pasad_train: A training function of Process-Aware Stealthy Attack...

In pasadr: An Implementation of Process-Aware Stealthy Attack Detection(PASAD)

Description

Singular value decomposition of log covariance matrix (Trajectory matrix). This is a training phase of pasad

Usage

pasad_train(x, train_idx, r = 3, ws, scree_plot = FALSE)

Arguments

x	A signal data for inspectation.
train_idx	A training index fot pasad. For example, train_idx = c(1:500).
r	A cardinal number of eigen value. Generally r is smaller than 3. (default : 3).
ws	A length of lag for creating covariance matrix. (default is a half of training length).
scree_plot	Whether to draw a scree_plot or not. (default : TRUE).

Value

An object of class pasad_train.

N	A length of signal data.
L	A length of lag for creating covariance matrix.
U	The r leading eigenvectors.
X	A trajectory matrix.
x	An original signal.
ws	A length of lag for creating covariance matrix.
train_idx	A training index fot pasad.
x_train	A data used for training.
singulars	A transpose of singular matrix

Author(s)

Donghwan Kim
[e-mail] [e-mail]

References

Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 817-831.
https://github.com/mikeliturbe/pasad https://github.com/rahulrajpl/PyPASAD

See Also

pasad_test

Examples

# data input
fpath = system.file("extdata", "sa.csv", package="pasadr")
sa = read.csv(fpath)

## NOT RUN:
## data(package = "pasadr")

# check data
sig = sa$V5
plot(sig)

# training using pasad and check the scree plot
train_idx = c(1:500)  
obj = pasad_train(x = sig, 
                  train_idx = train_idx,
                  r = 1, 
                  ws = length(train_idx)/2,
                  scree_plot = TRUE)
                  
# check the pasad model objects
str(obj)
                  

pasadr documentation built on June 30, 2021, 5:06 p.m.