AI-Driven Cybersecurity and Threat Intelligence
Cyber Automation, Intelligent Decision-Making and Explainability
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Article
Open AccessLLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling
Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, busi...
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Article
Open AccessExploring post-COVID-19 health effects and features with advanced machine learning techniques
COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recover...
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Article
A hybrid deep learning framework for daily living human activity recognition with cluster-based video summarization
In assisted living facilities or nursing homes, residents’ movements or actions can be monitored using Human Activity Recognition (HAR), ensuring they receive proper care and attention. The significance of HAR...
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Article
Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques
In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on ...
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Article
COVID-19 Fake News Detection using Deep Learning Model
People may now receive and share information more quickly and easily than ever due to the widespread use of mobile networked devices. However, this can occasionally lead to the spread of false information. Suc...
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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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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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Book
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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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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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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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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 and Conference Paper
Data-Driven Intelligence Can Revolutionize Today’s Cybersecurity World: A Position Paper
As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today’s digital era. Traditional security measures tend to be insufficient to defend ...
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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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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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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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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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Article
Open AccessMachine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects
Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in t...
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Article
A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors
Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this b...
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Article
CovTiNet: Covid text identification network using attention-based positional embedding feature fusion
Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding a large volume of Covid-affiliated text on the World Wi...
