Rethinking Concepts of the Dendritic Cell Algorithm for Multiple Data Stream Analysis

This paper begins by stating that the underlying concepts of signals and antigen used by the Dendritic Cell Algorithm are too abstract and arbitrary to be of use in real world applications as they stand. To address this, these concepts are more explicitly defined within a specific application area, namely that of data stream analysis. These new definitions are based around the outputs of the Change Point Detecting Subspace Tracker (CD-ST), a recently developed algorithm for detecting key change points across multiple data streams. Preliminary results demonstrate the utility of this new definition for antigen. The paper concludes by laying the theoretical groundwork for a novel anomaly detection framework for use in data streaming applications. The underlying methodology is to perform anomaly detection via the detection and classification of key change points that occur across the multiple data streams monitored.

[1]  Julie Greensmith,et al.  The dendritic cell algorithm , 2007 .

[2]  Gu Ji-yan,et al.  The Dendritic Cell Algorithm , 2011 .

[3]  Peter J. Bentley,et al.  Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm , 2006, ICARIS.

[4]  Muhammad Zubair Shafiq,et al.  A Sense of 'Danger' for Windows Processes , 2009, ICARIS.

[5]  Vipin Kumar,et al.  Chapman & Hall/CRC Data Mining and Knowledge Discovery Series , 2008 .

[6]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[7]  Graham Kendall,et al.  Geometrical insights into the dendritic cell algorithm , 2009, GECCO '09.

[8]  Julie Greensmith,et al.  Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows , 2008, ICARIS.

[9]  Ruy Luiz Milidiú,et al.  Data stream anomaly detection through principal subspace tracking , 2010, SAC '10.

[10]  Peter Strobach.  The fast recursive row-Householder subspace tracking algorithm , 2009, Signal Process..

[11]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[12]  Gene H. Golub,et al.  Matrix computations , 1983 .

[13]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[14]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[15]  Julie Greensmith,et al.  DCA for bot detection , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[16]  Graham Kendall,et al.  Frequency analysis for dendritic cell population tuning , 2008, Evol. Intell..

[17]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[18]  J. Nagy,et al.  Block RLS using row householder reflections , 1993 .

[19]  George V. Moustakides,et al.  Fast and Stable Subspace Tracking , 2008, IEEE Transactions on Signal Processing.

[20]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[21]  Julie Greensmith,et al.  Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm , 2011, ICARIS.

[22]  G. Golub,et al.  Tracking a few extreme singular values and vectors in signal processing , 1990, Proc. IEEE.