- Anomaly Detection in Multilayer Networks
- Project Year:
2017
- REU Student (s):
Gianna Schwarz | Rutgers University
- Student 1 Institution:
Rutgers University-New Brunswick
- Project Mentor:
Lazaros Gallos
- Project Mentor Area:
DIMACS
- Project Abstract:
Anomaly detection methods play an important role in keeping Internet operating without interruptions.
While many methods rely on centralized detection, there are many benefits in implementing distributed
methods where Internet nodes can decide whether there is an ongoing attack using local information only.
In this work, I extend a distributed anomaly detection algorithm, DIAMoND, for the case of attacks on
both layers of a two-layer network system. The nodes in each layer, which may represent the Internet and
the power-grid, communicate with each other and can share information on their status, without sharing
sensitive information such as the amount of traffic they handle. The main contribution of this work is to
implement a system of trust among layers, so that a node weighs differently information received by
nodes in different layers. I show that changes in the trust parameter can influence the accuracy of attack
detection.
