Type: Package
Title: An Implementation of Process-Aware Stealthy Attack Detection(PASAD)
Version: 1.0
Date: 2021-06-22
Author: Donghwan Kim
Maintainer: Donghwan Kim <donhkim9714@korea.ac.kr>
Description: Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <doi:10.1145/3243734.3243781>. Also referred to the following implementation: https://github.com/rahulrajpl/PyPASAD.
License: GPL-3
Encoding: UTF-8
LazyData: no
Repository: CRAN
URL: https://github.com/ainsuotain/pasadr
Depends: R (≥ 3.5.0), pracma, scales
NeedsCompilation: no
Packaged: 2021-06-29 15:55:10 UTC; BISTel
Date/Publication: 2021-06-30 10:40:02 UTC

A test function of Process-Aware Stealthy Attack Detection(PASAD)

Description

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

Usage

pasad_test(obj, test_idx, newdata, r = 3, calib = 1, thres = NULL, movn = 10, plot = TRUE)

Arguments

obj

A pasad model object result from pasad_train.

test_idx

A test index for pasad. For example, test_idx = c(1:4801).

newdata

A test data for pasad.

r

A cardinal number of eigen value. Generally, r is same by the training phase.

calib

A threshold multiplier. since the threshold calculated during the training phase is a rather small, so the calib can be multiplied to the threshold. (default is 1, should be biggner than 0).

thres

If user already know the threshold, user can input the threshold.

movn

A moving average window value to see the trend of the score.

plot

Whether to draw a anomaly score or not. (default : TRUE).

Value

An object of class pasad_test.

total_scores

Anomaly score results from the pasad.

tr_score

A pasad score of the training index.

te_score

A pasad score of the test index.

extraced

A noise-reduced signal.

threshold

A (calculated) threshold.

outidx

An index of a anomaly score greater than the threshold.

Author(s)

Donghwan Kim
donhkim9714@korea.ac.kr dhkim2@bistel.com

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_train

Examples

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

# 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)
                  
# test whole data and check the anomaly score of test data
pred = pasad_test(obj = obj, 
                  test_idx = 1:4801,
                  newdata = sig, 
                  r = 1, 
                  calib = 1,  
                  plot = TRUE)
                  
# check the structure of test results
str(pred)

A training function 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
donhkim9714@korea.ac.kr dhkim2@bistel.com

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)