This document discusses using machine learning for malware detection. It defines malware and machine learning, describes existing malware detection systems and their limitations, identifies problems with current approaches, and proposes using algorithms like decision trees, SVM, random forest and XGBoost to classify malware more accurately. Functional requirements and conclusions are noted but not described in detail.

1. Malware Detection by
Machine Learning
Shivam Vatshayan
Software Engineer

2. CONTENTS
 MALWARE
 Machine learning
 Existing system
 Problem identified
 Proposed solutions with algorithms
 Functional requirements
 Context diagrams
 Use case diagrams
 References

3. MALWARE
 Malware is any software intentionally designed to cause damage to a computer, server,
client, or computer network. A wide variety of malware types exist, including computer
viruses, worms, Trojan horses, ransomware, spyware, adware, rogue software, wiper
and scareware.
 Types of malware
• Trojan horse
• Virus
• Adware
• Bitware

4. MACHINE LEARNING
 Machine learning is a method of data analysis that automates analytical model
building. It is a branch of artificial intelligence based on the idea that systems
can learn from data, identify patterns and make decisions with minimal human
intervention.
 Types of machine learning
 Supervised learning
 Unsupervised learning
 Reinforcement learning

5. EXISTING SYSTEMS
 Malware detection by using window api sequence and machine learning
 Detecting unknown malicious code by applying classification techniques on
oppose patterns
 Detecting scareware by mining variable length instructions sequence
 Accurate adware detection using oppose sequence extraction
 Detection of spyware by mining executable files
 Detection by using neural networks on the malware

6. PROBLEM IDENTIFIED
 Detecting unknown malicious code by applying classifications techniques
on oppose pattern : Evaluated number of experiments and found that
setting of 2 grams, TF, using 300 features selected by Df measured outperform
the perform lacks ML specific techniques
 Detecting scareware by Mining variable length instructions sequence: This
paper present the static analysis method based on data mining which extends
the general heuristic detection techniques using a variable length instructions
sequence mining approach for purpose of scareware detection but metrics
specific and unsupervised techniques un included can be broken

7. PROPOSED SOLUTION WITH ALGORITHMS
 Machine learning can easily identify the malware in the data and datasets
 Different types of machine learning algorithms are applied such as :
 DECISION TREE
 SVM
 Random forest
 XG boost

8. FUNCTIONAL REQUIREMENTS

9. CONCLUSION
 A Malware is critical threat to user computer system in terms of stealing
confidential information or disabling security.
 This project present some of the existing machine learning algorithms directly
applied on the data or datasets of malware
 It explains the how the algorithms will play a role in detecting malware wit
high accuracy and predictions
 We are also using data science and data mining techniques to overcome the
drawbacks of existing system

10. REFERENCES
 https://en.wikipedia.org/wiki/Malware
 https://en.wikipedia.org/wiki/Machine_learning
 https://en.wikipedia.org/wiki/Supervised_learning
 https://en.wikipedia.org/wiki/Spamming
 https://www.researchgate.net/publication/343499527_Project_report_Malwa
re_analysis
 https://towardsdatascience.com/malware-detection-using-deep-learning-
6c95dd235432

11. Thanks
Hi there
I am Shivam Vatshayan
vatshayan007@gmail.com
Software engineer