Abstract:
A malware is any software intentionally designed to cause damage to a computer, server, client or network. Malware is very challenging issue and major concern for privacy...Show MoreMetadata
Abstract:
A malware is any software intentionally designed to cause damage to a computer, server, client or network. Malware is very challenging issue and major concern for privacy of data. This detection can give information about Malicious and Benign data. Machine learning contain Evaluation measures like multi-class log loss, Confusion matrix, precision and recall these four measures are used. Exploratory data analysis, ANOVA, Feature engineering and evaluation are methods for classification machine learning models. In this paper Decision tree model is used for the classification. Max depth for 16 value gives more accuracy score 0.63 as compare to Max depth for 32 value which gives accuracy score 0.5.
Published in: 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)
Date of Conference: 25-27 May 2022
Date Added to IEEE Xplore: 08 June 2022
ISBN Information:
ISSN Information:
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- IEEE Keywords
- Index Terms
- Machine Learning ,
- Exploratory Analysis ,
- Decision Tree ,
- Machine Learning Models ,
- Confusion Matrix ,
- Precision And Recall ,
- Accuracy Scores ,
- Feature Engineering ,
- Log Loss ,
- Chi-square ,
- Statistical Tests ,
- Model Performance ,
- Explanatory Variables ,
- Types Of Models ,
- Random Forest ,
- K-nearest Neighbor ,
- F-value ,
- Target Variable ,
- Test Error ,
- Ransomware ,
- Training Error ,
- XGBoost ,
- Malicious Data ,
- Terminal Nodes ,
- Underfitting
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Machine Learning ,
- Exploratory Analysis ,
- Decision Tree ,
- Machine Learning Models ,
- Confusion Matrix ,
- Precision And Recall ,
- Accuracy Scores ,
- Feature Engineering ,
- Log Loss ,
- Chi-square ,
- Statistical Tests ,
- Model Performance ,
- Explanatory Variables ,
- Types Of Models ,
- Random Forest ,
- K-nearest Neighbor ,
- F-value ,
- Target Variable ,
- Test Error ,
- Ransomware ,
- Training Error ,
- XGBoost ,
- Malicious Data ,
- Terminal Nodes ,
- Underfitting
- Author Keywords