1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
|
library(data.table)
library(lattice)
library(caret)
library(nnet)
data <- fread("2020.10.02.csv")
data1<-fread("2020.10.03.csv")
data=rbind(data,data1)
#selecting few rows
library(fastDummies)
library(ggplot2)
library(plotly)
library(GGally)
#finding missing values in each column
colSums(is.na(data))
#each column missing values box plot comes here
#taking non missing rows alone
data_na_removed = na.omit(data)
#checking if missing values are gone
colSums(is.na(data_na_removed))
#box plot of label column comes here
#checking unique values
unique(data_na_removed$label)
# 1 - Benign 2 - Malicious 3 - Outlier
data_na_removed$label = factor(data_na_removed$label,
levels = c("benign", "malicious", "outlier"),
labels = c(1, 2, 3))
summary(data_na_removed$label)
data_na_removed
#summary(data_na_removed)
#ggpairs(data_na_removed)
#options(scipen = 999)
#ggplot(na.omit(data), aes(x=label, colour = label, fill = label), stat = "count") + geom_bar() +
# ggtitle("Distibution of Labels in Dataset") +
# labs(y = "Number of Cases", x = "Type of Label")
#cor.test(data_na_removed$entropy, as.numeric(data_na_removed$label))
#data_na_removed$label = as.numeric(data_na_removed$label)
#data_na_removed = data_na_removed[, -c(12, 13)]
#ggplot(data_na_removed, aes(x = entropy)) + geom_bar() +
# facet_wrap(~label)
#hist(data_na_removed$entropy, bins = 10)
data_na_removed$label=as.factor(data_na_removed$label)
training=createDataPartition(data_na_removed$label,p=0.6,list=FALSE)
train_set=data_na_removed[training,]
test_set=data_na_removed[-training,]
head(train_set)
model=train(data=train_set,label~.,method="nnet",tuneGrid=expand.grid(.size=c(5), .decay=0.1),trControl=trainControl(method="none",seeds = 123),MaxNWts=100,maxit=100)
confusionMatrix(train_set$label,predict(model,data=train_set))
test_set$test_pred=predict(model,newdata = test_set[,-15])
confusionMatrix(test_set$label, test_set$test_pred)
aggregate()
####kmeans
install.packages("ClusterR")
install.packages("cluster")
library(ClusterR)
library(cluster)
data_na_removed
dendogram=hclust(dist(data,method="euclidean"),method="complete")
data1=data_na_removed[,-15]
data1
kmeans1<- kmeans(data1, centers = 3)
cm=table(data_na_removed$label, kmeans1$cluster)
cm
confusionMatrix(cm)
###cart
data_na_removed
data1=data_na_removed[,c(4,5,10,11,15)]
data1
training=createDataPartition(data_na_removed$label,p=0.6,list=FALSE)
train_set=data_na_removed[training,]
test_set=data_na_removed[-training,]
model=train(data=train_set,label~.,method="rpart")
confusionMatrix(predict(model,new_data=test_set),train_set$label)
##doing data cleaning
data_na_removed=data_na_removed%>%mutate(timediff=time_end-time_start)
data_na_removed$time_end<-NULL
data_na_removed$time_start<-NULL
#SELECTING ONLY POSITIVES
data_na_removed=data_na_removed[data_na_removed$timediff>=1]
data_na_removed
data_na_removed=data_na_removed[data_na_removed$dest_ip%in% c(786 , 15169 ,202425 , 61337 , 49453 ,45899 , 7713 , 16276 , 49505,
57172 , 43350)]
data_na_removed$dest_ip=as.factor(data_na_removed$dest_ip)
summary(data_na_removed$dest_ip)
data_na_removed=data_na_removed[data_na_removed$dest_port %in% c(445,9200,22,5900,5060,53 , 5060 , 23 , 123 , 33522, 33524 ,
33518, 33504 , 33520,33524 , 33518, 33504, 33520,
33526 , 3389 , 33514, 33512 , 60490 , 60506, 60512 , 60510)]
data_na_removed$dest_port=as.factor(data_na_removed$dest_port )
summary(data_na_removed$dest_port)
data_na_removed=data_na_removed[data_na_removed$src_ip %in% c(786 , 45899 ,202425 , 7552 , 7713 , 49453 , 8048 , 18403 , 16276 , 43350 ,213371 ,
4134 , 34665,12389 , 200019 , 57172, 9299 , 12876, 8452 , 3462,
25019 , 24961 , 55836 , 45820 , 8151 , 45090,45595 , 9498 , 45903, 47331 , 4812 , 9121 ,
6503 , 9484 , 4837 , 8376 , 15895, 9009 , 6057 )]
data_na_removed$src_ip=as.factor(data_na_removed$src_ip)
summary(data_na_removed$src_ip)
data_na_removed=data_na_removed[data_na_removed$src_port %in% c(9200 , 33504 , 33524 , 33518 , 33514 , 33522 , 60510,
33512, 60516 , 33526 , 60490 , 33520 , 60512 ,60506 , 60514 , 60518 , 60508, 55336 , 55330, 55332 ,
55334 , 123, 53278 , 53020 , 32651 , 26042)]
data_na_removed$src_port=as.factor(data_na_removed$src_port)
summary(data_na_removed$src_port)
data_na_removed
dmy <- dummyVars(" ~dest_ip+dest_port+src_ip+src_port", data = data_na_removed)
trsf <- data.frame(predict(dmy, newdata = data_na_removed))
data_na_removed=cbind(data_na_removed,trsf)
data_na_removed=data_na_removed[,c(-4,-5,-10,-11)]
data_na_removed$timediff<-NULL
data_na_removed
data_na_removed$avg_ipt=scale(data_na_removed$avg_ipt)
data_na_removed$bytes_in=scale(data_na_removed$bytes_in)
data_na_removed$bytes_out=scale(data_na_removed$bytes_out)
data_na_removed$entropy=scale(data_na_removed$entropy)
data_na_removed$num_pkts_out=scale(data_na_removed$num_pkts_out)
data_na_removed$proto=scale(data_na_removed$proto)
data_na_removed$total_entropy=scale(data_na_removed$total_entropy)
data_na_removed$duration=scale(data_na_removed$duration)
summary(data_na_removed)
|
