Seoul National University · 情報科学
Professor Sungzoon Cho's research lab specializes in data science and machine learning with a focus on real-world applications in cybersecurity, customer behavior modeling, and industrial data analytics. The lab develops advanced predictive models that address critical challenges such as missing data in production systems, secure user authentication through biometric-like keystroke dynamics, and response modeling in marketing with limited labeled data. Their work emphasizes robust, practical solutions for incomplete, imbalanced, or noisy data commonly found in industrial and web-based environments. The lab integrates statistical learning, neural networks, and data mining techniques to build reliable and deployable systems for real-world deployment.
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Abstract Password typing is the most widely used identity verification method in Web based electronic commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct
Response modeling is concerned with identifying potential customers who are likely to purchase a promoted product, based on customers’ demographic and behavioral data. Constructing a response model requires a preliminary campaign result database. Customers who responded to the campaign are labeled as respondents while those who did not are labeled as non-respondents. Those customers who were not chosen for the preliminary campaign do not have labels, and thus are called unlabeled. Then, using on
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