Book Image

Cyber Warfare – Truth, Tactics, and Strategies

By : Dr. Chase Cunningham
Book Image

Cyber Warfare – Truth, Tactics, and Strategies

By: Dr. Chase Cunningham

Overview of this book

The era of cyber warfare is now upon us. What we do now and how we determine what we will do in the future is the difference between whether our businesses live or die and whether our digital self survives the digital battlefield. Cyber Warfare – Truth, Tactics, and Strategies takes you on a journey through the myriad of cyber attacks and threats that are present in a world powered by AI, big data, autonomous vehicles, drones video, and social media. Dr. Chase Cunningham uses his military background to provide you with a unique perspective on cyber security and warfare. Moving away from a reactive stance to one that is forward-looking, he aims to prepare people and organizations to better defend themselves in a world where there are no borders or perimeters. He demonstrates how the cyber landscape is growing infinitely more complex and is continuously evolving at the speed of light. The book not only covers cyber warfare, but it also looks at the political, cultural, and geographical influences that pertain to these attack methods and helps you understand the motivation and impacts that are likely in each scenario. Cyber Warfare – Truth, Tactics, and Strategies is as real-life and up-to-date as cyber can possibly be, with examples of actual attacks and defense techniques, tools. and strategies presented for you to learn how to think about defending your own systems and data.
Table of Contents (14 chapters)
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Appendix – Major Cyber Incidents Throughout 2019

GANs power DeepFakes

In most ML algorithms of the past, the overarching methodology was to use a discriminative approach. The way that those ML applications work is that they seek to basically prove something is not what it claims to be. In a simple use case, consider a spam email. For a discriminative approach to work, the algorithm seeks to show that an email is not a valid email because of the contents within the email. In other words, using a sample of what is a known good bit of content, obviously at a large scale, the algorithm uses that known good content to judge subsequent submissions.

Unless a certain threshold is met, the algorithm works using that available data to prove that what was newly submitted is not a "good" email; it is spam. This works well mainly for this application because in this use case, most spam emails are relatively formulaic and typically are easily detected.

There are clear giveaways that the email does not contain "good&quot...