Paper (ACM MM 2016) “Leveraging Contextual Cues for Generating Basketball Highlights”

October 18th, 2016 Irfan Essa Posted in ACM MM, Caroline Pantofaru, Computational Photography and Video, Computer Vision, Papers, Sports Visualization, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, C. Pantofaru, and I. Essa (2016), “Leveraging Contextual Cues for Generating Basketball Highlights,” in Proceedings of ACM International Conference on Multimedia (ACM-MM), 2016. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2016-Bettadapura-LCCGBH,
      arxiv    = {http://arxiv.org/abs/1606.08955},
      author  = {Vinay Bettadapura and Caroline Pantofaru and Irfan
          Essa},
      booktitle  = {Proceedings of ACM International Conference on
          Multimedia (ACM-MM)},
      month    = {October},
      organization  = {ACM},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Bettadapura-LCCGBH.pdf},
      title    = {Leveraging Contextual Cues for Generating
          Basketball Highlights},
      url    = {http://www.vbettadapura.com/highlights/basketball/index.htm},
      year    = {2016}
    }

Abstract

2016-Bettadapura-LCCGBH

Leveraging Contextual Cues for Generating Basketball Highlights

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.

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Paper in IJCARS (2016) on “Automated video-based assessment of surgical skills for training and evaluation in medical schools”

September 2nd, 2016 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Computer Vision, Eric Sarin, Mark Clements, Medical, MICCAI, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. L. Sarin, T. Ploetz, M. A. Clements, and I. Essa (2016), “Automated video-based assessment of surgical skills for training and evaluation in medical schools,” International Journal of Computer Assisted Radiology and Surgery, vol. 11, iss. 9, pp. 1623-1636, 2016. [WEBSITE] [DOI] [BIBTEX]
    @Article{    2016-Zia-AVASSTEMS,
      author  = {Zia, Aneeq and Sharma, Yachna and Bettadapura,
          Vinay and Sarin, Eric L and Ploetz, Thomas and
          Clements, Mark A and Essa, Irfan},
      doi    = {10.1007/s11548-016-1468-2},
      journal  = {International Journal of Computer Assisted
          Radiology and Surgery},
      month    = {September},
      number  = {9},
      pages    = {1623--1636},
      publisher  = {Springer Berlin Heidelberg},
      title    = {Automated video-based assessment of surgical skills
          for training and evaluation in medical schools},
      url    = {http://link.springer.com/article/10.1007/s11548-016-1468-2},
      volume  = {11},
      year    = {2016}
    }

Abstract

2016-Zia-AVASSTEMS

Sample frames from our video dataset

Purpose: Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in- person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches, however, are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities.

Method : We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. Results: We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos.

Conclusion: Our evaluations show that frequency features perform better than motion texture features, which in turn perform better than symbol/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

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Paper (WACV 2016) “Discovering Picturesque Highlights from Egocentric Vacation Videos”

March 7th, 2016 Irfan Essa Posted in Computational Photography and Video, Computer Vision, Daniel Castro, PAMI/ICCV/CVPR/ECCV, Vinay Bettadapura No Comments »

Paper

  • D. Castro, V. Bettadapura, and I. Essa (2016), “Discovering Picturesque Highlights from Egocentric Vacation Video,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2016-Castro-DPHFEVV,
      arxiv    = {http://arxiv.org/abs/1601.04406},
      author  = {Daniel Castro and Vinay Bettadapura and Irfan
          Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      month    = {March},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Castro-DPHFEVV.pdf},
      title    = {Discovering Picturesque Highlights from Egocentric
          Vacation Video},
      url    = {http://www.cc.gatech.edu/cpl/projects/egocentrichighlights/},
      year    = {2016}
    }

Abstract

2016-Castro-DPHFEVVWe present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry, and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14-day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.

 

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Paper in MICCAI (2015): “Automated Assessment of Surgical Skills Using Frequency Analysis”

October 6th, 2015 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Eric Sarin, Mark Clements, Medical, MICCAI, Papers, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. Sarin, M. Clements, and I. Essa (2015), “Automated Assessment of Surgical Skills Using Frequency Analysis,” in International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2015. [PDF] [BIBTEX]
    @InProceedings{    2015-Zia-AASSUFA,
      author  = {A. Zia and Y. Sharma and V. Bettadapura and E.
          Sarin and M. Clements and I. Essa},
      booktitle  = {International Conference on Medical Image Computing
          and Computer Assisted Interventions (MICCAI)},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Zia-AASSUFA.pdf},
      title    = {Automated Assessment of Surgical Skills Using
          Frequency Analysis},
      year    = {2015}
    }

Abstract

We present an automated framework for a visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis technique for extracting motion quality via  frequency coefficients is introduced. The framework is tested in a case study that involved analysis of videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.

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Paper in ISWC 2015: “Predicting Daily Activities from Egocentric Images Using Deep Learning”

September 7th, 2015 Irfan Essa Posted in Activity Recognition, Daniel Castro, Gregory Abowd, Henrik Christensen, ISWC, Machine Learning, Papers, Steven Hickson, Ubiquitous Computing, Vinay Bettadapura No Comments »

Paper

  • D. Castro, S. Hickson, V. Bettadapura, E. Thomaz, G. Abowd, H. Christensen, and I. Essa (2015), “Predicting Daily Activities from Egocentric Images Using Deep Learning,” in Proceedings of International Symposium on Wearable Computers (ISWC), 2015. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2015-Castro-PDAFEIUDL,
      arxiv    = {http://arxiv.org/abs/1510.01576},
      author  = {Daniel Castro and Steven Hickson and Vinay
          Bettadapura and Edison Thomaz and Gregory Abowd and
          Henrik Christensen and Irfan Essa},
      booktitle  = {Proceedings of International Symposium on Wearable
          Computers (ISWC)},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Castro-PDAFEIUDL.pdf},
      title    = {Predicting Daily Activities from Egocentric Images
          Using Deep Learning},
      url    = {http://www.cc.gatech.edu/cpl/projects/dailyactivities/},
      year    = {2015}
    }

Abstract

Castro-ISWC2015We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of a week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person’s activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.

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Paper in IEEE WACV (2015): “Leveraging Context to Support Automated Food Recognition in Restaurants”

January 6th, 2015 Irfan Essa Posted in Activity Recognition, Computer Vision, Edison Thomaz, First Person Computing, Gregory Abowd, Mobile Computing, PAMI/ICCV/CVPR/ECCV, Papers, Ubiquitous Computing, Uncategorized, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, E. Thomaz, A. Parnami, G. Abowd, and I. Essa (2015), “Leveraging Context to Support Automated Food Recognition in Restaurants,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-LCSAFRR,
      arxiv    = {http://arxiv.org/abs/1510.02078},
      author  = {Vinay Bettadapura and Edison Thomaz and Aman
          Parnami and Gregory Abowd and Irfan Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.83},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-LCSAFRR.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Leveraging Context to Support Automated Food
          Recognition in Restaurants},
      url    = {http://www.vbettadapura.com/egocentric/food/},
      year    = {2015}
    }

 

Abstract

The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant’s online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).food-poster

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Paper in WACV (2015): “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices”

January 6th, 2015 Irfan Essa Posted in Activity Recognition, Caroline Pantofaru, Computer Vision, First Person Computing, Mobile Computing, PAMI/ICCV/CVPR/ECCV, Papers, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, I. Essa, and C. Pantofaru (2015), “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. (Best Paper Award) [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-EFLUFPD,
      arxiv    = {http://arxiv.org/abs/1510.02073},
      author  = {Vinay Bettadapura and Irfan Essa and Caroline
          Pantofaru},
      awards  = {(Best Paper Award)},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.89},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-EFLUFPD.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Egocentric Field-of-View Localization Using
          First-Person Point-of-View Devices},
      url    = {http://www.vbettadapura.com/egocentric/localization/},
      year    = {2015}
    }

Abstract

We present a technique that uses images, videos and sensor data taken from first-person point-of-view devices to perform egocentric field-of-view (FOV) localization. We define egocentric FOV localization as capturing the visual information from a person’s field-of-view in a given environment and transferring this information onto a reference corpus of images and videos of the same space, hence determining what a person is attending to. Our method matches images and video taken from the first-person perspective with the reference corpus and refines the results using the first-person’s head orientation information obtained using the device sensors. We demonstrate single and multi-user egocentric FOV localization in different indoor and outdoor environments with applications in augmented reality, event understanding and studying social interactions.

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Four Papers at IEEE Winter Conference on Applications of Computer Vision (WACV 2015)

January 5th, 2015 Irfan Essa Posted in Computational Photography and Video, Computer Vision, PAMI/ICCV/CVPR/ECCV, Papers, S. Hussain Raza, Steven Hickson, Vinay Bettadapura No Comments »

Four papers accepted at the IEEE Winter Conference on Applications of Computer Vision (WACV) 2015. See you at Waikoloa Beach, Hawaii!

  • V. Bettadapura, E. Thomaz, A. Parnami, G. Abowd, and I. Essa (2015), “Leveraging Context to Support Automated Food Recognition in Restaurants,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-LCSAFRR,
      arxiv    = {http://arxiv.org/abs/1510.02078},
      author  = {Vinay Bettadapura and Edison Thomaz and Aman
          Parnami and Gregory Abowd and Irfan Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.83},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-LCSAFRR.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Leveraging Context to Support Automated Food
          Recognition in Restaurants},
      url    = {http://www.vbettadapura.com/egocentric/food/},
      year    = {2015}
    }
  • S. Hickson, I. Essa, and H. Christensen (2015), “Semantic Instance Labeling Leveraging Hierarchical Segmentation,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2015-Hickson-SILLHS,
      author  = {Steven Hickson and Irfan Essa and Henrik
          Christensen},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.147},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Hickson-SILLHS.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Semantic Instance Labeling Leveraging Hierarchical
          Segmentation},
      year    = {2015}
    }
  • S. H. Raza, A. Humayun, M. Grundmann, D. Anderson, and I. Essa (2015), “Finding Temporally Consistent Occlusion Boundaries using Scene Layout,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2015-Raza-FTCOBUSL,
      author  = {Syed Hussain Raza and Ahmad Humayun and Matthias
          Grundmann and David Anderson and Irfan Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.141},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Raza-FTCOBUSL.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Finding Temporally Consistent Occlusion Boundaries
          using Scene Layout},
      year    = {2015}
    }
  • V. Bettadapura, I. Essa, and C. Pantofaru (2015), “Egocentric Field-of-View Localization Using First-Person Point-of-View Devices,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. (Best Paper Award) [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]
    @InProceedings{    2015-Bettadapura-EFLUFPD,
      arxiv    = {http://arxiv.org/abs/1510.02073},
      author  = {Vinay Bettadapura and Irfan Essa and Caroline
          Pantofaru},
      awards  = {(Best Paper Award)},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.89},
      month    = {January},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-EFLUFPD.pdf},
      publisher  = {IEEE Computer Society},
      title    = {Egocentric Field-of-View Localization Using
          First-Person Point-of-View Devices},
      url    = {http://www.vbettadapura.com/egocentric/localization/},
      year    = {2015}
    }

Last one was also the WINNER of Best Paper Award (see http://wacv2015.org/). More details coming soon.

 

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Paper in M2CAI 2014: “Video Based Assessment of OSATS Using Sequential Motion Textures”

September 14th, 2014 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Computer Vision, Medical, MICCAI, Papers, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • Y. Sharma, V. Bettadapura, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. Olivier, S. Deshmukh, A. Mccaskie, and I. Essa (2014), “Video Based Assessment of OSATS Using Sequential Motion Textures,” in Proceedings of Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), 2014. (Best Paper Honorable Mention Award) [PDF] [BIBTEX]
    @InProceedings{    2014-Sharma-VBAOUSMT,
      author  = {Yachna Sharma and Vinay Bettadapura and Thomas
          Ploetz and Nils Hammerla and Sebastian Mellor and
          Roisin McNaney and Patrick Olivier and Sandeep
          Deshmukh and Andrew Mccaskie and Irfan Essa},
      awards  = {(Best Paper Honorable Mention Award)},
      booktitle  = {{Proceedings of Workshop on Modeling and Monitoring
          of Computer Assisted Interventions (M2CAI)}},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2014-Sharma-VBAOUSMT.pdf},
      title    = {Video Based Assessment of OSATS Using Sequential
          Motion Textures},
      year    = {2014}
    }

Abstract

2014-Sharma-VBAOUSMTA fully automated framework for video-based surgical skill assessment is presented that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. The Objective Structured Assessment of Technical Skills (OSATS) assessments is replicated, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis techniques are introduced that incorporate sequential motion aspects into motion textures. Significant performance improvement over standard bag-of-words and motion analysis approaches is demonstrated. The framework is evaluated in a case study that involved medical students with varying levels of expertise performing basic surgical tasks in a surgical training lab setting.

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Paper in ACM KDD 2013 “Detecting insider threats in a real corporate database of computer usage activity”

August 11th, 2013 Irfan Essa Posted in AAAI/IJCAI/UAI, Josh Jones, Vinay Bettadapura No Comments »

  • T. E. Senator, H. G. Goldberg, A. Memory, W. T. Young, B. Rees, R. Pierce, D. Huang, M. Reardon, D. A. Bader, E. Chow, I. Essa, J. Jones, V. Bettadapura, D. H. Chau, O. Green, O. Kaya, A. Zakrzewska, E. Briscoe, R. I. L. Mappus, R. McColl, L. Weiss, T. G. Dietterich, A. Fern, W. Wong, S. Das, A. Emmott, J. Irvine, J. Lee, D. Koutra, C. Faloutsos, D. Corkill, L. Friedland, A. Gentzel, and D. Jensen (2013), “Detecting insider threats in a real corporate database of computer usage activity,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2013, pp. 1393-1401. [WEBSITE] [DOI] [BIBTEX]
    @InProceedings{    2013-Senator-DITRCDCUA,
      acmid    = {2488213},
      address  = {New York, NY, USA},
      author  = {Senator, Ted E. and Goldberg, Henry G. and Memory,
          Alex and Young, William T. and Rees, Brad and
          Pierce, Robert and Huang, Daniel and Reardon,
          Matthew and Bader, David A. and Chow, Edmond and
          Essa, Irfan and Jones, Joshua and Bettadapura, Vinay
          and Chau, Duen Horng and Green, Oded and Kaya, Oguz
          and Zakrzewska, Anita and Briscoe, Erica and Mappus,
          Rudolph IV L. and McColl, Robert and Weiss, Lora and
          Dietterich, Thomas G. and Fern, Alan and Wong,
          Weng--Keen and Das, Shubhomoy and Emmott, Andrew and
          Irvine, Jed and Lee, Jay-Yoon and Koutra, Danai and
          Faloutsos, Christos and Corkill, Daniel and
          Friedland, Lisa and Gentzel, Amanda and Jensen,
          David},
      booktitle  = {{Proceedings of the 19th ACM SIGKDD international
          conference on Knowledge discovery and data mining}},
      doi    = {10.1145/2487575.2488213},
      isbn    = {978-1-4503-2174-7},
      location  = {Chicago, Illinois, USA},
      month    = {September},
      numpages  = {9},
      pages    = {1393--1401},
      publisher  = {ACM},
      series  = {KDD '13},
      title    = {Detecting insider threats in a real corporate
          database of computer usage activity},
      url    = {http://doi.acm.org/10.1145/2487575.2488213},
      year    = {2013}
    }

Abstract

This paper reports on methods and results of an applied research project by a team consisting of SAIC and four universities to develop, integrate, and evaluate new approaches to detect the weak signals characteristic of insider threats on organizations’ information systems. Our system combines structural and semantic information from a real corporate database of monitored activity on their users’ computers to detect independently developed red team inserts of malicious insider activities. We have developed and applied multiple algorithms for anomaly detection based on suspected scenarios of malicious insider behavior, indicators of unusual activities, high-dimensional statistical patterns, temporal sequences, and normal graph evolution. Algorithms and representations for dynamic graph processing provide the ability to scale as needed for enterprise-level deployments on real-time data streams. We have also developed a visual language for specifying combinations of features, baselines, peer groups, time periods, and algorithms to detect anomalies suggestive of instances of insider threat behavior. We defined over 100 data features in seven categories based on approximately 5.5 million actions per day from approximately 5,500 users. We have achieved area under the ROC curve values of up to 0.979 and lift values of 65 on the top 50 user-days identified on two months of real data.

via ACM DL Detecting insider threats in a real corporate database of computer usage activity.

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