Book Image

Hands-On Machine Learning for Cybersecurity

By : Soma Halder, Sinan Ozdemir
Book Image

Hands-On Machine Learning for Cybersecurity

By: Soma Halder, Sinan Ozdemir

Overview of this book

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems
Table of Contents (13 chapters)
Free Chapter
Basics of Machine Learning in Cybersecurity
Using Data Science to Catch Email Fraud and Spam

Efficient Network Anomaly Detection Using k-means

Network attacks are on the rise, and a lot of research work has been done to thwart the negative effects from such attacks. As discussed in the previous chapters, we identify attacks as any unauthorized attempt to do the following:

  • Get hold of information
  • Modify information
  • Disrupt services
  • Perform distributed denial of service to and from the server where information is stored
  • Exploit using malware and viruses
  • Privilege escalation and credential compromise

Network anomalies are unlike regular network infections by viruses. In such cases, network anomalies are detected by identifying non-conforming patterns in the network data. Not just network intrusion detection, such methods can also be used for other forms of outlier detection such as credit fraud, traffic violation detection, and customer churn detection.

This chapter will...