Enhancing Agricultural Cybersecurity

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 307 - 338

Published: April 8, 2025

The rapid digital transformation of agriculture through smart farming technologies has introduced new cybersecurity challenges that threaten the integrity, confidentiality, and availability critical agricultural data systems. As precision agriculture, Internet Things (IoT)-enabled sensors, automated decision-making become integral to modern farming, risks associated with cyber threats—such as breaches, ransomware attacks, supply chain vulnerabilities—continue escalate. Unlike traditional security measures, AI-driven solutions, including deep learning Large Language Models (LLMs), offer real-time threat detection, adaptive defense mechanisms, enhanced risk assessment capabilities. This chapter explores application these in securing networks, from intrusion detection incident response. It also presents case studies solutions implemented environments.

Language: Английский

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Secure Authentication and Identity Management With AI

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 271 - 306

Published: April 8, 2025

Secure authentication and identity management are critical components of modern cybersecurity, ensuring that only authorized users gain access to sensitive systems data. Traditional methods, such as passwords multi-factor authentication, face increasing challenges due sophisticated cyber threats, credential theft, user experience limitations. Recent advancements in artificial intelligence (AI), particularly deep learning large language models (LLMs), have revolutionized mechanisms by enhancing security, accuracy, adaptability. AI-driven leverage biometric recognition, behavioral analysis, anomaly detection improve verification fraud prevention. Additionally, federated decentralized frameworks provide robust solutions for privacy-preserving authentication. This chapter explores the integration AI secure management, discussing its benefits, challenges, future research directions.

Language: Английский

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AI Automated Incident Response and Threat Mitigation Using AI

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 201 - 236

Published: April 8, 2025

The rapid evolution of cyber threats has necessitated the development advanced techniques for incident response and threat mitigation. Artificial Intelligence (AI) emerged as a transformative force in cybersecurity, particularly automating detection, response, mitigation processes. This chapter explores role AI, including Deep Learning (DL) Large Language Models (LLMs), revolutionizing strategies. By leveraging organizations can achieve faster, more accurate adaptive mechanisms, efficient strategies, significantly improving their overall security posture. examines key AI technologies, applications challenges faced, future potential AI-driven operations.

Language: Английский

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Malware Analysis and Classification Using Deep Learning

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 165 - 200

Published: April 8, 2025

Malware analysis and classification have become critical components of modern cybersecurity strategies, given the increasing sophistication cyber threats. With rapid advancement machine learning techniques, particularly deep learning, ability to detect classify malware has improved significantly. This chapter explores role in automating detection classification, focusing on use neural networks, feature extraction, pattern recognition. We discuss various architectures, such as Convolutional Neural Networks (CNNs) Recurrent (RNNs), that been successfully applied analysis. Additionally, examines challenges limitations using models, including data imbalance, overfitting, model interpretability. The integration large datasets, along with potential language models (LLMs), is also explored for enhanced accuracy.

Language: Английский

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Phishing and Social Engineering Attack Prevention With LLMs

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 133 - 164

Published: April 8, 2025

Phishing and social engineering attacks have become increasingly sophisticated, leveraging advanced psychological manipulation deceptive tactics to compromise individuals organizations. Traditional cybersecurity measures often fail address the dynamic evolving nature of these threats. Large Language Models (LLMs) present a promising solution by enabling real-time threat detection, automated phishing email classification, user education through natural language processing capabilities. This chapter explores how LLMs can enhance attack prevention, discussing their role in filtering, anomaly chatbot-based awareness training, behavioral analysis. Additionally, we challenges such as adversarial LLMs, ethical considerations, model biases. By integrating deep learning with frameworks, organizations develop more resilient adaptive defense mechanisms against human-targeted cyber

Language: Английский

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Threat Detection and Anomaly Identification Using Deep Learning

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 65 - 96

Published: April 8, 2025

Cyber threats are increasingly complex, requiring advanced detection and mitigation strategies. Deep learning (DL) offers powerful solutions for threat anomaly identification, thanks to its ability process large data volumes uncover subtle indicators of cyber threats. This chapter discusses the integration various DL techniques—CNNs, RNNs, autoencoders, GANs—for detecting malicious activities anomalies across diverse cybersecurity contexts. By examining both supervised unsupervised approaches, it highlights strengths limitations in tackling such as zero-day attacks, insider threats, APTs. Real-world applications, case studies, role explainable AI (XAI) enhancing transparency trust also explored. Finally, challenges like adversarial quality, computational constraints addressed, along with future directions improving robustness efficiency DL-driven systems.

Language: Английский

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Foundations of Deep Learning and Large Language Models in Cybersecurity

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 36

Published: April 8, 2025

The integration of deep learning (DL) and large language models (LLMs) has significantly advanced the field cybersecurity, offering innovative approaches to threat detection, anomaly identification, secure communication. Deep techniques, such as neural networks reinforcement learning, have demonstrated robust capabilities in detecting previously unknown threats by patterns from vast amounts cybersecurity data. Similarly, LLMs, particularly transformers, revolutionized natural processing tasks, enabling effective vulnerability analysis, malware classification, phishing detection. This chapter explores foundational concepts highlighting their applications challenges within landscape. Additionally, it discusses synergy between these technologies, focusing on how they complement traditional measures drive evolution intelligent defense mechanisms.

Language: Английский

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AI-Powered Contract Security

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 37 - 64

Published: April 8, 2025

The rapid advancement of artificial intelligence (AI) has transformed contract security, offering innovative solutions for managing expiry, ensuring compliance, and mitigating risks. Traditional management systems often struggle with scalability, accuracy, adaptability, leading to inefficiencies potential legal vulnerabilities. This chapter explores how deep learning large language models (LLMs) enhance security by automating review, expiration tracking, regulatory compliance assessment. By leveraging natural processing (NLP) predictive analytics, AI-driven can proactively identify risks, flag anomalies, ensure adherence contractual obligations. Furthermore, this discusses key challenges such as bias in AI models, data privacy concerns, the need robust frameworks. Through case studies experimental results, we demonstrate AI-powered improves efficiency, reduces human errors, enhances organizational risk management.

Language: Английский

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