Data science

The goal of this project is to create the algorithms, frameworks, and systems for defending the open web ecosystem from emerging threats. This project aims to (i) analyze malicious tasks and behaviors of crowdturfers; (ii) detect malicious tasks on crowdsourcing platforms by developing novel malicious task detectors; (iii) design and build a task blacklist; (iv) uncover the ecosystem of crowdturfers and detect crowdturfers; (v) combine crowdturfer detection approaches with other malicious participants detection approaches.

Social networks provide many benefits, but also give rise to serious concerns regarding privacy. Indeed, since privacy protections are not intrinsically incorporated into the underlying technological framework, user data is still accessible to the social network and is open to misuse. While there have been efforts to incorporate privacy into social networks, existing solutions are not sufficiently lightweight, transparent, and functional, and therefore have achieved only limited adoption.

Big data analytics can revolutionize innovation and productivity across diverse domains. However, this requires sharing or joint analysis of data, which is often inhibited due to privacy and security concerns. While techniques have been developed to enable the safe use of data for analysis, none of these work for the critical task of outlier detection. Outlier detection is one of the most fundamental data analysis tasks, useful in applications as far ranging as homeland security, to medical informatics, to financial fraud.

Spurred by developments such as cloud computing, there has been considerable interest in the data-mining-as-a-service (DMaS) paradigm in which a client outsources his/her data mining needs to a third-party service provider. However, this raises a few security concerns. One of the security concerns is that the service provider may return plausible but incorrect mining results to the client.

The ever-growing malware threats call for effective, yet efficient, mitigation techniques. Machine learning offers a promising solution to malware defense due to the scalability and automation that it brings. Machine learning techniques are however not a panacea for advanced malware attacks where cyber criminals can carefully craft malware features to evade detection. The root cause of such attacks can be attributed to the passive nature of existing machine learning-based malware defense systems.