Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Sheelavathy Veerabhadrappa Kudrekar
REVA University
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification of malware using multinomial linked latent modular double q learning Sheelavathy Veerabhadrappa Kudrekar; Udaya Rani Vinayakamurthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp577-586

Abstract

In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detection that extracts the behavior based on the network pattern. The behavior is extracted which are grouped to perform on the path protocol for analyzing repeated behaviors. Finally, the modular double q learning malware classification model is the grouped behaviors for significant malware detection. The effectiveness of proposed MLLD-M DQL method is compared with existing models. The results obtained by the proposed method show that the model combined with the machine learning (ML) significantly determined malware detection time and also reduced the false positive rate (FPR). The results showed that the false positive rate is significantly reduced by 42% for the proposed method better when compared to the existing behavior based malware detection model that obtained 62% of FPR.
Fisher exact Boschloo and polynomial vector learning for malware detection Sheelavathy Veerabhadrappa Kudrekar; Udaya Rani Vinayaka Murthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2942-2952

Abstract

Computer technology shows swift progress that has infiltrated people’s lives with the candidness and pliability of computers to work ease shows security breaches. Thus, malware detection methods perform modifications in running the malware based on behavioral and content factors. The factors are taken into consideration compromises of convergence rate and speed. This research paper proposed a method called fisher exact Boschloo and polynomial vector learning (FEB-PVL) to perform both content and behavioral-based malware detection with early convergence to speed up the process. First, the input dataset is provided as input then fisher exact Boschloo’s test Bernoulli feature extraction model is applied to obtain independent observations of two binary variables. Next, the extracted network features form input to polynomial regression support vector learning to different malware classes from benign classes. The proposed method validates the results with respect to the malware and the benign files. The present research aimed to develop the behaviors to detect the accuracy process of the features that have minimum time speeds the overall performances. The proposed FEB-PVL increases the true positive rate and reduces the false positive rate and hence increasing the precision rate using FEB-PVL by 7% compared to existing approaches.
Co-Authors Udaya Rani Vinayaka Murthy Udaya Rani Vinayakamurthy