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Cyber-Physical Systems Virtual Organization
Read-only archive of site from September 29, 2023.
CPS-VO
increasing sparseness
biblio
Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder
Submitted by grigby1 on Fri, 07/03/2020 - 4:54pm
outlier error values
highly structured adjacency matrix structures
increasing sparseness
learning (artificial intelligence)
Matrix decomposition
Metrics
network based attacks
network emulator
network security
network traffic
online learning algorithm
original adjacency matrix
Heuristic algorithms
pubcrawl
representative ecommerce traffic
Resiliency
resultant adjacency matrix
serious economic consequences
singular value decomposition
sparse autoencoder
Sparse matrices
telecommunication traffic
time 225.0 min
decomposition
autoencoder hyper-parameters
Bipartite graph
composability
Compositionality
Computer crime
computer network security
contiguous time intervals
cyber physical systems
Data mining
DDoS Attacks
anomaly detection algorithm
dynamic bipartite graph increments
dynamic graph
dynamic graph mining
dynamic network traffic
Dynamic Networks and Security
ecommerce websites
error distribution
GAAD
gaussian distribution
graph theory
