Preface
Chapter 1, Basics of Machine Learning in Cyber Security
The key focus of the chapter is allow readers to get familiarized with machine learning,
how is it practiced and its need in Cyber Security domain. This chapter builds on using
machine learning instead of conventional rule based engines while allowing the readers to
tackle challenges in the cyber security domain. Further, this chapter allow readers to get
hands-on knowledge on python, MongoDB and various libraries for machine learning and
concepts related to supervised and unsupervised learning.
Chapter 2, Time series Analysis and Ensemble Modeling
The first phase of the threat lifecycle deals with Reconnaissance where malwares/APTS
passively engages the target by searching through public information, penetrating
confidential corporate documents and so on. Time series Analysis helps detect packets
exchanged at odd hours or identify spikes seen during holidays or odd hours in the
corporate network.Given a sequence numbers for time series dataset we can restructure the
data model to look like a supervised learning by using the values at previous point in time
to predict the value at next point. Time
Similarly Ensembles methods are techniques that give boost in accuracy on predictions
made by our model. Meta learning from the ensemble algorithm helps identify the
resources which are getting exploited to continue the reconnaissance activity.
In this chapter we will implement two examples on ensemble modeling and time series
forecasting each.
Chapter 3, Segregating Legitimate and Lousyc
In the threat kill chain the initial attack often starts with “URL Injection” in emails,
attachments etcetera. Detecting bad urls in the initial stages of the attack help security
professionals to combat them early on. Thus, Having learnt the base concepts of machine
learning this chapter will focus on building practical skills and will discuss examples of
machine learning Implementation on URLs, identifying the good, bad and the worst of
URLs through an intelligent machine learning based python driven example application.
Additionally, this chapter will provide hands-on knowledge to the concepts learnt in the
first chapter as well as testing the accuracy of result generated by our program.
Chapter 4, Knocking Down Captchas
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