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ABSTRACT
Cyber-attack, via cyberspace, targeting an enterprise’s use of cyberspace for the
purpose of disrupting, disabling, destroying, or maliciously controlling a
computing environment/infrastructure; or destroying the integrity of the data or
stealing controlled information. The state of the cyberspace portends uncertainty
for the future Internet and its accelerated number of users. New paradigms add
more concerns with big data collected through device sensors divulging large
amounts of information, which can be used for targeted attacks. Though a
plethora of extant approaches, models and algorithms have provided the basis
for cyber-attack predictions, there is the need to consider new models and
algorithms, which are based on data representations other than task-specific
techniques. However, its non-linear information processing architecture can be
adapted towards learning the different data representations of network traffic to
classify type of network attack. In this paper, we model cyber-attack prediction
as a classification problem, Networking sectors have to predict the type of
Network attack from given dataset using machine learning techniques. The
analysis of dataset by supervised machine learning technique(SMLT) to capture
several information‘s like, variable identification, uni-variate analysis, bi-variate
and multi-variate analysis, missing value treatments etc. A comparative study
between machine learning algorithms had been carried out in order to determine
which algorithm is the most accurate in predicting the type cyber Attacks. We
classify four types of attacks are DOS Attack, R2L Attack, U2R Attack, Probe
attack. The results show that the effectiveness of the proposed machine learning
algorithm technique can be compared with best accuracy with entropy
calculation, precision, Recall, F1 Score, Sensitivity, Specificity and Entropy.
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TABLE OF CONTENT
SL.NO
TITLE
PAGE.NO
01
02
EXISTING SYSTEM
2.1 DRAWBACKS
12
03
INTRODUCTION
3.1 DATA SCIENCE
3.2 ARTIFICIAL INTELLIGENCE
13
04
MACHINE LEARNING
19
05
PREPARING DATASET
21
06
PROPOSED SYSTEM
6.1 ADVANTAGES
21
07
LITERATURE SURVEY
22
08
SYSTEM STUDY
8.1 OBJECTIVES
8.2 PROJECT GOAL
8.3 SCOPE OF THE PROJECT
30
09
FEASIBILITY STUDY
37
10
LIST OF MODULES
39
11
PROJECT REQUIREMENTS
11.1 FUNCTIONAL REQUIREMENTS
11.2 NON-FUNCTIONAL REQUIREMENTS
39
4
12
ENVIRONMENT REQUIREMENT
40
13
SOFTWARE DESCRIPTION
13.1 ANACONDA NAVIGATOR
13.2 JUPYTER NOTEBOOK
41
14
PYTHON
51
15
SYSTEM ARCHITECTURE
63
16
WORKFLOW DIAGRAM
64
17
USECASE DIAGRAM
65
18
CLASS DIAGRAM
66
19
ACTIVITY DIAGRAM
67
20
SEQUENCE DIAGRAM
68
21
ER – DIAGRAM
69
22
MODULE DESCRIPTION
22.1 MODULE DIAGRAM
22.2 MODULE GIVEN INPUT EXPECTED
OUTPUT
70
23
DEPLOYMENT (GUI)
94
24
CODING
95
25
CONCLUSION
141
26
FUTURE WORK
142
