Machine Learning Applied to Cyber Operations

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.

[1]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[2]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[3]  Malcolm I. Heywood,et al.  Generating mimicry attacks using genetic programming: A benchmarking study , 2009, 2009 IEEE Symposium on Computational Intelligence in Cyber Security.

[4]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[5]  Jinsha Yuan,et al.  K-Mean Algorithm with a Distance Based on the Characteristic of Differences , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[6]  Misty Blowers,et al.  Analysis of machine learning models and prediction tools for paper machine systems , 2010 .

[7]  Huaiyu Zhu,et al.  No Free Lunch for Cross-Validation , 1996, Neural Computation.

[8]  David H. Wolpert,et al.  The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.

[9]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[10]  Dimitrios Gunopulos,et al.  Locally Adaptive Metric Nearest-Neighbor Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[12]  Eric B. Baum,et al.  Supervised Learning of Probability Distributions by Neural Networks , 1987, NIPS.

[13]  Nicu Sebe,et al.  Distance Learning for Similarity Estimation , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Glenn A. Fink,et al.  Final Report for Bio-Inspired Approaches to Moving-Target Defense Strategies , 2012 .

[15]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[16]  Brian Everitt,et al.  Cluster analysis , 1974 .

[17]  John R. Woodward,et al.  Computable and incomputable functions and search algorithms , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[18]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[19]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[20]  Susan M. Bridges,et al.  FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION , 2002 .

[21]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[22]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[23]  Glenn J. Myatt.  Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining , 2006 .

[24]  Wei Li,et al.  Using Genetic Algorithm for Network Intrusion Detection , 2004 .

[25]  David G. Stork,et al.  Pattern Classification , 1973 .

[26]  Glenn J. Myatt.  Making Sense of Data , 2007 .

[27]  H. B. Barlow,et al.  Unsupervised Learning , 1989, Neural Computation.

[28]  Amit Chugh.  GHIDS: A HYBRID HONEYPOT SYSTEM USING GENETIC ALGORITHM , 2012 .