Anomaly Detection Using Persistent Homology
| Title | Anomaly Detection Using Persistent Homology |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Bruillard, P., Nowak, K., Purvine, E. |
| Conference Name | 2016 Cybersecurity Symposium (CYBERSEC) |
| Date Published | apr |
| Keywords | anomaly detection, anomaly detection algorithm, compositionality, cyber network, cybersecurity, detection algorithms, dynamic point cloud, Human Behavior, human factors, Indexes, IP networks, Metrics, persistent homology, Ports (Computers), pubcrawl, Resiliency, security of data, set theory, statistical analysis, summary statistics, Three-dimensional displays, Tools, vulnerability detection |
| Abstract | Many aspects of our daily lives now rely on computers, including communications, transportation, government, finance, medicine, and education. However, with increased dependence comes increased vulnerability. Therefore recognizing attacks quickly is critical. In this paper, we introduce a new anomaly detection algorithm based on persistent homology, a tool which computes summary statistics of a manifold. The idea is to represent a cyber network with a dynamic point cloud and compare the statistics over time. The robustness of persistent homology makes for a very strong comparison invariant. |
| DOI | 10.1109/CYBERSEC.2016.009 |
| Citation Key | bruillard_anomaly_2016 |
- Metrics
- vulnerability detection
- tools
- Three-dimensional displays
- summary statistics
- statistical analysis
- set theory
- security of data
- Resiliency
- pubcrawl
- Ports (Computers)
- persistent homology
- Anomaly Detection
- IP networks
- Indexes
- Human Factors
- Human behavior
- dynamic point cloud
- detection algorithms
- Cybersecurity
- cyber network
- Compositionality
- anomaly detection algorithm