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   Feature selection and machine learning classification for malware detection  
   
نویسنده khammas b.m. ,monemi a. ,bassi j.s. ,ismail i. ,nor s.m. ,marsono m.n.
منبع jurnal teknologi - 2015 - دوره : 77 - شماره : 1 - صفحه:243 -250
چکیده    Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. since new malware variants contain patterns that are similar to those in observed malware,machine learning techniques can be used to identify new malware. this work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. the result shows that the use of principal component analysis (pca) feature selection and support vector machines (svm) classification gives the best classification accuracy using a minimum number of features. © 2015 penerbit utm press. all rights reserved.
کلیدواژه Feature selection; Machine learning; Malware detection; Principal component analysis; Support vector machine
آدرس universiti teknologi malaysia,johor bahru,malaysia,network engineering department,collage of information engineering,the university of al-nahrain, Iraq, universiti teknologi malaysia, Malaysia, universiti teknologi malaysia, Malaysia, universiti teknologi malaysia, Malaysia, universiti teknologi malaysia, Malaysia, universiti teknologi malaysia, Malaysia
 
     
   
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