@conference {316,
	title = {Insider Threat Detection Based on Users{\textquoteright} Mouse Movements and Keystrokes Behavior},
	booktitle = {Secure Knowledge Management Workshop},
	year = {2017},
	month = {10/2017},
	abstract = {<p>Insider threat is considered as one of the most\&nbsp; serious threats in cybersecurity and has been a prime security\&nbsp; concern for government and industry. Traditional approaches\&nbsp; can{\textquoteright}t provide efficient solutions, and the threat keeps raising.\&nbsp; In this paper, we propose a new approach to insider threat\&nbsp; detection and prediction based on the user{\textquoteright}s mouse movements\&nbsp; and keystrokes behavior. We conduct human subject experiments\&nbsp; with 30 participants and capture their mouse movements and\&nbsp; keystroke dynamics as they perform several computer-based\&nbsp; activities in both benign and malicious scenarios. We extract\&nbsp; features and evaluate our approach using several classifiers and\&nbsp; statistical analysis measures. The results show that participants\&nbsp; performing malicious tasks showed faster speed and longer mouse\&nbsp; movements, and long left click and keystroke duration than the\&nbsp; benign tasks. Our results suggest that users{\textquoteright} mouse movements\&nbsp; and keystrokes behavior can reveal valuable knowledge about\&nbsp; their malicious behavior and can be used as indicators in the\&nbsp; insider threat monitoring and detection frameworks.</p>
},
	author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu}
}
