Big Data Analytics and Intelligent Systems for Cyber Threat Intelligence

Cyber-threat protection is critical for single users and businesses, and big data analytics and machine intelligence-based techniques can be used to detect and prevent attacks. This book brings together researchers in the field to advance the mission of anticipating, preventing, preparing, and responding to internal security threats.

Format: Hardback
Length: 280 pages
Publication date: 28 April 2023
Publisher: River Publishers

In recent years, a significant amount of effort has been dedicated to safeguarding computer systems against cyber threats, which is one of the most crucial cybersecurity tasks for individuals and businesses alike. Even a single attack can lead to compromised data and substantial losses, making it imperative to implement robust protection measures. The prevalence of massive losses and frequent attacks has underscored the urgent need for efficient detection methods. Current static and dynamic approaches, while effective in certain scenarios, fall short when confronted with zero-day attacks. Consequently, big data analytics and machine intelligence-based techniques have emerged as powerful tools to address this challenge.

This book serves as a valuable resource for researchers and practitioners in the field of big data analytics and intelligent systems for cyber threat intelligence (CTI). It brings together experts from various domains to advance the mission of anticipating, preventing, preventing, preparing, and responding to internal security threats. The diverse range of topics covered in the book offers readers a comprehensive perspective on various disciplines related to big data analytics and intelligent systems for CTI applications.

The book delves into technical topics such as:

Big data analytics for cyber threat intelligence and detection: This section explores the use of big data analytics techniques to analyze large volumes of data and extract valuable insights for CTI purposes. It covers methods such as data mining, machine learning, and natural language processing to identify patterns, anomalies, and threats.

Artificial intelligence analytics techniques: Artificial intelligence plays a pivotal role in CTI, and this section explores various AI-based analytics techniques such as neural networks, deep learning, and natural language processing. It discusses their applications in malware detection, intrusion detection, and cyber threat analysis.

Real-time situational awareness: The ability to detect and respond to threats in real-time is critical for effective CTI. This section discusses the development of real-time situational awareness systems that leverage big data analytics and machine learning to provide timely alerts and notifications.

Machine learning techniques for CTI: Machine learning algorithms are increasingly being used to improve the accuracy and efficiency of CTI systems. This section covers various machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, and their applications in CTI.

Deep learning techniques for CTI: Deep learning, a subset of machine learning, has shown remarkable promise in CTI applications. This section explores the use of deep learning algorithms for tasks such as image recognition, speech recognition, and natural language processing.

Malware detection and prevention techniques: Malware is a major threat to computer systems, and this section discusses various techniques for detecting and preventing malware attacks. It covers methods such as antivirus software, intrusion detection systems, and behavioral analysis.

Intrusion and cybersecurity threat detection and analysis: This section focuses on the detection and analysis of cybersecurity threats, including intrusion detection systems, network security, and threat intelligence. It discusses the development of effective strategies for identifying and mitigating threats.

Blockchain and machine learning techniques for CTI: Blockchain technology has gained significant attention in recent years for its potential applications in CTI. This section explores the use of blockchain and machine learning techniques to enhance the security and integrity of CTI systems.

By bringing together researchers and practitioners from diverse fields, this book provides a comprehensive understanding of big data analytics and intelligent systems for CTI. It serves as a valuable resource for those seeking to advance their knowledge and expertise in this critical area of cybersecurity.

Dimension: 234 x 156 (mm)
ISBN-13: 9788770227780