) presented a decision tree based light weight intrusion detection using a wrapper approach.”
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing (2016, methodology): “[25] proposed a wrapper method for feature selection to remove irrelevant instances from a feature set to achieve higher detection accuracy using neuro tree.”
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, background): “, bagging [23]–[25], boosting [25], [26], voting [27]–[29], random forest [10], [30], and other ensemble approaches [31]–[34].”
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection (2020, methodology): “According to Sindhu et al. [57], Random Tree is an algorithm that has scalability and efﬁciency.”
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, methodology): “On network trafﬁc, data clas-siﬁcation algorithms such as Support Vector Machine (SVM) [111], [112], Decision Tree [113], [114], KNN [100] have given important and powerful results.”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, neutral): “[137] [138] [156] [140] [28] [141] [142] [143] [144] [159] [160]”