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ABSTRACT
In recent times many malware attacks increasing in our society. Mainly image-
based malware attacks are spreading worldwide and many people get harmful
malware-based images through the technique called steganography. In the
existing system, only open malware and files from the internet is identified.
The image-based malware cannot be identified and detected so many phishers
make use of this technique and exploit the target. Social media platforms would be
totally harmful to the users. To avoid these difficulties, by implementing Machine
learning we can find the steganographic malware images(contents).
Our proposed methodology developing an Automation detection of malware and
steganographic content using Machine Learning. Steganography is the field of
hiding messages in apparently innocuous media (e.g., images), and steganalysis
is the field of detecting this covert malware.
We propose a machine learning (ML) approach to steganalysis. In the existing
system, only open malware and files from the internet are identified. But in recent
times many people get harmful malware-based images through the technique
called steganography. Social media platforms would be totally harmful to the
users.
To avoid these difficulties, by implementing Machine learning we can find the
steganographic malware images(contents). We use the steganalysis method using
machine learning for logistic classification. By using this we can spot and get
escape from the malware images sharing in social media like WhatsApp,
Facebook without downloading it. It can be also used for all the photo-sharing sites
such as google photos.
VI
LIST OF FIGURES
Figure no.
Name of the Figure
Page no.
4.1
Input JPG image
41
4.2
Output image
41
4.3
Change in Output image
42
4.4
Malware Detection simulation
43
4.5
RGB Layer Identification Step
44
5.1
LSB Graph
50
5.2
False rate graph
50
5.3
Output image
51
5.4
Output image
51
5.5
Binary code image
52
VII
TABLE OF CONTENT
CHAPTER NO. TITLE PAGE NO
1
INTRODUCTION 1
2
LITERATURE SURVEY
2.1 Survey Walk Through 2
2.2 Tensor Flow 2
2.3 Opencv 2
2.4 keras 6
2.5 Numpy 7
2.6 Neural Networks 9
2.7 Convolutional Neural Network 14
3 IMPLEMENTATION
3.1 Image Processing 19
3.1.1 Digital Image Processing 19
3.1.2 Pattern Recognition 20
3.2 Basic approaches to malware detection 21
