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Siber güvenlikte klavye davranış analizi

Authors: AKŞİT, Nurgül; AYDIN, Muhammed Ali; ZAİM, Abdül Halim;

Siber güvenlikte klavye davranış analizi

Abstract

2019 yılında Çin'de ortaya çıkan ve tüm dünyayı etkisi altına alan Covid-19 salgını ile bilgi sistemleri üzerinde değişen çalışma koşullarını daha güvenli bir ortam haline getirme ihtiyacı artmıştır. Bu ihtiyaç araştırmacıları bilgi sistemlerini kullanan kişinin gerçek kişi olduğuna dair doğrulama sistemi geliştirmeye itmiştir. Geliştirilen Klavye Davranış Analizi programı ile her biri farklı alışkanlıklara sahip kullanıcıların verileri toplanmakta ve belirlenen örnekler derin öğrenme ile yapay zekada kullanmak üzere analiz edilmektedir. Bu analizlerin sonuçları, bilgisayarları ele geçiren kötü niyetli saldırganlar tarafından kullanıldığında kimlik doğrulama yöntemi ile tespitinin yapılması konusunda literatüre katkı sağlamaktadır. Çoklu kimlik doğrulama, kullanıcıların sahip oldukları kimliklerinin farklı kombinasyonlar ile bilgi sistemlerinde onaylanma yöntemidir. Çoklu kimlik doğrulamanın yönü, tekli kimlik doğrulama ile atlatılabilecek sistem açıklıklarının güvenliğini sağlamaktır. Bu çalışmanın amacı, iyi bir derin öğrenme yöntemi ile kullanıcıların klavye davranış analizlerini çıkarmak ve bilgi sistemlerine girişlerde kimlik doğrulaması yapmaktır.

With the Covid-19 epidemic that emerged in China in 2019 and affected the whole world, the need to make the changing working conditions on information systems a safer environment has increased. This need has prompted researchers to develop a verification system that confirms that the person using information systems is a real person. With the developed Keyboard Behavior Analysis program, the data of users with different habits are collected and the determined examples are analyzed for use in artificial intelligence with deep learning. The results of these analyzes contribute to the literature in detecting computers by means of authentication when used by malicious attackers. Multiple authentication is a method of confirming the identities of users in information systems with different combinations. The aspect of multiple authentication is to secure system vulnerabilities that can be circumvented by single authentication. The aim of this study is to analyze the keyboard behavior of the users and to authenticate the logins to the information systems with a good deep learning method.

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Keywords

Engineering, User behavior analysis, keyboard usage habits, cyber security measures, cyber defense methods, machine learning, deep learning., Mühendislik, Kullanıcı davranışı analizi, klavye kullanım alışkanlıkları, siber güvenlik önlemleri, siber savunma yöntemleri, makine öğrenmesi, derin öğrenme., Kullanıcı davranışı analizi;klavye kullanım alışkanlıkları;siber güvenlik önlemleri;siber savunma yöntemleri;makine öğrenmesi;derin öğrenme, User behavior analysis;keyboard usage habits;cyber security measures;cyber defense methods;machine learning;deep learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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