23 Result(s)
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Chapter
AI-Enabled Cybersecurity for IoT and Smart City Applications
AI-driven cybersecurity is crucial to enhancing the resilience of the Internet of Things (IoT) and smart city ecosystems. Due to the dynamic and heterogeneous nature of IoT devices, these interconnected networ...
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Chapter
AI for Critical Infrastructure Protection and Resilience
This chapter explores how artificial intelligence (AI) can be used to enhance the protection and resilience of critical infrastructure. Society is becoming increasingly dependent on interconnected systems, whi...
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Chapter
Cybersecurity Background Knowledge: Terminologies, Attack Frameworks, and Security Life Cycle
This chapter provides a foundational understanding of cybersecurity concepts, including terminologies and attack frameworks like the cyber kill chain and MITRE ATT&CK, as well as the cybersecurity life cycle. ...
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Chapter
Detecting Anomalies and Multi-attacks Through Cyber Learning: An Experimental Analysis
Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity. The knowledge of artificial intelligence (AI), particularly the machine learning techniques, can b...
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Chapter
Cybersecurity Data Science: Toward Advanced Analytics, Knowledge, and Rule Discovery for Explainable AI Modeling
In a computing context, cybersecurity technology and operations are constantly changing, and data science is driving the change. Building a data-driven model that extracts patterns in cybersecurity incidents i...
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Chapter
AI for Enhancing ICS/OT Cybersecurity
In today’s industrial environments, advanced technologies have become increasingly integrated, increasing vulnerabilities and risks related to cyber threats. This chapter explores the transformative role of ar...
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Chapter
CyberAI: A Comprehensive Summary of AI Variants, Explainable and Responsible AI for Cybersecurity
The integration of cybersecurity and artificial intelligence (AI), referred to as “CyberAI,” represents a dynamic and transformative landscape. This chapter outlines the diverse landscape of AI variants, as we...
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Chapter
Introduction to AI-Driven Cybersecurity and Threat Intelligence
With the convergence of artificial intelligence (AI) and cybersecurity, a new paradigm has emerged in how we defend against evolving digital threats. This book explores the dynamic landscape of AI-driven cyber...
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Chapter
Learning Technologies: Toward Machine Learning and Deep Learning for Cybersecurity
This chapter explores the transformative landscape of learning technologies, focusing specifically on machine learning and deep learning techniques used in cybersecurity. As digital threats become increasingly...
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Chapter
Generative AI and Large Language Modeling in Cybersecurity
Cybersecurity is encountering new challenges demanding innovative solutions due to the complexity and frequency of cyberattacks progressing. Artificial intelligence (AI), particularly generative AI, has emerge...
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Chapter and Conference Paper
Genetic Algorithm-Based Optimal Deep Neural Network for Detecting Network Intrusions
Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in network intrusion detection system (NIDS) technology, most implem...
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Chapter and Conference Paper
SATLabel: A Framework for Sentiment and Aspect Terms Based Automatic Topic Labelling
In this paper, we present a framework that automatically labels latent Dirichlet allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the ...
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Chapter and Conference Paper
Ransomware Family Classification with Ensemble Model Based on Behavior Analysis
Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signa...
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Chapter
Recency-Based Updating and Dynamic Management of Contextual Rules
In the previous chapter, we have presented an approach for discovering behavioral rules of individual mobile phone users based on multi-dimensional contexts (temporal, spatial, and social context) utilizing th...
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Chapter
Deep Learning for Contextual Mobile Data Analytics
Deep learning is considered as a part of the broader family of machine learning methods, which is based on artificial neural networks with representation learning. In the earlier chapters, we have presented me...
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Chapter
Application Scenarios and Basic Structure for Context-Aware Machine Learning Framework
Context-aware machine learning typically focuses on applications that learn from contextual data and develop their decision-making abilities over time. To make intelligent decisions in different context-aware ...
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Chapter
Context-Aware Rule-Based Expert System Modeling
An expert system is a computer system that simulates the decision-making abilities of a human expert in artificial intelligence (AI). Expert systems, rather than using traditional procedural code, are structur...
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Chapter
Context-Aware Machine Learning System: Applications and Challenging Issues
Context-awareness has recently received much attention in academia and industry for a variety of applications. Due to its intelligence in technologies and availability in various real-world applications, there...
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Chapter
Introduction to Context-Aware Machine Learning and Mobile Data Analytics
The concept of context-aware computing has grown in popularity in recent years, especially with the current evolution of smart mobile devices. Recent advancements in smartphones and their sensing capabilities ...
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Chapter
Discretization of Time-Series Behavioral Data and Rule Generation based on Temporal Context
In this chapter, we explore the discretization of the continuous time-series data to generate temporal segments according to the behavioral patterns of the users, which is used as the basis of generating rules...
