IEEE/ACM Transactions on Networking (TON)
Interconnections (2nd ed.): bridges, routers, switches, and internetworking protocols
Interconnections (2nd ed.): bridges, routers, switches, and internetworking protocols
Dynamics of complex systems
Self-stabilization
Code red worm propagation modeling and analysis
Proceedings of the 9th ACM conference on Computer and communications security
Route flap damping exacerbates internet routing convergence
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Observation and analysis of BGP behavior under stress
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Monitoring and early warning for internet worms
Proceedings of the 10th ACM conference on Computer and communications security
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
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Complex systems experience dramatic changes in behavior and can undergo transitions from functional to dysfunctional states. An unstable system is prone to dysfunctional collective cascades that result from self-reinforcing behaviors within the system. Because many human and technological civilian and military systems today are complex systems, understanding their susceptibility to collective failure is a critical problem. Understanding vulnerability in complex systems requires an approach that characterizes the coupled behaviors at multiple scales of cascading failures. We used neuromorphic methods, which are modeled on the pattern-recognition circuitry of the brain and can find patterns in high-dimensional data at multiple scales, to develop a procedure for identifying the vulnerabilities of complex systems. This procedure was tested on microdynamic Internet2 network data. The result was a generic pipeline for identifying extreme events in high dimensional datasets.