کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969514 1449977 2017 38 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Mini-batch bagging and attribute ranking for accurate user authentication in keystroke dynamics
ترجمه فارسی عنوان
بسته بندی مینی دسته بندی و ویژگی رتبه بندی برای تأیید اعتبار کاربر دقیق در دینامیک کلید زدن
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
We consider the problem of differentiating users' typing behavior patterns using machine learning algorithms with keystroke dynamics features. We have proposed mini-batch bagging (MINIBAG) method and attribute ranking of one-class naïve Bayes (AR-ONENB) algorithm. MINIBAG is motivated from bagging because MINIBAG chunks each attribute of the dataset into multiple sub-datasets during the preprocessing phase. Meanwhile, AR-ONENB sorts the attributes based on the time length during the preprocessing phase for effective classification. Both proposed algorithms have shown promising experimental results from various keystroke dynamics based user authentication benchmark tests. From the experimental results, it can be seen that MINIBAG facilitates machine learning algorithms to have an ensemble of multiple models from mini-batches. AR-ONENB, on the other hands, calculates log-likelihood value from keystroke index order for anomaly estimation, which exploits the observation that the user's typing speed is unique.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 70, October 2017, Pages 139-151
نویسندگان
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