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Towards Fundamental Science of Cyber Security Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Machine Learning Applied to Cyber Operations

Authors : Misty Blowers, Jonathan Williams

Published in: Network Science and Cybersecurity

Publisher: Springer New York

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Abstract

Cyber attacks have evolved from operational to strategic events, with the aim to disrupt and influence strategic capability and assets, impede business operations, and target physical assets and mission critical information. With this emerging sophistication, current Intrusion Detection Systems (IDS) are also constantly evolving. As new viruses have emerged, the technologies used to detect them have also become more complex relying on sophisticated heuristics. Hosts and networks are constantly evolving with both security upgrades and topology changes. In addition, at most critical points of vulnerability, there are often vigilant humans in the loop.

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Literature
1.
D.H. Wolpert, The lack of a priori distinctions between learning algorithms. Neural Comput 8(7), 1341–1390 (1996)CrossRef
2.
H Zhu. No free lunch for cross validation. Neural Comput. 8(7), 1421–1426 (1996)
3.
K. Mehrotra, C. Mohan, S. Ranka, Elements of Artificial Neural Networks (The MIT Press, Cambridge, 2000)
4.
E. Baum, F. Wilczek. Supervised learning of probability distributions by neural networks. Neural Inf. Proces. Syst. 1 52–61 (1988)
5.
G. Scott. Knowledge-based artificial neural networks for process modeling and control (The University of Wisconsin, Madison, 1993)
6.
7.
J. Woodward, Computable and incomputable functions and search algorithms. Intell. Comput. Intell. Syst. 1, 871–875 (2009)
8.
9.
H. Barlow. Possible principles underlying the transformations of sensory messages. ed. by W. Rosenblith. Sensory Communication (MIT Press, Cambridge, 1961)
10.
Z. Li, J. Yuan, H. Yang, K. Khang. K-mean Algorithm with a distance based on characteristics of differences, in 4th International Conference on Wireless Communications, Networking and Mobile Computing, Oct 2008, pp. 1–4
11.
J. Han, M. Kamber, Data Mining: Concepts and Techniques (Academic Press, San Diego, 2001)
12.
R. Duda, P. Hart, D. Stork, Pattern Classification (John Wiley & Sons, New York, 2001)MATH
13.
T. Pang-Ning, M. Steinbach, V. Kumar. Introduction to Data Mining (Addison-Wesley, Boston, 2005)
14.
J. Yu, J. Amores, N. Sebe, P. Radeva, Q. Tian, Distance learning for similarity estimation. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 451–462 (2008)CrossRef
15.
C. Domeniconi, J. Peng, D. Gunopulos, Locally adaptive metric nearest neighbor classification. IEEE Trans. Pattern. Anal. Mach. Intell. 24(9), 1281–1285 (2002)CrossRef
16.
E. Xing, A. Ng, M. Jordan, S. Russell. Distance metric learning, with application to clustering with side-information. in Proceedings of Neural Information Processing Systems, 2003, pp. 505–512
17.
18.
G. Myatt. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining (John Wiley & Sons, New York, 2007
19.
L. Heyer et al. Exploring expression data: identification and analysis of coexpressed genes. Genome Res. 9, 1106–1115 (1999)
20.
L. Zadeh. Fuzzy sets. Inf. Control 8, 338–353 (1965). As sited by Martin
21.
M. Ester, H.-P. Kriegel, J. Sander, X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), ed. by E. Simoudis, J. Han, U.M. Fayyad. AAAI Press, pp. 226–231. ISBN 1-57735-004-9
22.
23.
H. Gunes Kayacik, A.N. Zincir-Heywood, M.I. Heywood, S. Burschka. Generating mimicry attacks using genetic programming: a benchmarking study, in IEEE Symposium on Computational Intelligence in Cyber Security, 2009
24.
M. Blowers. Analysis of Machine Learning Models and Prediction Tools for Paper Machine Systems. Dissertation, State University of New York College of Environmental Science and Forestry, 2010
25.
26.
W. Stitieler, Multivariate Statistics with Applications in Statistical Ecology (International Co-operative Publishing House, Fairland, 1979)
27.
J. Han, M. Kamber. Data Mining Concepts and Techniques, 2nd edn (Morgan Kaufmann, San Francisco, 2006). ISBN 1-55860-9016
28.
M. Blowers, C. Salisbury. Method and apparatus for event detection permitting per event adjustment of false alarm rate. United States Patent Application 20120271782
29.
G.A. Fink, C. S. Oehmen. Final Report for Bio-Inspired Approaches to Moving-Target Defense Strategies. No. PNNL-21854. Pacific Northwest National Laboratory (PNNL), Richland, WA , 2012
30.
31.
32.
33.
R.M. Chandrasekaran, M. Govindarajan. Bagged genetic algorithm for network intrusion detection. Int. J. Res. Rev. Inf. Secur. Priv. 1(2), 33+ (2011)
34.
S. Akbar, K. Nageswara Rao, J.A. Chandulal. Implementing rule based genetic algorithm as a solution for intrusion detection system. Int. J. Comput. Sci. Netw. Secur. 11(8), 138 (2011)
35.
W. Li. Using genetic algorithm for network intrusion detection, in Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference, Kansas City, Kansas, 2004, pp. 24–27
36.
A. Goyal, C. Kumar. GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System. not published. Electrical Engineering and Computer Science, Northwestern University, Evanston, IL (2007)
37.
A.C. Divya. GHIDS: a hybrid honeypot system using genetic algorithm. Int. J. Comput. Technol. Appl. 3, 187 (2012)
38.
J.M. Smith. Evolution and the Theory of Games Springer US, 1993, pp. 202-215
39.
W. Li. Using genetic algorithm for network intrusion detection, in Proceedings of the United States Department of Energy Cyber Security Group, 2004
40.
S.M. Bridges, R.B. Vaughn. Fuzzy data mining and genetic algorithms applied to intrusion Detection, in Proceedings of 12th Annual Canadian Information Technology Security Symposium, 2000, pp. 109–122
Metadata
Title
Machine Learning Applied to Cyber Operations
Authors
Misty Blowers
Jonathan Williams
Copyright Year
2014
Publisher
Springer New York
Book
Network Science and Cybersecurity

Print ISBN: 978-1-4614-7596-5

Electronic ISBN: 978-1-4614-7597-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4614-7597-2_10

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