DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

100%
1
95%
2
14 15
64%
TAO 4
64%
62%
5 6 7
59%
VI. CONCLUSIONS
In this paper, we developed a methodology for extracting
features by characterizing the time series’ and relating it to
stress situations in distributed multiagent systems. One
important aspect of our approach is that we identify the
stress situations of the agents in the society by observing
local behavior of one representative agent. This approach is
motivated by the fact that a local time series can have the
information of the dynamics of entire system in
deterministic dynamic systems. It is important to identify the
situations of other agents when agents are interdependent in
networked systems.
When we have a large society we will be able to predict
the stress levels in some other agents in the society. This
helps in invoking an appropriate control policy. For example
by studying the local behavior of TAO during certain time,
we may be able to estimate that agent 11’s OpTempo is high
with 62% accuracy. This may need us to reduce the amount
of tasks to the agent as high OpTempo requires more
computational resource.
To extract meaningful behavioral parameters we collected
the time series data from a representative agent and
computed 38 statistical and deterministic parameters to
represent its behavior. Discriminability Index defined by us
in this paper as a measure of the discriminating power of the
parameters seems to be a promising direction for agent
behavior estimation. Using those selected parameters we
validated our approach through identifying the stress
situations using k-nearest neighbor algorithm with the index
values as weights. Although our analysis showed that
deterministic parameters don’t have significant ability to
identify stress situations in our stress space, it is possible
that they can be good indicators under other stress space
such as security and robustness stresses.
8
9
16 17
52%
10
62%
Fig. 5. Correct estimation with proportional weights to DI
11
12
13
[2] O. F. Rana and K. Stout, “What is scalability in multi-agent systems?”
in Proc. 4th Int. Conf. Autonomous Agents, 2000, pp. 56–63.
[3] N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw,
“Geometry from a time series,” Physical Review Letters, vol. 45, pp.
712–716, 1980.
[4] F. Taken, “Detecting strange attractors in turbulence,” in Dynamical
Systems and Turbulence, D. Rand and L.-S. Young, Eds. Springer:
Berlin, 1981, pp. 366–381.
[5] A. P. Moore, R. J. Ellison, and R. C. Linger, “Attack modeling for
information security and survivability,” Software Engineering
Institute, Carnegie Mellon University, Pittsburg, PA, Tech. Note
CMU/SEI-2001-TN-001, 2001.
[6] F. Moberg, “Security analysis of an information system using an
attack tree-based methodology,” M.S. thesis, Automation Engineering
Program, Chalmers University of Technology, Sweden, 2000.
[7] S. Jha and J. M. Wing, “Survivability analysis of networked systems,”
in Proc. 23rd Int. Conf. Software engineering, 2001, pp. 307–317.
[8] A. M. Fraser and H. Swinney, “Independent coordinates for strange
attractors from mutual information,” Physical Review A, vol. 33, pp.
1134–1140, 1986.
[9] H. G. Schuster, Deterministic Chaos: An Introduction,
Verlagsgesellshaft: Weinheim, 1989.
[10] H. D. I. Abarbanel, M. E. Gilpin, and M. Rotenberg, Analysis of
Observed Chaotic Data, Springer: New York, 1998.
[11] P. Grassberger and I. Procaccia, “Characterization of strange
attractors,” Physical Review Letters, vol. 50, pp. 346–349, 1983.
[12] P. Grassberger and I. Procaccia, “Characterization of strange
attractors,” Physica D, vol. 9, pp. 189–208, 1983.
[13] A. Wolf, J. B. Swift, H. L. Swinney, and J. Vastano, “Determining
Lyapunov exponents from a time series,” Physica D, vol. 16, pp. 285–
317, 1985.
[14] T. M. Mitchell, Machine Learning, MaGraw-Hill, pp. 230–236, 1997.
REFERENCES
[1] R. Ellison, D. Fisher, H. Lipson, T. Longstaff, and N. Mead,
“Survivable network systems: An emerging discipline,” Software
Engineering Institute, Carnegie Mellon University, Pittsburg, PA,
Tech. Rep. CMU/SEI-97-153, 1997.