Improved Practical Vulnerability Analysis of Mouse Data According to Offensive Security based on Machine Learning in Image-Based User Authentication

The objective of this study was to verify the feasibility of mouse data exposure by deriving features to improve the accuracy of a mouse data attack technique using machine learning models. To improve the accuracy, the feature appearing between the mouse coordinates input from the user was analyzed, which is defined as a feature for machine learning models to derive a method of improving the accuracy. As a result, we found a feature where the distance between the coordinates is concentrated in a specific range. We verified that the mouse data is apt to being stolen more accurately when the distance is used as a feature. An accuracy of over 99% was achieved, which means that the proposed method almost completely classifies the mouse data input from the user and the mouse data generated by the defender.

[1]  Lawrence O. Hall,et al.  A Comparison of Decision Tree Ensemble Creation Techniques , 2007 .

[2]  Kyungroul Lee,et al.  A Protection Technique for Screen Image-Based Authentication Protocols Utilizing the SetCursorPos Function , 2017, WISA.

[3]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[4]  Kyungroul Lee,et al.  Vulnerability Analysis on the Image-Based Authentication Through the PS/2 Interface , 2018, IMIS.

[5]  Eyke Hüllermeier,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009, Machine Learning.

[6]  Insoo Koo,et al.  Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features , 2017, IEEE Access.

[7]  Lawrence O. Hall,et al.  A Comparison of Decision Tree Ensemble Creation Techniques , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ajinkya Pawar,et al.  Secure Authentication using Anti-Screenshot Virtual Keyboard , 2011 .

[9]  Wm. Arthur Conklin,et al.  Password-based authentication: a system perspective , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[10]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.

[11]  Kyungroul Lee,et al.  Security Assessment on the Mouse Data using Mouse Loggers , 2016, BWCCA.

[12]  Kyungroul Lee,et al.  Vulnerability Analysis Challenges of the Mouse Data Based on Machine Learning for Image-Based User Authentication , 2019, IEEE Access.

[13]  Kyungroul Lee,et al.  Security Assessment of the Image-Based Authentication Using Screen-Capture Tools , 2017, IMIS.

[14]  HüllermeierEyke,et al.  Combining instance-based learning and logistic regression for multilabel classification , 2009 .

[15]  Kyungroul Lee,et al.  Vulnerability analysis on the image‐based authentication: Through the WM_INPUT message , 2020, Concurr. Comput. Pract. Exp..

[16]  Kyungroul Lee,et al.  Keyboard Security: A Technological Review , 2011, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[17]  R.E. Newman,et al.  Security analysis of and proposal for image-based authentication , 2005, Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology.

[18]  Hideki Koike,et al.  Awase-E: Image-Based Authentication for Mobile Phones Using User's Favorite Images , 2003, Mobile HCI.

[19]  Sara Matzner,et al.  An application of machine learning to network intrusion detection , 1999, Proceedings 15th Annual Computer Security Applications Conference (ACSAC'99).