این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
International Journal of Engineering، جلد ۳۶، شماره ۷، صفحات ۱۳۴۳-۱۳۵۳
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Feature Engineering Methods in Intrusion Detection System: A Performance Evaluation
چکیده انگلیسی مقاله Today, the number of cyber-attacks has increased and become more complex with an increase in the size of high-dimensional data, which includes noisy and irrelevant features. In such cases, the removal of irrelevant and noisy features, by Feature Selection (FS) and Dimensions Reduction (DR) methods, can be very effective in increasing the performance of intrusion detection systems (IDS). This paper compares some FS and DR methods for detecting cyber-attacks with the best accuracy using implementation on KDDCUP99 dataset. A Deep Neural Network (DNN) is used for training and simulating them. The results show the filter methods are faster than wrapper methods but less accurate. Whereas the Wrapper methods have more accuracy but are computationally costlier. Embedded methods have the best output and maximum values, which is 99% for all the metrics, comparing to it the DR methods have shown a good performance and speed, among them Linear Discriminant Analysis (LDA) method even better than embedded method.
کلیدواژه‌های انگلیسی مقاله Feature selection,Dimensions Reduction,Intrusion Detection System,Deep Neural Network,Security,Machine Learning
نویسندگان مقاله F. Zare |
Department of Compute Engineering, University of Mazandaran, Mazandaran, Iran

P. Mahmoudi-Nasr |
Department of Compute Engineering, University of Mazandaran, Mazandaran, Iran

نشانی اینترنتی https://www.ije.ir/article_170882_012b234995f6dfd363b1a9c1798a3735.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات