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William Acosta locking of existing data is necessary. It also allows for the analysis framework to make use novel summary data structures and algorithms that can incorporate the changes made to the data without requiring analysis of the full dataset. 2.1 Storage and data management The large quantity of data makes a centralized storage solution unfeasible; instead, a distributed storage solution Is favored. The parallel nature of many of the algorithms makes a distributed solution not only more feasible, but also desirable. Distributed storage systems such as Google’s BigTable (Chang et al. 2006), Yahoo’s PNUTS (Cooper et al. 2008), and Amazon’s Dynamo (DeCandia et al. 2007) provide the low-level mechanisms for storing, and managing large quantities of data. These systems were designed to support coordinated reads and updates of data in a distributed environment. To support the needs of applications like cyber-warfare threat detection, a distributed storage system should provide efficient, low-level support for append-only writes of raw data, as well as efficient tracking of incremental additions and updates of the dataset. 2.2 Distributed processing of data Recently, there has been a great deal of research in Google’s MapReduce (Dean & Ghemawat 2004) distributed computing software framework for processing large datasets. However, its batch-oriented nature was not designed to deal with incremental or continuous data updates. This makes it unsuitable for a variety of applications including cyber-warfare threat analysis and detection. Systems like Haloop (Bu et al. 2010) and MapReduce Online (Condie et al. 2010) have sought to add continuous query support to MapReduce. To achieve this, these systems had to make fundamental changes to the API and underlying architecture of MapReduce. This paper argues that what is needed instead is a system designed from the ground-up to support the demands of analysis and mining algorithms on large sets of continuously generated data. 2.3 Data management and analysis The problem of analyzing continuous data has been explored by stream databases (Abadi et al. 2005, Shah et al. 2004). Similarly, continuous queries in databases have been proposed with systems such as TelegraphCQ (Chandrasekaran et al. 2003) and CQL (Arasu et al. 2006). These systems can handle processing queries on streams of data with long-running/continuous queries. However, they lack the ability to support analytic algorithms over a large and diverse dataset. In contrast, verticallypartitioned databases such as C-Store (Stonebraker et al. 2005) excel at fast and efficient support of complex analytics. Unfortunately, vertically-partitioned databases suffer from poor performance on writes. In essence, insertions and updates require that the index be rebuilt. Although performance of reads is very fast once the index is built, building the index is very expensive. What is needed is a system that can perform complex analytics on continuous data without requiring a complex index to be completely rebuilt as a result of data updates. This paper proposes a new, incremental indexing system that keeps track of summarized historical data while allowing for many small [incremental] updates to be incorporated. The key difference is that, unlike traditional database indexes, the new incremental index would not be build off-line (batch-process). Instead, the index would incorporate the many incremental updates on-line so that the index of past data is always active and valid. In addition to the storage and distributed computing framework, it is also important to consider the needs of the algorithms that will be used in the system. Applications with such diverse data require equally diverse analysis. For example, detecting hidden correlations and associations between events seen in server logs requires mining association rules (Agrawal & Srikant 1994) whereas detecting interaction of attackers in a network may involve graph theoretic algorithms. 3. Conclusion This paper presents a case for a new distributed computing system that is explicitly designed to meet the unique needs of applications such as cyber-warfare threat detection. The system should support large quantities of diverse data such as server logs, emails, social-network data, etc. It should allow for a variety of mining and analysis algorithms and support for those algorithms to be processed in a parallel and distributed manner. The system must not only meet these needs, but also do so in a way that can efficiently support continuous analysis of data that is continuously generated. 318
References William Acosta Abadi, D. J., Ahmad, Y., Balazinska, M., Cherniack, M., hyon Hwang, J., Lindner, W., Maskey, A. S., Rasin, E., Ryvkina, E., Tatbul, N., Xing, Y. & Zdonik, S. (2005), The design of the borealis stream processing engine, in ‘CIDR ’05: Proceedings of the second biennial Conference on Innovative Data Systems Research’, pp. 277–289. Agrawal, R. & Srikant, R. (1994), Fast algorithms for mining association rules, in J. B. Bocca, M. Jarke & C. Zaniolo, eds, ‘Proc. 20th Int. Conf. Very Large Data Bases, VLDB’, Morgan Kaufmann, pp. 487–499. Arasu, A., Babu, S. & Widom, J. (2006), ‘The cql continuous query language: semantic foundations and query execution’, The VLDB Journal 15(2), 121–142. Bu, Y., Howe, B., Balazinska, M. & Ernst, M. D. (2010), Haloop: Efficient iterative data processing on large clusters, in ‘Proceedings of the VLDB Endowment’, Vol. 3. Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M. J., Hellerstein, J. M., Hong, W., Krishnamurthy, S., Madden, S., Raman, V., Reiss, F. & Shah, M. (2003), Telegraphcq: Continuous dataflow processing for an uncertain world, in ‘CIDR ’03: Proceedings of the first biennial Conference on Innovative Data Systems Research’. Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A. & Gruber, R. E. (2006), Bigtable: A distributed storage system for structured data, in ‘Proceedings of the 7th symposium on Operating systems design and implementation (OSDI ’06)’, Seattle, WA. Condie, T., Conway, N., Alvaro, P., Elmeleegy, J. M. H. K. & Sears, R. (2010), Mapreduce online, in ‘Proceedings of the Seventh USENIX Symposium on Networked System Design and Implementation (NSDI 2010)’, San Jose, CA. Cooper, B. F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., arno Jacobsen, H., Puz, N., Weaver, D. & Yerneni, R. (2008), Pnuts: Yahoo!s hosted data serving platform, in ‘Proceedings of the 34th International Conference on Very Large Data Bases (VLDB ’08)’, Auckland, New Zealand. Dean, J. & Ghemawat, S. (2004), Mapreduce: simplified data processing on large clusters, in ‘OSDI’04: Proceedings of the 6th conference on Symposium on Operating Systems Design & Implementation’, USENIX Association, Berkeley, CA, USA, pp. 10–10. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P. & Vogels, W. (2007), Dynamo: amazon’s highly available key-value store, in ‘SOSP ’07: Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles’, ACM, New York, NY, USA, pp. 205–220. Metz, C. (2010), ‘Google search index splits with mapreduce’. URL: http: // www. theregister. co. uk/ 2010/ 09/ 09/ google_ caffeine_ explained/ Myers, J., Grimaila, M. & Mills, R. (2010), Insider threat detection using distributed event correlation of web server logs, in ‘ICIW ’10: Proceedings of the 5th International Conference on Information-Warfare and Security’. Peng, D. & Dabek, F. (2010), Large-scale incremental processing using distributed transactions and notifications, in ‘OSDI ’10: Proceedings of the Ninth USENIX Symposium on Operating Systems Design and Implementation’. Shah, M. A., Hellerstein, J. M. & Brewer, E. (2004), Highly available, fault-tolerant, parallel dataflows, in ‘SIGMOD ’04: Proceedings of the 2004 ACM SIGMOD international conference on Management of data’, ACM, New York, NY, USA, pp. 827–838. Stonebraker, M., Abadi, D. J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S., O’Neil, E., O’Neil, P., Rasin, A., Tran, N. & Zdonik, S. (2005), C-store: a column-oriented dbms, in ‘VLDB ’05: Proceedings of the 31st international conference on Very large data bases’, VLDB Endowment, pp. 553–564. 319
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The Proceedings of the 6th Internat
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Contents Paper Title Author(s) Page
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Preface These Proceedings are the w
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Biographies of contributing authors
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Department of Computer Science, IQR
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Using the Longest Common Substring
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Jaime Acosta Some techniques that u
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Jaime Acosta assigned by an anti-vi
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Jaime Acosta Cormen, T.H., Leiserso
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Hind Al Falasi and Liren Zhang tran
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Hind Al Falasi and Liren Zhang ther
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Hind Al Falasi and Liren Zhang the
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Edwin Leigh Armistead and Thomas Mu
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Deception Operations Security (OPS
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The Uses and Limits of Game Theory
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3. Limits to using game theory 3.1
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Merritt Baer Effective cyberintrusi
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Merritt Baer 1.5), there seems to b
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Merritt Baer Report of the Defense
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Ivan Burke and Renier van Heerden F
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Ivan Burke and Renier van Heerden a
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Marco Carvalho et al. systems start
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Marco Carvalho et al. Benatallah, 2
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Marco Carvalho et al. management la
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Marco Carvalho et al. Figure 4: Sta
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Marco Carvalho et al. Sorrels, D.,
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Manoj Cherukuri and Srinivas Mukkam
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Mecealus Cronkrite et al. to see bo
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Mecealus Cronkrite et al. trivial.
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Mecealus Cronkrite et al. socially
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Mecealus Cronkrite et al. The views
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Vincent Garramone and Daniel Likari
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Stephen Groat et al. Sections 4 and
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Stephen Groat et al. probability fo
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Stephen Groat et al. host. In this
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Stephen Groat et al. changing addre
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Marthie Grobler et al. leadership,
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Marthie Grobler et al. apply to sta
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Marthie Grobler et al. 6. Working t
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Cyber Strategy and the Law of Armed
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Ulf Haeussler Alliance and Allies r
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Ulf Haeussler following the invocat
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Ulf Haeussler NCSA (2009) NCSA Supp
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Karim Hamza and Van Dalen of respon
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Karim Hamza and Van Dalen From a mi
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Karim Hamza and Van Dalen productiv
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Intelligence-Driven Computer Networ
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Eric Hutchins et al. of defensive a
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Eric Hutchins et al. Defenders can
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Eric Hutchins et al. Equally as imp
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Eric Hutchins et al. X-Mailer: Yaho
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Eric Hutchins et al. Received: (qma
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Eric Hutchins et al. U.S.-China Eco
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Saara Jantunen and Aki-Mauri Huhtin
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Brian Jewell and Justin Beaver In t
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Brian Jewell and Justin Beaver Figu
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4. Evaluation Brian Jewell and Just
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Brian Jewell and Justin Beaver othe
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Detection of YASS Using Calibration
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Kesav Kancherla and Srinivas Mukkam
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M M M Su−2 Sv ∑∑ u= 1 v= 1 h(
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5.2 ROC curves Kesav Kancherla and
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Developing a Knowledge System for I
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Louise Leenen et al. We distinction
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Louise Leenen et al. There is growi
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3.1 Needs analysis Louise Leenen et
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Louise Leenen et al. Kroenke, D.M.
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Jose Mas y Rubi et al. As we can se
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Figure 2: CALEA forensic model (Pel
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Jose Mas y Rubi et al. Table 2: Com
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Jose Mas y Rubi et al. Another pend
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Tree of Objectives Acknowledgements
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Secure Proactive Recovery - a Hardw
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Ruchika Mehresh et al. implementing
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Ruchika Mehresh et al. The coordina
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Ruchika Mehresh et al. multiplicati
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Ruchika Mehresh et al. Table 2: App
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2. Network infiltration detection D
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David Merritt and Barry Mullins on
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David Merritt and Barry Mullins Ess
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David Merritt and Barry Mullins Dev
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Muhammad Naveed Pakistan Computer E
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Muhammad Naveed response could also
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Muhammad Naveed Table 10: Aggressiv
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Muhammad Naveed 2006 Tcp Open Mysql
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Muhammad Naveed 8009 Tcp Open Ajp13
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Muhammad Naveed Austalian Taxation
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Alexandru Nitu world and bring it i
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Alexandru Nitu Article 51 restricts
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Alexandru Nitu As IW strategy and t
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Cyberwarfare and Anonymity Christop
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Christopher Perr attacks again help
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Christopher Perr about the current
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Catch me if you can: Cyber Anonymit
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David Rohret and Michael Kraft reve
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David Rohret and Michael Kraft sary
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Data (Evidence) Removal Shield Davi
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Neutrality in the Context of Cyberw
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Julie Ryan and Daniel Ryan 18th cen
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Julie Ryan and Daniel Ryan “Decla
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Julie Ryan and Daniel Ryan von Glah
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Harm Schotanus et al. In the remain
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Harm Schotanus et al. 2.3.1 Secure
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Harm Schotanus et al. In this setup
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Harm Schotanus et al. the label (by
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Harm Schotanus et al. these aspects
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Maria Semmelrock-Picej et al. they
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Maria Semmelrock-Picej et al. User
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Maria Semmelrock-Picej et al. SPIKE
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Maria Semmelrock-Picej et al. A cl
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Maria Semmelrock-Picej et al. in co
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Maria Semmelrock-Picej et al. In th
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Maria Semmelrock-Picej et al. Fuchs
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Madhu Shankarapani and Srinivas Muk
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Figure 1: UPX packed Trojan Figure
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Trojan.Zb ot- 1342.mal Trojan.Sp y.
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Madhu Shankarapani and Srinivas Muk
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Namosha Veerasamy and Marthie Grobl
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Page 277 and 278: 4. Conclusion Namosha Veerasamy and
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Page 299 and 300: Michael Bilzor a diverse base of U.
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Page 327: Large-Scale Analysis of Continuous
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