This document presents information on malware detection using machine learning. It defines malware and describes common types like viruses, adware, ransomware, rootkits, and spyware. It also outlines malware detection methods and symptoms. Machine learning algorithms like decision trees, SVM, random forest, and XGBoost are proposed for detection. Existing systems apply techniques like malware behavior analysis, classification, and neural networks. The document concludes machine learning can accurately detect malware and help overcome drawbacks of previous systems.

1. Government Women Engineering College, Ajmer
(Bikaner Technical University, Bikaner)
Presentation
on
Malware Detection By Machine Learning
Submitted to: Submitted by:
Mr. Sudarshan Maurya Alisha Patidar
Assistant Professor B.tech VII Sem (CSE-A)
(Dept. of CSE) BTU Roll No.- 20EEMCS005

2. CONTENTS
 MALWARE
 Malware Detection
 Malware Attacks and How to Prevent Them
 Malware Symptoms
 Machine learning
 Proposed solutions with algorithms
 Existing Systems for malware detection using machine learning techniques
 Problem identified
 Conclusion
 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, bots, bugs, rootkits, spyware
 MALWARE DETECTOR :
Malware detection is the process of scanning the computer and files to detect
malware. It is effective at detecting malware because it involves multiple tools and
approaches. It's not a one way process, it's actually quite complex.

4. Malware Detection
 Malware detection is the process of scanning the computer and files to detect
malware. It is effective at detecting malware because it involves multiple tools
and approaches. It's not a one way process, it's actually quite complex.
 Malware Detection Methods :

5.  1. Viruses
• Viruses require human intervention to propagate.
• Once users download the malicious code onto their devices -- often delivered via malicious
advertisements or phishing emails the virus spreads throughout their systems.
• Viruses can modify computer functions and applications; copy, delete and exfiltrate data.
 2. Adware:
• It is capable of downloading or displaying advertisements to the device user.
• Not steel any data from the system but it forcing users to see ads.
• Some Irritating forms of adware display browser pop-ups that cannot be closed.
 3. Ransomware
• Ransomware locks or encrypts files or devices and forces victims to pay a ransom in exchange
for reentry. While ransomware and malware are often used synonymously, ransomware is a
specific form of malware.
• Types of Ransomware : Locker ransomware, Crypto ransomware, Triple extortion
ransomware,
Malware Attacks and How to Prevent Them

6.  4. Rootkits
• A rootkits is malicious software that enables threat actors to remotely access
and control a device.
• Rootkits facilitate the spread of other types of malware, including ransomware,
viruses and keyloggers.
• Rootkits often go undetected, because once inside a device, they can
deactivate antimalware and antivirus software.
• Rootkits typically enter devices and systems through phishing emails and
malicious attachments.
 5. Spyware
• Spyware is malware that downloads onto a device without the user's
knowledge.
• It steals users’ data to sell to advertisers and external users.
• Spyware can track credentials and obtain bank details and other sensitive data.
• It infects devices through malicious apps, links, websites and email
attachments.

7. How to prevent malware attacks
 Strong Cyber hygiene is the best defense against malware attacks. The premise of
cyber hygiene is similar to that of personal hygiene: If an organization maintains a high
level of health (security), it avoids getting sick (attacked).
 Cyber hygiene practices that prevent malware attacks include the following:
• Follow email security best practices.
• Deploy email security gateways.
• Avoid clicking links and downloading attachments.
• Implement strong access control.
• Require multifactor authentication.
• Use the principle of least privilege.
• Adopt a zero-trust security strategy.
• Monitor for abnormal or suspicious activity.

8. Malware Symptoms
 computers, they all can produce similar symptoms. Computers that are
infected
with malware can exhibit any of the following symptoms:
• Increased CPU usage
• Slow computer or web browser speeds
• Problems connecting to networks
• Freezing or crashing
• Modified or deleted files
• Appearance of strange files, programs, or desktop icons
• Programs running, turning off, or reconfiguring themselves (malware will
often reconfigure or turn off antivirus and firewall programs)
• Strange computer behavior
• Emails/messages being sent automatically and without user's knowledge (a
friend receives a strange email from you that you did not send)

9. 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

10. 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

11. Existing Systems for malware detection using
machine learning techniques
• Implement Machine Learning Pipeline: Leverage a machine learning pipeline for
malware detection, as illustrated in the provided figure, to enhance the system's
capabilities.
• Utilize Advanced Algorithms: Apply advanced machine learning algorithms to
analyze large volumes of data effectively, enhancing malware detection accuracy.
• Incorporate Dynamic Malware Detection: Focus on dynamic malware detection to
adapt to evolving threats, considering the progressive changes in malware behavior.
• Explore Automated System-Level Detection: Investigate automated system-level
malware detection, exploring fundamentals and the current status quo in machine
learning-based detection systems.
• Consider Proposed Techniques: Evaluate proposed methods, like the one
demonstrating effectiveness in Android devices for automated malware detection.
• Regularly Update Models: Keep machine learning models updated to stay resilient
against emerging malware threats.

12. 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

13. 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

14. 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

15. Thank You