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Title: :  Ensemble Models and Explainable AI for Malware Detection
PaperId: :  28235
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 12 Issue 2 2026
DUI:    16.0415/IJARIIE-28235
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
M VandanaSphoorthy Engineering College
Gattu PrasadSphoorthy Engineering College
G Sreeja Reddy Sphoorthy Engineering College
P Snigdha ReddySphoorthy Engineering College
P Juhee ReddySphoorthy Engineering College
M VenkateshSphoorthy Engineering College

Abstract

Computer Science
Malware Detection, Hybrid Model, Random Forest, Artificial Neural Network, Explainable AI, SHAP, LIME, Cybersecurity, Behavioral Analysis, MalwareShield AI.
The rapid growth of malware has created serious security challenges for modern computing systems. Traditional signature-based detection techniques are often ineffective against newly emerging and polymorphic malware, making intelligent detection mechanisms necessary. This study proposes a hybrid machine learning framework for malware detection that combines Random Forest (RF) and Artificial Neural Network (ANN) models to improve classification accuracy and reliability. The dataset used in this research consists of 100,000 records obtained from a publicly available Kaggle repository, evenly split between malware and benign samples. The dataset underwent preprocessing steps including removal of redundant attributes, handling missing values, numeric conversion of features, and feature scaling using StandardScaler. The 33 behavioral features capture Linux kernel process characteristics such as memory usage, CPU scheduling, context switches, and execution timing. Several machine learning models — Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (KNN), and Random Forest (RF) — were implemented to evaluate baseline performance. A deep learning model based on an Artificial Neural Network (ANN) with two hidden layers, dropout regularization, and early stopping was also trained to capture complex non-linear patterns. A Hybrid model was developed by combining the prediction probabilities of the Random Forest and ANN models using ensemble averaging, achieving the highest accuracy of 93.0%, precision of 92.93%, recall of 93.08%, and F1-score of 93.01%. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curves, and confusion matrices. To improve interpretability, Explainable Artificial Intelligence techniques — SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) — were applied to analyze feature contributions globally and locally. A Chi-Square statistical test was used to validate that the top features identified by XAI methods are genuinely significant. A Streamlit-based interactive web application called MalwareShield AI was developed to demonstrate the system with live detection, batch scanning, model performance visualization, SHAP analysis, LIME explanation, and Chi-Square validation modules. The proposed hybrid approach provides an accurate, scalable, and interpretable solution for malware detection in modern cybersecurity systems.

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IJARIIE M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. "Ensemble Models and Explainable AI for Malware Detection" International Journal Of Advance Research And Innovative Ideas In Education Volume 12 Issue 2 2026 Page 892-902
MLA M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. "Ensemble Models and Explainable AI for Malware Detection." International Journal Of Advance Research And Innovative Ideas In Education 12.2(2026) : 892-902.
APA M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, & M Venkatesh. (2026). Ensemble Models and Explainable AI for Malware Detection. International Journal Of Advance Research And Innovative Ideas In Education, 12(2), 892-902.
Chicago M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. "Ensemble Models and Explainable AI for Malware Detection." International Journal Of Advance Research And Innovative Ideas In Education 12, no. 2 (2026) : 892-902.
Oxford M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. 'Ensemble Models and Explainable AI for Malware Detection', International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, 2026, p. 892-902. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Ensemble_Models_and_Explainable_AI_for_Malware_Detection_ijariie28235.pdf (Accessed : ).
Harvard M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. (2026) 'Ensemble Models and Explainable AI for Malware Detection', International Journal Of Advance Research And Innovative Ideas In Education, 12(2), pp. 892-902IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Ensemble_Models_and_Explainable_AI_for_Malware_Detection_ijariie28235.pdf (Accessed : )
IEEE M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh, "Ensemble Models and Explainable AI for Malware Detection," International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, pp. 892-902, Mar-App 2026. [Online]. Available: http://ijariie.com/AdminUploadPdf/Ensemble_Models_and_Explainable_AI_for_Malware_Detection_ijariie28235.pdf [Accessed : ].
Turabian M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. "Ensemble Models and Explainable AI for Malware Detection." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 12 number 2 ().
Vancouver M Vandana, Gattu Prasad, G Sreeja Reddy , P Snigdha Reddy, P Juhee Reddy, and M Venkatesh. Ensemble Models and Explainable AI for Malware Detection. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2026 [Cited : ]; 12(2) : 892-902. Available from: http://ijariie.com/AdminUploadPdf/Ensemble_Models_and_Explainable_AI_for_Malware_Detection_ijariie28235.pdf
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