@conference {270,
	title = {Prediction of human error using eye movements patterns for unintentional insider threat detection},
	booktitle = {2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)},
	year = {2018},
	month = {Jan},
	abstract = {<p>Threats from the inside of an organization{\textquoteright}s perimeters are a significant problem since it is difficult to distinguish them from benign activities. Recent reports indicate that the accidental/unintentional incidents account for the majority ofall insider security incidents. Human error is a major factor in unintentional insider threat. In this paper, we propose a novel approach for unintentional insider threat (UIT) detection and mitigation based on eye movement patterns. We perform experiments to capture unique characteristics of a user{\textquoteright}s eye movements as they perform several computer-based activities in different scenarios. The goal is to evaluate the effectiveness of using eye movement patterns in determining a user{\textquoteright}s subjective mental workload which is one of the main contributing factors to human error. We extract eye movement and pupil features which allow us to reliably achieve this goal. We evaluate our proposed approach using several classifiers and examine how different subsets of features affect the performance. The results show about 82\% accuracy on average for users wearing eye glasses and an average accuracy of 84.5\% for users without eye glasses. Our results demonstrate that users{\textquoteright} eye movement patterns and pupil behaviors can reveal valuable clues about their subjective mental workload and could be used in developing effective tools for unintentional insider threat detection and mitigation in real-world environments.</p>
},
	keywords = {accidental/unintentional incidents, Companies, eye, eye glasses, eye movement patterns, eye movements patterns, Feature extraction, Gaze tracking, Glass, human error prediction, insider security incidents, pupil feature extraction, Security, security of data, subjective mental workload, Task analysis, UIT detection, unintentional insider threat detection, Visualization},
	doi = {10.1109/ISBA.2018.8311479},
	author = {Takabi, Hassan and Hashem, Yassir and Ram Dantu}
}
@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}
}
@conference {269,
	title = {A Multi-Modal Neuro-Physiological Study of Malicious Insider Threats},
	booktitle = {Proceedings of the 2017 International Workshop on Managing Insider Security Threats},
	year = {2017},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {electroencephalogram (eeg), eye tracking, insider threat, neuroscience},
	isbn = {978-1-4503-5177-5},
	doi = {10.1145/3139923.3139930},
	url = {http://doi.acm.org/10.1145/3139923.3139930},
	author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu and Nielsen, Rodney}
}
@article {273,
	title = {Inside the Mind of the Insider: Towards Insider Threat Detection Using Psychophysiological Signals},
	journal = {Journal of Internet Services and Information Security},
	volume = {6},
	year = {2016},
	month = {02},
	pages = {20-36},
	author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu}
}
@conference {268,
	title = {Towards Insider Threat Detection Using Psychophysiological Signals},
	booktitle = {Proceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats},
	year = {2015},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {brain computer interface, electroencephalograph, insider threat detection, physiological indicators},
	isbn = {978-1-4503-3824-0},
	doi = {10.1145/2808783.2808792},
	url = {http://doi.acm.org/10.1145/2808783.2808792},
	author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu}
}
