User authentication based on keystroke dynamics using artificial neural networks

العناوين الأخرى

التحقق من المستخدم استنادا على ديناميكية ضغطة المفتاح باستخدام شبكات عصبية اصطناعية

مقدم أطروحة جامعية

Hubi, Mays Muhammad

مشرف أطروحة جامعية

Hamid, Sarab Majid

أعضاء اللجنة

Duaymi, Mahdi Kazaz.
Stephan, Jane Jalil
Abd al-Wahhab, Halah Bahjat

الجامعة

جامعة بغداد

الكلية

كلية العلوم

القسم الأكاديمي

قسم علوم الحاسبات

دولة الجامعة

العراق

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2012

الملخص الإنجليزي

Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly.

Usually, password-based user authentication is used to authenticate the legitimate user.

However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing.

Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm.

In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns.

This thesis considers static keystroke dynamics as a transparent layer of the user for user authentication.

Back Propagation Neural Network (BPNN) and the Probabilistic Neural Network (PNN) are used as a classifier to discriminate between the authentic and impostor users.

Furthermore, four keystroke dynamics features namely: Dwell Time (DT), Flight Time (FT), Up-Up Time (UUT), and a mixture of (DT) and (FT) are extracted to verify whether the users could be properly authenticated.

Two datasets (keystroke-1) and (keystroke-2) are used to show the applicability of the proposed Keystroke dynamics user authentication system.

The best results obtained with lowest false rates and highest accuracy when using UUT compared with DT and FT features and comparable to combination of DT and FT, because of UUT as one direct feature that implicitly contained the two other features DT, and FT; that lead to build a new feature from the previous two features making the last feature having more capability to discriminate the authentic users from the impostors.

In addition, authentication with UUT alone instead of the combination of DT and FT reduce the complexity and computational time of the neural network when compared with combination of DT and FT features.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• العقود
• الابتكارات العلمية
• معالجة البيانات
• الحواسيب
• التحكم بالوصول
• الشبكات العصبية(الحاسبات الإلكترونية)

عدد الصفحات

118

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-605390