Journal article
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic
IEEE eTransactions on network and service management, Vol.13(3), pp.651-665
Sep 2016
Abstract
The monitoring and management of high-volume feature-rich traffic in large networks offers significant challenges in storage, transmission, and computational costs. The predominant approach to reducing these costs is based on performing a linear mapping of the data to a low-dimensional subspace such that a certain large percentage of the variance in the data is preserved in the low-dimensional representation. This variance-based subspace approach to dimensionality reduction forces a fixed choice of the number of dimensions, is not responsive to real-time shifts in observed traffic patterns, and is vulnerable to normal traffic spoofing. Based on theoretical insights proved in this paper, we propose a new distance-based approach to dimensionality reduction motivated by the fact that the real-time structural differences between the covariance matrices of the observed and the normal traffic is more relevant to anomaly detection than the structure of the training data alone. Our approach, called the distance-based subspace method, allows a different number of reduced dimensions in different time windows and arrives at only the number of dimensions necessary for effective anomaly detection. We present centralized and distributed versions of our algorithm and, using simulation on real traffic traces, demonstrate the qualitative and quantitative advantages of the distance-based subspace approach.
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Details
- Title
- A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic
- Creators
- Tingshan Huang - AkamaiHarish Sethu - Drexel UniversityNagarajan Kandasamy - Drexel University
- Publication Details
- IEEE eTransactions on network and service management, Vol.13(3), pp.651-665
- Publisher
- IEEE
- Grant note
- #1228847 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019168087704721
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