1. GOVERNMENT ENGINEERING COLLEGE
HASSAN
Seminar topic on:
Malware Detection using Machine Learning
Under the Guidance of : Presented by:
Dr. T G Basavaraju BE,ME,PHD Savitha c
Head of Department CS&E 4GH19CS416
GEC Hassan
2. CONTENTS
1. Introduction
2. Types of Malware
3. Malware detection using Neural networks
4. Malware detection using Naïve Bayes
5. Approaches of malware detection
6. Methodology
7. Advantages
8. Applications
9. Conclusion
10. References
3. INTRODUCTION
Malware, short for malicious software, is a sweeping term for
viruses, worms, trojans and other harmful software programs which
can either create harm to data or access some important data
illegally.
There are various methods of arranging malware the first is by how
the malicious software spreads.
A worm is an independent piece of malicious software that
duplicates itself and spreads from one PC to another.
4. Types Of Malware
There are 3 types of malwares :-
1. Ransomware
2. Spyware
3. Adware
• Ransomware :-
Ransomware is a kind of malware that scrambles your hard drives records
and requests an installment, ordinarily in Bitcoin, in returns for the decryption key. A
few prominent malware flare-ups of the most recent couple of years, like Petya, are
ransomware.
5. • Spyware :-
Spyware is characterized by Webroot Cybersecurity as “malware
utilized with the end goal of covertly assembling information on a clueless
client”.
• Adware :-
Adware is malware that powers your program to divert to web
commercials, which frequently try themselves to download further, more
malicious software.
6. Malware detection using Neural
networks
• Neural Networks are essentially a piece of deep learning, which thus is a subset of
machine learning .
• Neural networks are only an exceptionally currently progressed use of machine
learning that is currently discovering applications in numerous fields of interest.
• Neural networks are a class of machine learning algorithms which is used to
demonstrate complex patterns in datasets using different secret layers and non-
straight actuation functions.
• A neural networks is a series of algorithms that endeavors to perceive basic
relationships in a set of data through a process that mimics the manner in which
the human mind operates.
7. Malware Detection using Naive Bayes
• It is a classification technique dependent on Bayes' Theorem with a
suspicion of freedom among indicators.
• In straightforward terms, a Naïve Bayes classifier expects that the
presence of a specific element in a class is inconsequential to the
presence of some other feature.
• A naïve bayes classifier accepts that the presence of a specific element of
a class is disconnected to the presence of some other component, given
the class variable.
8. Approaches to Malware Detection
• An efficient, robust and scalable malware recognition module is the key
component of every cybersecurity product.
• Malware recognition modules decide if an object is a threat based on they
collected on it.
• This data may be collected at different phases:-
1. Pre-execution phase
2. Post-execution phase
9. • Pre-execution phase:-
Data is anything you can tell about a file without executing it. This may include
executable file format descriptions, code descriptions, binary data statistics, text strings and
information extracted via code emulation and other similar data.
• Post-execution phase:-
data conveys information about behavior or events caused by process activity in a
system. In the early part of the cyber era, the number of malware threats was relatively low,
and simple manually created pre-execution rules were often enough to detect threats.
10. METHODOLOGY
• There are three main methods used to malware detection:-
1. Signature based
2. Behavioral based
3. Heuristic based
Signature based:-
As computer usage became more common, it became easier for attackers to
spread their malicious code. This method is called signature based detection.
11. ADVANTAGES
• It can detect known as well as new.
• Unknown instances of malware .
• It identifies vulnerabilities in a runtime environment.
• It focuses on the behavior of system to detect unknown attack.
12. APPLICATIONS
• Data mining techniques through have increased using machine learning to
recognize malicious files.
• Malware is a any type of malicious software designed to harm or exploit any
programmable device, service or network.
13. CONCLUSION
• Malware is a critical threat to users computer system in terms of stealing
confidential information, corrupting or disabling security system.
• According to their comparative study we are going to use advanced malware
detection technique i.e. data mining and machine learning method to overcome
the drawbacks of existing malware detection techniques .
• We explore the various different types of the modals which are used by various
researchers in the malware detection and highlight the accuracy of these models.
• As per the accuracy of the results we found that the malware detection based on
the neural networks are more effective and accurate as compared to the other
approaches.
14. REFERENCES
• Mohsen Kakavand Mohammad Dabbagh and Ali. Dehghantanha Application of Machine Learning Algorithms for
Android Malware Detection pp. 32-36 2018.
• M. Kalash M. Rochan N. Mohammed N. D. Bruce Y. Wang and F. Iqbal "Malware classification with deep
convolutional neural networks" 2018 9th IFIP International Conference on New technologies Mobility and
Security (NTMS) pp. 1-5 2018 February.
• A. Mujumdar G. Masiwal and D. B. Meshram "Analysis of signature-based and behavior-based anti-malware
approaches" International Journal of Advanced Research in Computer Engineering and Technology (IJARCET)
vol. 2 no. 6 2013.
• I. Burguera U. Zurutuza and S. Nadjm-Tehrani "Crowdroid: behavior-based malware detection system for
Android" Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices pp.
15-26 2011 October.
• D. Gavrilut M. Cimpoesu D. Anton and L. Ciortuz Malware Detection Using Machine Learning Proceedings of
the International Multiconference on Computer Science and InformationTechnology pp. 735-741 2009.
