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testing unsupervised
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jaketarnow committed Dec 7, 2016
1 parent f9fa836 commit 89c5d92
Showing 1 changed file with 114 additions and 80 deletions.
194 changes: 114 additions & 80 deletions intrusion-detector.R
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Original file line number Diff line number Diff line change
Expand Up @@ -323,33 +323,34 @@ test = kddcup.testdata.ten.percent
# Logistic Regression -----------------------------------------------------
glm.fit.time <- proc.time()

glm.fit = glm(access_type~
+flag
+src_bytes
+logged_in
+num_root
+num_file_creations
+count
+srv_count
+serror_rate
+srv_serror_rate
+rerror_rate
+srv_rerror_rate
+same_srv_rate
+diff_srv_rate
+srv_diff_host_rate
+dst_host_count
+dst_host_srv_count
+dst_host_same_srv_rate
+dst_host_diff_srv_rate
+dst_host_same_src_port_rate
+dst_host_srv_diff_host_rate
+dst_host_serror_rate
+dst_host_srv_serror_rate
+dst_host_rerror_rate
+dst_host_srv_rerror_rate
-access_type
-connection_type, data=train, family=binomial)
#glm.fit = glm(access_type~
# +flag
# +src_bytes
# +logged_in
# +num_root
# +num_file_creations
# +count
# +srv_count
# +serror_rate
# +srv_serror_rate
# +rerror_rate
# +srv_rerror_rate
# +same_srv_rate
# +diff_srv_rate
# +srv_diff_host_rate
# +dst_host_count
# +dst_host_srv_count
# +dst_host_same_srv_rate
# +dst_host_diff_srv_rate
# +dst_host_same_src_port_rate
# +dst_host_srv_diff_host_rate
# +dst_host_serror_rate
# +dst_host_srv_serror_rate
# +dst_host_rerror_rate
# +dst_host_srv_rerror_rate
# -access_type
# -connection_type, data=train, family=binomial)
glm.fit = glm(access_type~src_bytes+logged_in, data=train, family=binomial)
summary(glm.fit)
glm.probs = predict(glm.fit, newdata=kddcup.data.ten.percent, type = "response")
glm.pred = ifelse(glm.probs > 0.5, 1, 0)
Expand All @@ -364,33 +365,35 @@ rm(glm.fit.time)
# Linear Discriminant Analysis (LDA) --------------------------------------
lda.fit.time <- proc.time()

lda.fit = lda(access_type~
+flag
+src_bytes
+logged_in
+num_root
+num_file_creations
+count
+srv_count
+serror_rate
+srv_serror_rate
+rerror_rate
+srv_rerror_rate
+same_srv_rate
+diff_srv_rate
+srv_diff_host_rate
+dst_host_count
+dst_host_srv_count
+dst_host_same_srv_rate
+dst_host_diff_srv_rate
+dst_host_same_src_port_rate
+dst_host_srv_diff_host_rate
+dst_host_serror_rate
+dst_host_srv_serror_rate
+dst_host_rerror_rate
+dst_host_srv_rerror_rate
-access_type
-connection_type, data=train, family=binomial)
#lda.fit = lda(access_type~
# +flag
# +src_bytes
# +logged_in
# +num_root
# +num_file_creations
# +count
# +srv_count
# +serror_rate
# +srv_serror_rate
# +rerror_rate
# +srv_rerror_rate
# +same_srv_rate
# +diff_srv_rate
# +srv_diff_host_rate
# +dst_host_count
# +dst_host_srv_count
# +dst_host_same_srv_rate
# +dst_host_diff_srv_rate
# +dst_host_same_src_port_rate
# +dst_host_srv_diff_host_rate
# +dst_host_serror_rate
# +dst_host_srv_serror_rate
# +dst_host_rerror_rate
# +dst_host_srv_rerror_rate
# -access_type
# -connection_type, data=train, family=binomial)

lda.fit = lda(access_type~src_bytes+logged_in, data=train, family=binomial)
summary(lda.fit)
lda.pred = predict(lda.fit, newdata=kddcup.data.ten.percent, type = "response")
table(lda.pred$class, kddcup.data.ten.percent$access_type)
Expand All @@ -403,28 +406,34 @@ rm(lda.fit.time)

qda.fit.time <- proc.time()

qda.fit = qda(access_type~
+src_bytes
+logged_in
+count
+srv_count
+serror_rate
+srv_serror_rate
+rerror_rate
+srv_rerror_rate
+same_srv_rate
+diff_srv_rate
+srv_diff_host_rate
+dst_host_count
+dst_host_srv_count
+dst_host_same_srv_rate
+dst_host_diff_srv_rate
+dst_host_same_src_port_rate
+dst_host_srv_diff_host_rate
+dst_host_serror_rate
+dst_host_srv_serror_rate
+dst_host_rerror_rate
, data=train)
#qda.fit = qda(access_type~
# +flag
# +src_bytes
# +logged_in
# +num_root
# +num_file_creations
# +count
# +srv_count
# +serror_rate
# +srv_serror_rate
# +rerror_rate
# +srv_rerror_rate
# +same_srv_rate
# +diff_srv_rate
# +srv_diff_host_rate
# +dst_host_count
# +dst_host_srv_count
# +dst_host_same_srv_rate
# +dst_host_diff_srv_rate
# +dst_host_same_src_port_rate
# +dst_host_srv_diff_host_rate
# +dst_host_serror_rate
# +dst_host_srv_serror_rate
# +dst_host_rerror_rate
# +dst_host_srv_rerror_rate
# -access_type
# -connection_type, data=train, family=binomial)
qda.fit = qda(access_type~src_bytes+logged_in, data=train, family=binomial)
summary(qda.fit)
qda.pred = predict(qda.fit, newdata=kddcup.data.ten.percent)
table(qda.pred$class, kddcup.data.ten.percent$access_type)
Expand Down Expand Up @@ -474,12 +483,13 @@ plot(tree.model)

# Unsupervised
# Hierarchical Clustering
x =
hc.complete=hclust(dist(x),method="complete")
mydata = c(1,2,3,4,5,5,6,7,1,55,234,67,12,45,7)
x = dist(as.matrix(trainK))
hc.complete=hclust(x,method="complete")
plot(hc.complete)
hc.single=hclust(dist(x),method="single")
hc.single=hclust(x,method="single")
plot(hc.single)
hc.average=hclust(dist(x),method="average")
hc.average=hclust(x,method="average")
plot(hc.average)

# K Means
Expand All @@ -489,3 +499,27 @@ unique(kddcup.data.ten.percent$connection_type)
kCluster = kmeans(kddcup.data.ten.percent$connection_type[, 2:18], 4, nstart=20)
kCluster

install.packages("caret")
library(mlbench)
library(caret)
# load the dataset
data(kddcup.data.ten.percent)
# prepare training scheme
control <- trainControl(method="repeatedcv", number=10, repeats=3)
# train the LVQ model
set.seed(7)
modelLvq <- train(kddcup.data.ten.percent$connection_type~kddcup.data.ten.percent$src_bytes + kddcup.data.ten.percent$logged_in, data=kddcup.data.ten.percent, method="lvq", trControl=control)
# train the GBM model
set.seed(7)
modelGbm <- train(kddcup.data.ten.percent$connection_type~., data=kddcup.data.ten.percent, method="gbm", trControl=control, verbose=FALSE)
# train the SVM model
set.seed(7)
modelSvm <- train(kddcup.data.ten.percent$connection_type~., data=kddcup.data.ten.percent, method="svmRadial", trControl=control)
# collect resamples
results <- resamples(list(LVQ=modelLvq, GBM=modelGbm, SVM=modelSvm))
# summarize the distributions
summary(results)
# boxplots of results
bwplot(results)
# dot plots of results
dotplot(results)

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