Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143916
Title: Employee profiling via aspect-based sentiment and network for insider threats detection
Authors: Soh, Charlie
Yu, Sicheng
Narayanan, Annamalai
Duraisamy, Santhiya
Chen, Lihui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Soh, C., Yu, S., Narayanan, A., Duraisamy, S., & Chen, L. (2019). Employee profiling via aspect-based sentiment and network for insider threats detection. Expert Systems with Applications, 135, 351-361. doi:10.1016/j.eswa.2019.05.043
Journal: Expert Systems with Applications
Abstract: Historically, the harm caused by insiders has proven to be one of the greatest concerns for any organization. As such, it has received considerable attention from both the industrial and research communities. Existing works mainly focused on modeling the employees’ normal biometric behavior (e.g., human to device interaction pattern) to detect anomalous behavior which corresponds to the insider activity. However, it is unattainable to stop the insider at the final moment when the malicious act is being carried out. In this paper, we propose a novel framework which performs employee profiling based on aspect-based sentiments and social network information and examine its applicability for early detection of potential insider threats. On the contrary to the traditional sentiment analysis, aspect-based sentiment analysis provides more fine-grained information on the employee. Our framework employs a combination of deep learning techniques such as Gated Recurrent Unit (GRU) and skipgram to build temporal sentiment profiles for the employees. It then performs anomaly detection on the profiles and ranks the employees based on their respective anomaly score. Due to the absence of relevant benchmark dataset, we augmented the publicly available real-world Enron email corpus with an insider threat scenario to evaluate our framework. The evaluation results demonstrate that the augmentation is indeed reflected in the augmented employee’s anomaly ranking (i.e., from normal to abnormal) and her close associates are indeed placed closely to her when the profiles are visualized in the 2D space. The profiles obtained from our framework can also be used to complement any existing expert and intelligent systems with additional capabilities in handling textual information such as, integration with profiles obtained from biometric behavior to form a more comprehensive threat detection system.
URI: https://hdl.handle.net/10356/143916
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2019.05.043
Rights: © 2019 Elsevier Ltd. All rights reserved. This paper was published in Expert Systems with Applications and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20211231
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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