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REFERENCES
Aho, A. V., Sethi, R., and Ullman, J. D. (1986). Compilers,
Principles, Techniques. Addison Wesley.
Bilge, L. and Dumitras, T. (2012). Before we knew it: an
empirical study of zero-day attacks in the real world.
In Proc. of the 2012 ACM conference on Computer
and communications security, pages 833–844. ACM.
Chen, M., Ebert, D., Hagen, H., Laramee, R., Van Liere,
R., Ma, K.-L., Ribarsky, W., Scheuermann, G., and
Silver, D. (2009). Data, information, and knowledge
in visualization. Computer Graphics & Applications,
29(1):12–19.
Creech, G. and Hu, J. (2014). A Semantic Approach to
Host-Based Intrusion Detection Systems Using Con-
tiguousand Discontiguous System Call Patterns. Com-
puters, IEEE Transactions on, 63(4):807–819.
Dornhackl, H., Kadletz, K., Luh, R., and Tavolato, P.
(2014). Defining malicious behavior. In Ninth In-
ternational Conference on Availability Reliability and
Security (ARES), pages 273–278. IEEE.
Eiland, E., Evans, S., Markham, T., and Impson, J. (2012).
Mdl compress system and method for signature infer-
ence and masquerade intrusion detection. US Patent
8,327,443.
Filiol, E., Jacob, G., and Le Liard, M. (2007). Evaluation
methodology and theoretical model for antiviral be-
havioural detection strategies. Journal in Computer
Virology, 3(1):23–37.
Jacob, G., Debar, H., and Filiol, E. (2009). Malware behav-
ioral detection by attribute-automata using abstrac-
tion from platform and language. In International
Workshop on Recent Advances in Intrusion Detection,
pages 81–100. Springer.
Luh, R., Marschalek, S., Kaiser, M., Janicke, H., and Schrit-
twieser, S. (2016a). Semantics-aware detection of tar-
geted attacks: a survey. Journal of Computer Virology
and Hacking Techniques, pages 1–39.
Luh, R., Schrittwieser, S., and Marschalek, S. (2016b).
Taon: An ontology-based approach to mitigating tar-
geted attacks. In Proc. of the 18th Int. Conference on
Information Integration and Web-based Applications
& Services. ACM.
Luh, R., Schrittwieser, S., Marschalek, S., and Janicke, H.
(2017). Design of an Anomaly-based Threat Detec-
tion & Explication System In Proc. of the 3rd Int.
Conference on Information Systems Security & Pri-
vacy. SCITEPRESS.
Marschalek, S., Luh, R., Kaiser, M., and Schrittwieser, S.
(2015). Classifying malicious system behavior us-
ing event propagation trees. In Proc. of the 17th
Int. Conference on Information Integration and Web-
based Applications & Services. Association for Com-
putational Linguistics.
Miksch, S. and Aigner, W. (2014). A matter of time: Ap-
plying a data-users-tasks design triangle to visual an-
alytics of time-oriented data. Computers & Graphics,
38:286–290.
Munsey, C. (2013). Economic Espionage: Competing For
Trade By Stealing Industrial Secrets. Accessed 2015-
09-15.
Nevill-Manning, C. G. and Witten, I. H. (1997). Identify-
ing hierarchical structure in sequences: A linear-time
algorithm. J. Artif. Intell. Res. (JAIR), 7:67–82.
Rieck, K., Trinius, P., Willems, C., and Holz, T. (2011). Au-
tomatic analysis of malware behavior using machine
learning. Journal of Computer Security.
Rozenberg, G. (1997). Handbook of graph grammars and
computing by graph transformation, volume 1. World
Scientific.
Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedi-
hardjo, A. P., Chen, C., and Frankenstein, S. (2015).
Time series anomaly discovery with grammar-based
compression. In EDBT, pages 481–492.
Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedi-
hardjo, A. P., Chen, C., Frankenstein, S., and Lerner,
M. (2014). Grammarviz 2.0: a tool for grammar-based
pattern discovery in time series. In Joint European
Conference on Machine Learning and Knowledge
Discovery in Databases, pages 468–472. Springer.
Sood, A. K. and Enbody, R. J. (2013). Targeted cyberat-
tacks: a superset of advanced persistent threats. IEEE
security & privacy, (1):54–61.
Symantec (2015). Symantec Internet Security Threat Re-
port Volume 20. Whitepaper.
Thomas, J. J. and Cook, K. A., editors (2005). Illuminating
the Path: The Research and Development Agenda for
Visual Analytics. IEEE.
Wagner, M., Aigner, W., Rind, A., Dornhackl, H., Kadletz,
K., Luh, R., and Tavolato, P. (2014). Problem char-
acterization and abstraction for visual analytics in
behavior-based malware pattern analysis. In Whitley,
K., Engle, S., Harrison, L., Fischer, F., and Prigent,
N., editors, Proc. 11th Workshop on Visualization for
Cyber Security, VizSec, pages 9–16. ACM.
Wagner, M., Fischer, F., Luh, R., Haberson, A., Rind, A.,
Keim, D., Aigner, W., Borgo, R., Ganovelli, F., and
Viola, I. (2015). A Survey of Visualization Systems
for Malware Analysis. In Eurographics Conference
on Visualization, pages 105–125. EuroGraphics.
Wegner, P. (1997). Why interaction is more powerful than
algorithms. Communications of the ACM, 40(5):80–
91.
Zhao, C., Kong, J., and Zhang, K. (2010). Program behav-
ior discovery and verification: A graph grammar ap-
proach. IEEE Transactions on software Engineering,
36(3):431–448.
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