| Description |
303 pages : illustrations ; 24 cm |
| Contents |
Chapter 1: Basics of Machine learning in cybersecurity -- What is machine learning?. Problems that machine learning solves ; Why use machine learning in cybersecurity? ; Current cybersecurity solutions ; Data in machine learning ; Different types of machine learning alogrithm ; Algorithms in machine learning ; The machine learning architecture ; Hands-on machine learning ; Summary -- Chapter 2 Time series analysis and ensemble modeling. What is a time series? ; Classes of time series models ; Time series decomposition ; Use cases for time series ; Time series analysis in cybersecurity ; Time series trends and seasonal spikes ; Predicting DDoS attacks ; Ensemble learning methods ; VOting ensemble method to detect cyber attacks ; Summary -- Chapter 3: Segregating legitimate and lousy URLs. Introduction to the types of abnormalities in URLs ; Using heuristics to detect malicious pages ; Using machine learning to detect malicious URLs ; Logistic regression to detect malicious URLs ; SVM to detect4 malicious URLs ; Multiclass classification for URL classification ; Summary -- Chapter 4: Knocking down CAPTCHAs. Characteristics of CAPTCHA ; Using artificial intelligence to crack CAPTCHA ; Summary -- Chapter 5: Using data science to catch email fraud and spam. Email spoofing ; Spam detection ; Summary -- Chapter 6: Efficient network anomaly detection using k-means. Stages of a network attack ; Dealing with lateral movement in networks ; Using Windows event logs to detect network anomalies ; Ingesting active directory data ; Data parsing ; Modeling ; Detecting anomalies in a network with k-means ; Summary -- Chapter 7: Decision tree and context based malicious event detection. Adware ; Bots ; Rugs ; Ransomware ; Rootkit ; Spyware ; Trojan horses ; Viruses ; Worms ; Malicious data injection within databases ; Malicious injections in wireless sensors ; Use case ; Revisiting malicious URL detection with decision trees ; Summary -- Chapter 8: Catching impersonators and hackers red handed. Different types of impersonation fraud ; Levenstein distance ; Summary -- Chapter 9: Changing the game with TensorFlow. Introduction to TensorFlow ; Installation of TensorFlow ; TensorFlow for Windows users ; Hello world in TensorFlow ; Importing the MNIST dataset ; Computation graphs ; Tensor processing unit ; Using TensorFlow for intrustion detection -- Summary -- Chapter 10: Financial fraud and how deep learning can mitigate it. Logistic regression classifier -- under-sampled data ; Deep learning time ; Summary -- Chapter 11: Case Studies. Introduction to our password dataset ; Summary |
| Summary |
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. Things you will learn: Use machine learning algorithms with complex datasets to implement cybersecurity concepts ; Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA ; Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes ; Use TensorFlow in the cybersecurity domain and implement real-world examples ; Learn how machine learning and Python can be used in complex cyber issues. -- Back cover |
| Notes |
Includes index |
| Subject |
Computer security
|
|
Machine learning
|
|
Computer Security
|
|
Computer security.
|
|
Machine learning.
|
| Author |
Ozdemir, Sinan, author
|
| ISBN |
1788992288 |
|
9781788992282 |
|
