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Method and System for Detecting Anomalous User Behaviors: An Ensemble Approach.

X. Xi, T. Zhang, D. Du, G. Zhao, Q. Gao, W. Zhao, and S. Zhang. SEKE, page 263-262. KSI Research Inc. and Knowledge Systems Institute Graduate School, (2018)

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Qian Du University of Stuttgart

Christian Dürnhofer University of Stuttgart

Andrian Dudonu University of Stuttgart

Tobias Dürr University of Stuttgart

Margun Dürr University of Stuttgart

Other publications of authors with the same name

Refining Traceability Links Between Vulnerability and Software Component in a Vulnerability Knowledge Graph.D. Du, X. Ren, Y. Wu, J. Chen, W. Ye, J. Sun, X. Xi, Q. Gao, and S. Zhang. ICWE, volume 10845 of Lecture Notes in Computer Science, page 33-49. Springer, (2018)Multiple-Parameter Side-Channel Analysis: A Non-invasive Hardware Trojan Detection Approach.S. Narasimhan, R. Chakraborty, D. Du, S. Paul, F. Wolff, C. Papachristou, K. Roy, and S. Bhunia. HOST, page 13-18. IEEE Computer Society, (2010)An Ensemble Approach for Detecting Anomalous User Behaviors.X. Xi, T. Zhang, W. Ye, W. Zhao, S. Zhang, D. Du, and Q. Gao. International Journal of Software Engineering and Knowledge Engineering, 28 (11-12): 1637-1656 (2018)TeSR: A robust Temporal Self-Referencing approach for Hardware Trojan detection.S. Narasimhan, X. Wang, D. Du, R. Chakraborty, and S. Bhunia. HOST, page 71-74. IEEE Computer Society, (2011)Hardware Trojan Detection by Multiple-Parameter Side-Channel Analysis.S. Narasimhan, D. Du, R. Chakraborty, S. Paul, F. Wolff, C. Papachristou, K. Roy, and S. Bhunia. IEEE Trans. Computers, 62 (11): 2183-2195 (2013)A Dynamic Boosted Ensemble Learning Method Based on Random Forest.X. Ren, C. Long, L. Zhang, Y. Wei, D. Du, J. Liang, S. Zhang, and W. Li. CoRR, (2018)DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery.R. Xie, L. Chen, W. Ye, Z. Li, T. Hu, D. Du, and S. Zhang. SANER, page 434-444. IEEE, (2019)Self-referencing: A Scalable Side-Channel Approach for Hardware Trojan Detection.D. Du, S. Narasimhan, R. Chakraborty, and S. Bhunia. CHES, volume 6225 of Lecture Notes in Computer Science, page 173-187. Springer, (2010)Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics.D. Du, J. Wang, B. Wang, L. Zhu, and X. Hong. Sensors, 19 (2): 419 (2019)Method and System for Detecting Anomalous User Behaviors: An Ensemble Approach.X. Xi, T. Zhang, D. Du, G. Zhao, Q. Gao, W. Zhao, and S. Zhang. SEKE, page 263-262. KSI Research Inc. and Knowledge Systems Institute Graduate School, (2018)