Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction

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doi:10.9790/0661-155107112

Cited by 15 publications

(2 citation statements)

References 6 publications

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“…Moreover, it was observed that the Lazy classifier yielded superior results compared to other technique. Mahmood and Hussein [15] employed the K-star algorithm in conjunction with filtering analysis to construct a network intrusion detection system. They conducted experimental analysis using the K-star dataset, a modified version of the intrusion detection benchmark dataset.…”

Section: Related Workmentioning

confidence: 99%

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

Ravi Kumar,

Syed Wahid Ali,

Pandian

et al. 2024

Eng. Res. Express

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The future of the automotive industry lies in the utilization of electric vehicles (EVs). A crucial component of these EVs is the drive motor. The verification of critical parameters for the electrical motor is considered to be of utmost importance, and this is achieved through the use of a motor testing machine. The motor testing machine is utilized frequently to determine characteristic curve points and the electromagnetic behavior of electric motors under various conditions. However, it has been observed that the presence of a fault in the helical gear setup of the testing machine leads to frequent breakdowns and suboptimal performance, thereby affecting the efficiency of the assembly line in the production of electric vehicles. In light of these observations, the main objective of this research endeavor is to improve the motor test bench apparatus by altering essential design parameters through the utilization of vibration signal analysis and machine learning techniques. Prior to extracting statistical features, vibration signals are obtained at different gear settings. These signals are subsequently categorized using classifiers such as Bagged Trees and Quadratic SVM. Machine learning techniques are utilized to classify the gathered signals as either normal or faulty, both with and without the inclusion of a 0.25 KW load for each condition. The performance of multiple algorithms is evaluated and scrutinized. The results have demonstrated that the Bagged Trees technique outperforms the other algorithm, achieving an impressive accuracy of 95.3%. Consequently, the proposed method presents an adept solution for augmenting the motor test bench’s capability by modifying crucial design parameters.

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Section: Related Workmentioning

confidence: 99%

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

Ravi Kumar,

Syed Wahid Ali,

Pandian

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Both KNN and K* are instancebased learners, which means that the class of a test instance is decided by the class of training instances that are like it, as defined by some similarity function. In the case of K*, an entropy-based distance function is deployed as a similarity function while KNN is based on the Euclidean distance function [42,43]. Table 2 shows the parameters of KNN and K* as deployed in this study.…”

Section: Algorithms Parameter Settingmentioning

confidence: 99%

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Akintola¹,

Balogun

Mojeed³

et al. 2022

Int. J. Interact. Mob. Technol.

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Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.

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Classification of colorectal cancer based on the association of multidimensional and multiresolution features

Ribeiro

Neves

Nascimento

et al. 2019

Expert Systems with Applications

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Intrusion Detection System Based on K-Star Classifier and Feature Set Reduction

Abstract: Network security and Intrusion Detection Systems (IDS's) is an important

Cited by 15 publications

References 6 publications

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Classification of colorectal cancer based on the association of multidimensional and multiresolution features

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