Benchmark datasets for insider threat detection and indoor crowd behavior analysis

ZHAO Ying; ZHAO Xin; YANG Kui; CHEN Siming; ZHANG Zhuo; HUANG Xin

doi:10.11805/tkyda2021143

[1] GUO H, WANG L, LIANG D, et al. Big earth data from space: a new engine for earth science[J]. Science Bulletin, 2016, 61(7): 505-513.

[4] CAPPELLI D M, MOORE A P, TRZECIAK R F. The CERT guide to insider threats: how to prevent, detect, and respond to information technology crimes(theft,sabotage,fraud)[M]. Boston,MA,USA:Addison-Wesley Professional, 2012.

[6] ZEADALLY S,YU B,JEONG D H,et al. Detecting insider threats: solutions and trends[J]. Information Security Journal:A Global Perspective, 2012,21(4):183-192.

[8] PARVEEN P, EVANS J, THURAISINGHAM B,et al. Insider threat detection using stream mining and graph mining[C]// 2011 3rd IEEE International Conference on Social Computing. Boston,MA,USA:IEEE, 2011:1102-1110.

[9] LEGG P A. Visualizing the insider threat:challenges and tools for identifying malicious user activity[C]// 2015 IEEE Symposium on Visualization for Cyber Security. Chicago,IL,USA:IEEE, 2015:1-7.

[10] SPITZNER L. Honeypots: catching the insider threat[C]// Proceedings of the 19th Annual Computer Security Applications Conference. Las Vegas,NV,USA:IEEE, 2003:170-179.

[11] Visual analytics benchmark repository[EB/OL]. [2021-06-10]. https://www.cs.umd.edu/hcil/varepository/benchmarks.php.

[12] GRINSTEIN G, SCHOLTZ J, WHITING M, et al. VAST 2009 Challenge: An Insider Threat[C]// Proceedings of the 2009 IEEE Symposium on Visual Analytics Science and Technology. Atlantic City,NJ,USA:IEEE, 2009:243-244.

[13] SCHONLAU M. Masquerading user data[EB/OL]. [2021-06-10]. http://www.schonlau.net.

[14] SCHONLAU M,DUMOUCHEL W,JU W H,et al. Computer intrusion:detecting masquerades[J]. Statistical Science, 2001,16(1): 58-74.

[15] CAMI.A J B,HERNáNDEZ-GRACIDAS C,MONROY R,et al. The windows-users and-intruder simulations logs dataset(wuil):

[16] YUAN J, ZHENG Y, XIE X, et al. Driving with knowledge from the physical world[C]// Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego,CA,USA:ACM, 2011:316-324.

[17] YUAN J, ZHENG Y, ZHANG C, et al. T-drive: driving directions based on taxi trajectories[C]// Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose,CA,USA:ACM, 2010:99-108.

[18] PIORKOWSKI M, SARAFIJANOVIC-DJUKIC N, GROSSGLAUSER M. CRAWDAD dataset epfl/mobility[EB/OL]. [2021-06-10]. https://crawdad.org/epfl/mobility/20090224.

[19] PIORKOWSKI M, SARAFIJANOVIC-DJUKIC N, GROSSGLAUSER M. A parsimonious model of mobile partitioned networks with clustering[C]// 2009 1st International Communication Systems and Networks and Workshops. Bangalore,India:IEEE, 2009: 1-10.

[20] HERRERA J C,WORK D B,HERRING R,et al. Evaluation of traffic data obtained via gps-enabled mobile phones:the mobile century field experiment[J]. Transportation Research Part C, 2010,18(4):568-583.

[21] CHAN A B, LIANG Z, VASCONCELOS N. Privacy preserving crowd monitoring: counting people without people models or tracking[C]// 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage,AK,USA:IEEE, 2008:1-7.

[22] IEEE Vast challenge 2016 homepage[EB/OL]. [2021-06-10]. http://www.vacommunity.org/VAST+Challenge+2016.

[23] IEEE Vast challenge 2016 benchmark[EB/OL]. [2021-06-10]. http://www.cs.umd.edu/hcil/varepository.

[24] LIN Y, ZHAO H, MA X, et al. Adversarial attacks in modulation recognition with convolutional neural networks[J]. IEEE Transactions on Reliability, 2021,70(1):389-401.

[25] WANG P,GAO F,ZHAO Y,et al. Detection of indoor high-density crowds via WiFi tracking data[J]. Sensors, 2020,20(18):5078-1-15.

[27] HAN D,JUNG S,LEE M,et al. Building a practical WiFi based indoor navigation system[J]. IEEE Pervasive Computing, 2014,13 (2):72-79.

[28] MEHRAN R,OYAMA A,SHAH M. Abnormal crowd behavior detection using social force model[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami,Florida,USA:IEEE, 2009:935-942.

[29] ITD-2018 dataset homepage[EB/OL]. [2021-06-10]. https://github.com/csuvis/InsiderThreatData.

[30] ZHAO Y, YANG K, CHEN S, et al. A benchmark for visual analysis of insider threat detection[J]. Science China-Information Sciences, 2022,65(9):199102-1-4.

[31] ICMTD-2019 dataset homepage[EB/OL]. [2021-06-10]. http://github.com/csuvis/IndoorTrajectoryData.

[32] ZHAO Y,ZHAO X,CHEN S,et al. An indoor crowd movement trajectory benchmark dataset[J]. IEEE Transactions on Reliability, 2021,70(4):1368-1380.

[33] ChinaVis data challenge homepage[EB/OL]. [2021-06-10]. http://www.chinavis.org/2019/english/challenge_en.html.

[35] ChinaVis homepage[EB/OL]. [2021-06-10]. http://www.chinavis.org.