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Artificial Intelligence for Cybersecurity
Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity: Develop AI approaches to solve cybersecurity problems in your organization

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Profile Icon Bojan Kolosnjaji Profile Icon Huang Xiao Profile Icon Peng Xu Profile Icon Apostolis Zarras
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€33.99
Paperback Oct 2024 358 pages 1st Edition
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€33.99
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Arrow left icon
Profile Icon Bojan Kolosnjaji Profile Icon Huang Xiao Profile Icon Peng Xu Profile Icon Apostolis Zarras
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€33.99
Paperback Oct 2024 358 pages 1st Edition
eBook
€8.99 €26.99
Paperback
€33.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €26.99
Paperback
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Artificial Intelligence for Cybersecurity

Big Data in Cybersecurity

In this chapter, we will explore the significance of big data in cybersecurity. More precisely, it will encompass an overview of challenges, applications, and technologies associated with big data in cybersecurity, along with considerations related to privacy and ethics. Whether you are new to the concept of big data in cybersecurity or seeking to deepen your understanding, this chapter will provide valuable insights and detailed information.

In this chapter, we’re going to cover the following main topics:

  • What is big data?
  • Big data challenges in cybersecurity
  • Big data applications in cybersecurity
  • Big data technologies for cybersecurity

By the end of this chapter, you will have gained a comprehensive understanding of how big data is reshaping the landscape of cybersecurity. From grasping the fundamental concept of big data and its distinctions from conventional data processing to navigating the intricate challenges it...

Technical requirements

There are no specific technical prerequisites for delving into this chapter, apart from a basic understanding of computer science concepts. Whether you’re a cybersecurity enthusiast looking to explore the broader implications of big data or a professional seeking to deepen your understanding of its applications, this chapter is designed to be accessible to a wide range of readers. It offers insights and explanations in a clear and approachable manner, making the content valuable for both technical and non-technical individuals interested in the intersection of big data and cybersecurity.

What is big data?

Before delving into the introduction of big data, it is essential to understand the concept of data. Data processed by a computer comprises quantities, characters, or symbols, which can be stored, transmitted, and recorded as electrical signals on magnetic, optical, or mechanical media. Big data, on the other hand, refers to an extensive collection of data that is massive in volume and continues to grow exponentially over time. It is characterized by its substantial size and complexity, to the extent that traditional data management tools cannot efficiently store and process it. Big data is a unique form of data that presents immense challenges and opportunities due to its sheer magnitude. Let’s now explore these distinctive features, or the four Vs of big data, in detail:

  • Volume: Big data refers to vast amounts of data generated, collected, and stored by various sources, including sensors, social media, transactional data, and more. The sheer volume...

Big data challenges in cybersecurity

In today’s digital world, the proliferation of connected devices and the increasing digitization of information have led to a staggering volume of data generated in cyberspace. Big data presents unique challenges in the context of cybersecurity. Big data presents unique challenges in the context of cybersecurity. The volume, velocity, variety, and veracity of data generated in cyberspace can overwhelm traditional cybersecurity practices. The sheer volume of data generated by devices, networks, and applications can be massive and difficult to manage, making it challenging to detect anomalies or identify patterns indicative of cyber threats. The velocity at which data is generated and transmitted in cyberspace requires timely and efficient processing for effective cybersecurity. The variety of data types, formats, and sources, including logs, network traffic, social media, and sensor data, adds complexity to the analysis process. Moreover,...

Big data applications in cybersecurity

Big data has become increasingly relevant in cybersecurity because it can unlock insights and identify patterns that may indicate cyber threats. Organizations and analysts use big data to improve their cybersecurity posture by enhancing their threat detection and mitigation capabilities.

One significant application of big data in cybersecurity is TI. TI involves collecting and analyzing large volumes of data from various sources to identify patterns and trends in cyber-attacks. Big data techniques such as ML, data mining, and NLP are used to extract and analyze information from structured and unstructured data sources. This information is used to build threat models that help organizations and analysts identify and respond to emerging cyber threats more quickly and effectively. TI has become a critical component of cybersecurity, enabling defenders to stay ahead of cybercriminals and protect against sophisticated attacks.

Another application...

Big data technologies for cybersecurity

In recent years, big data technologies have played a significant role in advancing cybersecurity practices. With the growth of big data in cybersecurity, organizations have turned to various technologies to help manage and analyze large volumes of data to detect and respond to cyber threats. Distributed computing frameworks are a crucial technology in big data for cybersecurity. These frameworks enable processing massive amounts of data by distributing the workload across many nodes. Apache Hadoop is one of cybersecurity’s most popular distributed computing frameworks. It is an open source software (OSS) framework that enables storing and processing large datasets in a distributed computing environment. Hadoop Distributed File System (HDFS) allows for the distributed storage and processing of large datasets, and its MapReduce programming model facilitates the parallel processing of data. Apache Spark is another popular distributed computing...

Summary

Big data is critical in cybersecurity, presenting challenges and opportunities. With the proliferation of connected devices and the increasing digitization of information, modern society faces an overwhelming volume, velocity, and variety of data, making it harder to detect and mitigate cyber threats. Traditional cybersecurity methods may not be sufficient; thus, we must invest in advanced technologies and skilled cybersecurity professionals to effectively handle the complexity and scale of big data in cyberspace.

Big data applications in cybersecurity, such as TI, anomaly detection, behavior analysis, and log analysis, are used to identify and respond to cyber threats promptly. These advanced analytics techniques rely on distributed computing frameworks, stream processing platforms, and data storage and management technologies to process and analyze big data at scale.

Overall, understanding the significance of big data in cybersecurity is essential for effectively improving...

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Key benefits

  • Familiarize yourself with AI methods and approaches and see how they fit into cybersecurity
  • Learn how to design solutions in cybersecurity that include AI as a key feature
  • Acquire practical AI skills using step-by-step exercises and code examples
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Artificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.

Who is this book for?

This book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.

What you will learn

  • Recognize AI as a powerful tool for intelligence analysis of cybersecurity data
  • Explore all the components and workflow of an AI solution
  • Find out how to design an AI-based solution for cybersecurity
  • Discover how to test various AI-based cybersecurity solutions
  • Evaluate your AI solution and describe its advantages to your organization
  • Avoid common pitfalls and difficulties when implementing AI solutions
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Publication date : Oct 31, 2024
Length: 358 pages
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Language : English
ISBN-13 : 9781805124962
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Publication date : Oct 31, 2024
Length: 358 pages
Edition : 1st
Language : English
ISBN-13 : 9781805124962
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Table of Contents

26 Chapters
Part 1: Data-Driven Cybersecurity and AI Chevron down icon Chevron up icon
Chapter 1: Big Data in Cybersecurity Chevron down icon Chevron up icon
Chapter 2: Automation in Cybersecurity Chevron down icon Chevron up icon
Chapter 3: Cybersecurity Data Analytics Chevron down icon Chevron up icon
Part 2: AI and Where It Fits In Chevron down icon Chevron up icon
Chapter 4: AI, Machine Learning, and Statistics - A Taxonomy Chevron down icon Chevron up icon
Chapter 5: AI Problems and Methods Chevron down icon Chevron up icon
Chapter 6: Workflow, Tools, and Libraries in AI Projects Chevron down icon Chevron up icon
Part 3: Applications of AI in Cybersecurity Chevron down icon Chevron up icon
Chapter 7: Malware and Network Intrusion Detection and Analysis Chevron down icon Chevron up icon
Chapter 8: User and Entity Behavior Analysis Chevron down icon Chevron up icon
Chapter 9: Fraud, Spam, and Phishing Detection Chevron down icon Chevron up icon
Chapter 10: User Authentication and Access Control Chevron down icon Chevron up icon
Chapter 11: Threat Intelligence Chevron down icon Chevron up icon
Chapter 12: Anomaly Detection in Industrial Control Systems Chevron down icon Chevron up icon
Chapter 13: Large Language Models and Cybersecurity Chevron down icon Chevron up icon
Part 4: Common Problems When Applying AI in Cybersecurity Chevron down icon Chevron up icon
Chapter 14: Data Quality and its Usage in the AI and LLM Era Chevron down icon Chevron up icon
Chapter 15: Correlation, Causation, Bias, and Variance Chevron down icon Chevron up icon
Chapter 16: Evaluation, Monitoring, and Feedback Loop Chevron down icon Chevron up icon
Chapter 17: Learning in a Changing and Adversarial Environment Chevron down icon Chevron up icon
Chapter 18: Privacy, Accountability, Explainability, and Trust – Responsible AI Chevron down icon Chevron up icon
Part 5: Final Remarks and Takeaways Chevron down icon Chevron up icon
Chapter 19: Summary Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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