Online Deception Detection

Behavioral Control of Deceivers in Online Attacks

Award details

Abstract

Online attacks can cause not only temporary asset loss, but long-term psychological or emotional harm to victims as well. The richness and large scale of online communication data open up new opportunities for detecting online attacks. However, attackers are motivated to constantly adapt their behaviors to changes in security operations to evade detection. Deception underlies most attacks in online communication, and people are poor at detecting deception. Against this backdrop, this project aims to improve the resilience of solutions to online attacks and enable predictive methods for their detection. Although a complete set of deception behaviors of online attackers is assumed to be unknown, there is a reason to expect that some behaviors are more difficult for attackers to control than others.

By identifying such behaviors and their relations in online communication, the project lays the groundwork for the development of resilient and predictive approaches to the detection of online attacks, and advances the state of knowledge on online deception behavior and its identification. At the educational front, the project provides new educational material for enriching the curriculum in cyber security and related disciplines. The interdisciplinary nature of this work contributes to graduate student training toward a new generation of scientists who are capable of conducting multi-disciplinary cutting-edge research using a variety of research methods. The PIs actively engage students at both graduate and undergraduate levels in their research activities, particularly making a strong effort to engage women and underrepresented minorities.

Online attackers’ evolving behaviors can make the existing solutions to online attacks become ineffective quickly. This project not only discovers new deception behaviors and their relations from the discourse and structure of online communication, but also determines attackers’ behavioral control during online attacks by comparing different types of online deception behavior. Further, this project develops techniques for automatic extraction of deception behaviors from online communication by building upon natural language processing and network analysis techniques. Some anticipated advances include:

(1) deception theory extension by investigating deception behavior in online attacks via a new lens of behavior control,

(2) guidelines on how to improve the resilience of online attack detection methods by identifying deception behaviors that likely escape the attackers’ control attempt,

(3) a predictive approach to attack detection in online communication by exploring the temporal relationships among deception behaviors, and

(4) techniques for extracting deception behaviors from online discourse and structure. This project can lead to integrative and effective methods for online attack detection.

Selected Publications

J. Lim, Zhou, L. and D. Zhang (2021), Verbal Deception Cues Training for the Detection of Phishing Emails, IEEE International Conference on Intelligence and Security Informatics, San Antonio, TX, Nov. 2-3, 2021.

Zhou, L., J. Tao, E. Lai, and D. Zhang (2021), Do Fake News between Different Languages Talk Alike? A Case Study of COVID-19 Related Fake News, International Conference on Secure Knowledge Management in the artificial intelligence, San Antonio, TX, USA, Oct. 5-6, 2021.

Shan, G., L. Zhou, and Zhang, D. (2021). From Conflicts and Confusion to Doubts: Examining Review Inconsistency for Fake Review Detection, Decision Support Systems. 144, May

Tao, J., X. Fang, and L. Zhou (2021), Unsupervised Deep Learning for Fake Content Detection in Social Media, Hawaii International Conference on System Sciences (HICSS-54). January 5-8, Kauai, HI, USA.

Tao, J. and L. Zhou (2020), A Weakly Supervised WordNet-guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews, ACM Transactions on Management Information Systems, 11(3), 1-22.

Zhihui Liu, Lina Zhou and Dongsong Zhang (2020). Effects of Demographic Factors on Phishing Victimization in the Workplace, Proceedings of the PACIS, June 20-24.

Shan, J., L. Zhou, and D. Zhang (2020), What Reveals About Depression Level? The Role of Multimodal Features at the Level of Interview Questions, Information & Management, 57 (7), Nov.

Wang, P., L. Zhou, D. Mu, D. Zhang, and Q. Shao (2020), What Makes Clinical Documents Helpful and Engaging? An Empirical Investigation of Experience Sharing in an Online Medical Community, International Journal of Medical Informatics, 143, Nov.

Kang, Y. and L. Zhou (2019). Helpfulness Assessment of Online Reviews: The Role of Semantic Hierarchy of Product Features, ACM Transactions on Management Information Systems, 10(3), 1-18. https://doi.org/10.1145/3365538.

Zhou, L., J. Lim, H. Alsaleh, J. Wang, and D. Zhang (2019). Language Alternation in Online Communication with Misinformation. The 18th Pre-ICIS Workshop on e-Business. Munich, Germany. Dec.14, 2019.

Ping Wang, Dongmei Mu, L. Zhou, Dongsong Zhang and Qi Shao (2019). Analysis of Factors Affecting the Quality of Clinical Document Based on Online Community of Clinical Professionals, ALIRG 2019 (The 11th Asia Library and Information Research Group Workshop), Nov. 28-29, 2019. Beijing, China.

J. Lim, Z. Liu and L. Zhou (2019) Detecting of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique. IEEE International Conference on Intelligence and Security Informatics, Shenzhen, China, July 1-3, 2019.

Alsaleh, H., L. Zhou (2018). A Heuristic Method for Identifying Scam Ads on Craigslist. European Intelligence and Security Informatics Conference (EISIC), Blekinge Institute of Technology, Karlskrona, Sweden, Oct. 24-25. READ

Shan, G., D. Zhang, L. Zhou, L. Suo, and J. Lim (2018). Inconsistency Investigation between Online Review Content and Rating, Proceedings of the 2018 Americas Conference on Information Systems (AMCIS), Aug. 16-18, New Orleans, LA, USA.READ

Ahmed F. Aleroud and L. Zhou (2017). Phishing Environments, Techniques, and Countermeasures: A Survey, Computers & Security, 68, 160-196.

Kang, Y. and L. Zhou (2017), RubE: Rule-based Methods for Extracting Product Features from Online Consumer Reviews, Information & Management, 54(2), March, 166-176.

Zhang, D., Zhou, L., Kehoe, L. J., and Kilic, I. (2016). What Online Reviewer Behaviors Really Matter? A Study of Effects of Verbal and Nonverbal Behaviors on Online Fake Review Detection. Journal of Management Information Systems. 33(2). p.456-481 READ

Wu, J. and L. Zhou (2015), DOBNet: Exploring the Discourse of Deception Behavior to Uncover Online Deception Strategies, Behavioral & Information Technology, 34(9), 936-948.

Li, S., L. Zhou, and Y. Li (2015). Improving Aspect Extraction by Augmenting a Frequency-Based Method with Web-based Similarity Measures. Information Processing and Management.51, 58-67.

Zhou, L., Wu, J., & Zhang, D. (2014). Discourse Features of Deception Behavior in the Case of Multiple Receivers. Information & Management. 51(6): 726-737 READ

Zhou, L., Song, Y., and Zhang, D. (2013). Deception performance in online group negotiation and decision making: the effects of deception experience and deception skill. Group Decision & Negotiation. 22(1), p.153-172 READ

Zhou, L. and Zhang, D. (2012). Automatic Deception Detection in Computer-Mediated Communication. IEEE Intelligent Systems. Nov./Dec., p. 73-75.

Zhou, L., Shi, Y, and Zhang, D. (2008). A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering (TKDE). 20(8): pp.1077-1081. READ