September 2020
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22 Reads
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13 Citations
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September 2020
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22 Reads
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13 Citations
March 2020
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116 Reads
The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, , is a strong function of the number of edges in the graph, , which improved significantly from 2017 () to 2018 () and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.
June 2019
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767 Reads
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3 Citations
Understanding cybersecurity in an environment is uniquely challenging due to highly dynamic and potentially-adversarial activity. At the same time, the stakes are high for performance during these tasks: failures to reason about the environment and make decisions can let attacks go unnoticed or worsen the effects of attacks. Opportunities exist to address these challenges by more tightly integrating computer agents with human operators. In this paper, we consider implications for this integration during three stages that contribute to cyber analysts developing insights and conclusions about their environment: data organization and interaction, toolsmithing and analytic interaction, and human-centered assessment that leads to insights and conclusions. In each area, we discuss current challenges and opportunities for improved human-machine teaming. Finally, we present a roadmap of research goals for advanced human-machine teaming in cybersecurity operations.
June 2019
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161 Reads
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2 Citations
Cyber SA is described as the current and predictive knowledge of cyberspace in relation to the Network, Missions and Threats across friendly, neutral and adversary forces. While this model provides a good high-level understanding of Cyber SA, it does not contain actionable information to help inform the development of capabilities to improve SA. In this paper, we present a systematic, human-centered process that uses a card sort methodology to understand and conceptualize Senior Leader Cyber SA requirements. From the data collected, we were able to build a hierarchy of high- and low- priority Cyber SA information, as well as uncover items that represent high levels of disagreement with and across organizations. The findings of this study serve as a first step in developing a better understanding of what Cyber SA means to Senior Leaders, and can inform the development of future capabilities to improve their SA and Mission Performance.
October 2018
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32 Reads
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1 Citation
September 2018
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59 Reads
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23 Citations
May 2018
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76 Reads
The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. Graph Challenge 2017 received 22 submissions by 111 authors from 36 organizations. The submissions highlighted graph analytic innovations in hardware, software, algorithms, systems, and visualization. These submissions produced many comparable performance measurements that can be used for assessing the current state of the art of the field. There were numerous submissions that implemented the triangle counting challenge and resulted in over 350 distinct measurements. Analysis of these submissions show that their execution time is a strong function of the number of edges in the graph, , and is typically proportional to for large values of . Combining the model fits of the submissions presents a picture of the current state of the art of graph analysis, which is typically edges processed per second for graphs with edges. These results are times faster than serial implementations commonly used by many graph analysts and underscore the importance of making these performance benefits available to the broader community. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.
October 2017
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27 Reads
In this keynote, I will discuss the importance of human factors in cyber security and highlight lessons learned from conducting user-centered design activities with cyber security analysts. As network traffic volume, interconnectedness of devices, and sophistication of cyber threats all continue to increase, so do concerns about the complexity of providing cyber security. Many research efforts focus on the technology aspects of cyber security; few focus on studying the challenges faced by the human ecosystem of analysts, operators, and senior leaders. User-centered design can help uncover unmet needs and gather requirements to build effective systems to support those that perform cyber security work. Design methods in this domain can help establish user needs, identify opportunities for technology to assist, and evaluate concepts - in this, talk we will discuss examples of each. Ultimately, by embracing the human element of cyber, and positioning the human as the focal point of the research process, we can help the technology community be more efficient at building effective tools. We encourage future cyber security projects to broaden the research methodologies, methods, and techniques at their disposal in order to more completely explore this space. The talk will draw from research experience and field work with users on a number of cyber security research projects. Topics covered will include formative user research and the user-centered design process [1, 2], situation awareness prototyping efforts [3, 4], and evaluation methods for cyber security visualization tools [5].
October 2017
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350 Reads
Proceedings of the 2017 IEEE Symposium on Visualization for Cyber Security (VizSec)
October 2017
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35 Reads
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6 Citations
... However, our ATC algorithm differs from a typical triangle counting algorithm. In recent years, we have witnessed a surge of new triangle counting algorithms due to the HPEC GraphChallenge [39]. We also observed exciting efforts such as matrix multiplication-based triangle counting [40], new loadbalancing mechanisms [41], [42], and subgraph matchingbased triangle counting [43]. ...
Reference:
Anti-Section Transitive Closure
September 2020
... There were in total 14 review papers; some of them also presented theoretical or implementation proposals for further development of SA within SOC environments ( Ahmad et al., 2019 ;Andrade and Yoo, 2019 ;Brynielsson et al., 2016 ;Schuster, 2014 , 2016 ;Debatty and Mees, 2019 ;Franke and Brynielsson, 2014 ;Gomez et al., 2019 ;Gutzwiller et al., 2020 ;Hall et al., 2015 ;McNeese and Hall, 2017 ;Nazir and Han, 2022 ;Pahi et al., 2017 ). The reviews ranged in content from specifically addressing the current state of the art of SA within cybersecurity ( Franke and Brynielsson, 2014 ;Gutzwiller et al., 2020 ), to broader reviews concluding with the proposition of a model of cognitive processes within cyber security ( Andrade and Yoo, 2019 ). ...
June 2019
... Security awareness in cyber security domain can be described as the attainment of current knowledge and prediction of future knowledge about computer networks, missions and threats over neutral, friendly, and adversary forces [1]. The development of situation awareness (SA) has been demonstrated to be helpful in the cyber security domain to specify, analyze, evaluate, and predict human performance in the cyber attack incidents [2], which is especially crucial in real-time changing situations, where decision-making capacity is paramount to solving security tasks. ...
June 2019
... We follow an iterative approach in developing the guideline. In the first phase, two undergraduate students (who are co-authors of this paper) investigated the 2017 VAST Challenge Mini-Challenge 1 [30] following the pre-registration practice of articulating their analysis questions and hypotheses while minimizing their exposure to the raw data. Similar to the process of coding in qualitative studies [16], the two students examined the VAST Challenge independently and met over multiple sessions to achieve internal agreement between their listed analysis questions, as well as their overall analysis approach. ...
October 2017
... Due to the widely-recognized importance across various domains, a number of datasets and benchmarks have been developed for network and graph research, including graph data collections [19][20][21], graph learning [22][23][24][25][26][27][28], and diffusion model [29][30][31] (see Section 2 for details). However, none of them provides a comprehensive benchmark suite for flow problems on graphs. ...
September 2018
... The target audience for these challenges are any individual or team that seeks to highlight their contributions to graph and sparse data analysis software, hardware, algorithms, and/or systems. The Sparse DNN [1]- [9], Stochastic Block Partitioning [10]- [14], Research was sponsored by the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Department of the Air Force or the U.S. Government. ...
September 2017
... The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Subgraph Isomorphism [15]- [29], and PageRank [30]- [32] Graph Challenges have enabled a new generation of graph analysis by highlighting the benefits of novel innovations. Graph Challenge is part of the long tradition of challenges that have played a key role in advancing computation, AI, and other fields, such as, YOHO [33], MNIST [34], HPC Challenge [35], ImageNet [36] and VAST [37], [38]. ...
September 2017
... In addition to graph algorithms, some authors [23,34,35] introduced streaming algorithms [36] designed for partitioning dynamic graphs. Generally, dynamic graphs [36] are subject to frequent CRUD operations over the set of vertices and edges. ...
August 2017
... We compare our algorithm against the two latest champions of the Static Graph Challenge [4]. HavoqGT [28] uses a vertex-centric approach. ...
August 2017
... Similarly to Monroe et al. [5], we utilize action classes for creating simpler views. Another example of using action classes can be given based on the IEEE VAST Challenge 2015 story [65], in which visitors of an amusement park attended attractions grouped in 12 classes, such as "thrill rides", "shopping", "food", etc. ...
October 2015