@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 {67,
	title = {Another free app: Does it have the right intentions?},
	booktitle = {Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on},
	year = {2014},
	month = {July},
	abstract = {<p>Security and privacy holds a great importance in mobile devices due to the escalated use of smart phone applications (app). This has made the user even more vulnerable to malicious attacks than ever before. We aim to address this problem by proposing a novel framework to identify potential Android malware apps by extracting the intention and their permission requests. First, we constructed a dataset consisting of 1,730 benign apps along with 273 malware samples. Then, both datasets were subjected to source code extraction. From there on, we followed a two phase approach to identify potential malware samples. In phase 1, we constructed a machine learning model to group benign apps into different clusters based on their operations known as the task-intention. Once we trained the model, it was used to identify the task-intention of an Android app. Further, in this phase, we only used the benign apps to construct the task-intentions and none of the malware signatures were involved. Therefore, our approach does not use machine learning models to identify malware apps. Then, for each task-intention group, we extracted the permission-requests of the apps and constructed the probability mass functions (PMF). We named the shape of this PMF as Intention-Shape or I-Shape. In phase 2, we used the permission-requests, task-intentions and I-Shapes to identify potential malware apps. We compared the permission-requests of an unknown app with its corresponding I-Shape to identify the potential malware apps. Using this approach, we obtained an accuracy of 89\% in detecting potential malware samples. The novelty of our work is to perform potential malware identification without training any models with malware signatures, and utilization of I-Shapes to identify such potential malware samples. Our approach can be utilized to identify the safety of an app before it is installed as it performs static code analysis. Further, it can be utilized in pre-screening or multi-layer security sys- ems. It is also highly useful in screening malware apps when launching in Android markets.</p>
},
	keywords = {Android app task-intention identification, Android malware apps, Androids, benign apps, clusters, Feature extraction, Humanoid robots, I-Shape, intention-shape, invasive software, learning (artificial intelligence), machine learning model, malicious attacks, Malware, Mathematical model, mobile computing, permission-requests, PMF, probability mass functions, Shape, smart phone applications, smart phones, source code (software), source code extraction, static code analysis, Unsupervised learning},
	doi = {10.1109/PST.2014.6890950},
	author = {Fazeen, Mohamed and Ram Dantu}
}
