Practical AI for Cybersecurity
- Available for pre-order. Item will ship after February 26, 2021
The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way.
IT Security teams in businesses and corporations are struggling daily in order to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to try to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced.
IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very time consuming and laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds.
What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later.
Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.
Table of Contents
Chapter 1. The Role of Artificial Intelligence in Cybersecurity. Chapter 2. Machine Learning. Chapter 3. Neural Networks. Chapter 4. Computer Vision. Chapter 5. Conclusions.
Ravi Das is a Business Development Specialist for BN.Net™ Cybersecurity, a leading Cybersecurity communications and journalism firm based in the Greater Chicago area. He holds an M.S. in Agribusiness Economics with a specialization in Exchange Rate Risk Theory, and an M.B.A in Management Information Systems. He has published four books, which include “Biometric Technology: Authentication, Biocrpytography, and Cloud-based Architecture” (CRC Press); “Adopting Biometric Technology: Challenges and Solutions” (CRC Press); “The Science of Biometrics: Security Technology for Identity Verification” (Routledge); “Protecting Information Assets and IT Infrastructure In The Cloud” (CRC Press) and “Testing and Securing Web Applications”.