Your Introduction to AI and Machine Learning in Cybersecurity

By The Cylance Data Science Team

The field of artificial intelligence (AI) encompasses a broad range of technologies intended to endow computers with human-like capabilities for learning, reasoning and drawing user insights. This book introduces you to the machine learning techniques and methods that are more applicable to the modern profile of security problems.

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Chapter 1

Clustering

Clustering encompasses a variety of techniques for sub-dividing samples into distinct sub-groups or clusters based on similarities among their key features and attributes. Clustering is particularly useful in data exploration and forensic analysis thanks to its ability to sift through vast quantities of data to identify outliers and anomalies that require further investigation.

Chapter 2

Classification

Classification encompasses a set of computational methods for predicting the likelihood that a given sample belongs to a predefined class, e.g., whether a given piece of email is spam or not.

Chapter 3

Probability

We consider probability as a predictive modeling technique for classifying and clustering samples. Including foundational concepts, such as trial, outcome, and event, along with the differences between the joint and conditional types of probability.

Chapter 4

Deep Learning

Deep Learning encompasses a wide range of learning methods primarily based on the use of neural networks, a class of algorithms so named because they simulate the ways densely interconnected networks of neurons interact in the brain. In this chapter, we consider how two types of neural networks can be applied to solve a classification problem.

01

Chapter 1

Clustering

Clustering encompasses a variety of techniques for sub-dividing samples into distinct sub-groups or clusters based on similarities among their key features and attributes. Clustering is particularly useful in data exploration and forensic analysis thanks to its ability to sift through vast quantities of data to identify outliers and anomalies that require further investigation.

02

Chapter 2

Classification

Classification encompasses a set of computational methods for predicting the likelihood that a given sample belongs to a predefined class, e.g., whether a given piece of email is spam or not.

03

Chapter 3

Probability

We consider probability as a predictive modeling technique for classifying and clustering samples. Including foundational concepts, such as trial, outcome, and event, along with the differences between the joint and conditional types of probability.

04

Chapter 4

Deep Learning

Deep Learning encompasses a wide range of learning methods primarily based on the use of neural networks, a class of algorithms so named because they simulate the ways densely interconnected networks of neurons interact in the brain. In this chapter, we consider how two types of neural networks can be applied to solve a classification problem.

About the Authors

Brian Wallace

Author

Brian Wallace is a Security Researcher and Data Scientist at Cylance with experience in software engineering, reverse engineering, malware analysis, machine learning, vulnerability research, cryptography and more. As the primary researcher responsible for exposing the threat actor behind Operation Cleaver, he also has experience as a Threat Actor Investigator.

Sepehr Akhavan-Masouleh

Author

Sepehr Akhavan-Masouleh is a data scientist who works on application of statistical and machine learning models in cyber-security with a Ph.D. from University of California, Irvine.

Andrew Davis

Author

Andrew Davis is a neural network wizard wielding a Ph.D. in computer engineering from University of Tennessee.

Mike Wojnowicz

Author

Mike Wojnowicz is a data scientist with a PhD. From Cornell University who enjoys developing and deploying large-scale probabilitistic models due to their interpretability.

John H. Brock

Author

Data scientist John H. Brock researches applications of machine learning to static malware detection and analysis, holds and M.S. in computer science from University of California, Irvine and can usually be found debugging Lovecraftian open source code while mumbling to himself about the virtues of unit testing.

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