Human Evaluation in Large Language Model Testing DOI

H. M Dharmendra,

G. Raghunandan,

A. N. Sindhu

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 553 - 574

Published: Sept. 20, 2024

LLMs excel in language tasks, but testing them effectively is tricky. Automated metrics help, human evaluation crucial for aspects like clarity, relevance, and ethics. This chapter explores methods challenges of LLM testing, including factors fairness user experience. The authors discuss a sample method highlight ongoing efforts robust to ensure responsible development. Finally, they explore the use cybersecurity, showcasing their potential challenges.

Language: Английский

From Vulnerability to Defense: The Role of Large Language Models in Enhancing Cybersecurity DOI Creative Commons

Wafaa Kasri,

Yassine Himeur, Hamzah Ali Alkhazaleh

et al.

Computation, Journal Year: 2025, Volume and Issue: 13(2), P. 30 - 30

Published: Jan. 29, 2025

The escalating complexity of cyber threats, coupled with the rapid evolution digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores transformative role LLMs in addressing critical cybersecurity. With landscapes and increasing sophistication security mechanisms often fall short detecting, mitigating, responding complex risks. LLMs, such as GPT, BERT, PaLM, demonstrate unparalleled capabilities natural language processing, enabling them parse vast datasets, identify vulnerabilities, automate threat detection. Their applications extend phishing detection, malware analysis, drafting policies, even incident response. By leveraging advanced features like context awareness real-time adaptability, enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying is not without challenges, including issues interpretability, scalability, ethical concerns, susceptibility adversarial attacks. critically examines foundational elements, real-world applications, limitations highlighting key advancements their integration into frameworks. Through detailed analysis case studies, this paper identifies emerging trends proposes future research directions, improving robustness, privacy automating management. study concludes by emphasizing potential redefine cybersecurity, driving innovation enhancing ecosystems.

Language: Английский

Citations

9
Enhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey DOI Creative Commons
Martin Andreoni Lopez, Willian T. Lunardi,

George Lawton

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 109470 - 109493

Published: Jan. 1, 2024

This survey explores the transformative role of Generative Artificial Intelligence (GenAI) in enhancing trustworthiness, reliability, and security autonomous systems such as Unmanned Aerial Vehicles (UAVs), self-driving cars, robotic arms. As edge robots become increasingly integrated into daily life critical infrastructure, complexity connectivity these introduce formidable challenges ensuring security, resilience, safety. GenAI advances from mere data interpretation to autonomously generating new data, proving complex, context-aware environments like robotics. Our delves impact technologies—including Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based models, Large Language Models (LLMs)—on cybersecurity, decision-making, development resilient architectures. We categorize existing research highlight how technologies address operational innovate predictive maintenance, anomaly detection, adaptive threat response. comprehensive analysis distinguishes this work reviews by mapping out applications, challenges, technological advancements their on creating secure frameworks for systems. discuss significant future directions integrating within evolving landscape cyber-physical threats, underscoring potential make more adaptive, secure, efficient.

Language: Английский

Citations

12
Enhancing road traffic flow in sustainable cities through transformer models: Advancements and challenges DOI

Shahriar Soudeep,

Most. Lailun Nahar Aurthy,

Jamin Rahman Jim

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 116, P. 105882 - 105882

Published: Oct. 10, 2024

Language: Английский

Citations

4
ATIRS: Towards Adaptive Threat Analysis with Intelligent Log Summarization and Response Recommendation DOI Open Access

Daekyeong Park,

Byeongjun Min,

Sanghun Lim

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1289 - 1289

Published: March 25, 2025

Modern maritime operations rely on diverse network components, increasing cybersecurity risks. While security solutions like Suricata generate extensive alert logs, ships often operate without dedicated personnel, requiring general crew members to review and respond alerts. This challenge is exacerbated when vessels are at sea, delaying threat mitigation due limited external support. We propose an Adaptive Threat Intelligence Response Recommendation System (ATIRS), a small language model (SLM)-based framework that automates log summarization response recommendations address this. The ATIRS processes real-world data converts unstructured alerts into structured summaries, allowing the recommendation contextually relevant actionable countermeasures. It then suggests appropriate follow-up actions, such as IP blocking or account locking, ensuring timely effective response. Additionally, employs adaptive learning, continuously refining its based user feedback emerging threats. Experimental results from shipboard demonstrate significantly reduces Mean Time Respond (MTTR) while alleviating burden members, for faster more efficient mitigation, even in resource-constrained environments.

Language: Английский

Citations

0
Optimising AI models for intelligence extraction in the life cycle of Cybersecurity Threat Landscape generation DOI Creative Commons
Alexandros Zacharis,

Razvan Gavrila,

Constantinos Patsakis

et al.

Journal of Information Security and Applications, Journal Year: 2025, Volume and Issue: 90, P. 104037 - 104037

Published: April 3, 2025

Language: Английский

Citations

0
LLM-Powered Security Solutions in Healthcare, Government, and Industrial Cybersecurity DOI

S. Karkuzhali,

S. Senthilkumar

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 132

Published: April 8, 2025

Large Language Models (LLMs) are revolutionizing cybersecurity in healthcare, government, and industrial sectors by enabling real-time threat detection, anomaly identification, compliance automation. This chapter explores the transformative role of LLMs mitigating cyber risks while addressing challenges such as adversarial attacks, data privacy, bias. It examines real-world applications, highlighting their effectiveness securing critical infrastructures. Additionally, ethical, legal, regulatory considerations discussed to ensure responsible AI deployment. The provides strategic recommendations for integrating LLM-powered security solutions risks, enhancing resilience, improving automated incident response. By leveraging effectively, organizations can strengthen frameworks safeguard sensitive against evolving threats an increasingly digital landscape.

Language: Английский

Citations

0
Advanced Computational Methods for News Classification: A Study in Neural Networks and CNN integrated with GPT DOI Creative Commons
Fahim Sufi

Journal of Economy and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

2
Chat-GPT Based Learning Platform for Creation of Different Attack Model Signatures and Development of Defense Algorithm for Cyberattack Detection DOI
Thulasi M. Santhi,

K. Srinivasan

IEEE Transactions on Learning Technologies, Journal Year: 2024, Volume and Issue: 17, P. 1869 - 1882

Published: Jan. 1, 2024

Language: Английский

Citations

1
Human Evaluation in Large Language Model Testing DOI

H. M Dharmendra,

G. Raghunandan,

A. N. Sindhu

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 553 - 574

Published: Sept. 20, 2024

LLMs excel in language tasks, but testing them effectively is tricky. Automated metrics help, human evaluation crucial for aspects like clarity, relevance, and ethics. This chapter explores methods challenges of LLM testing, including factors fairness user experience. The authors discuss a sample method highlight ongoing efforts robust to ensure responsible development. Finally, they explore the use cybersecurity, showcasing their potential challenges.

Language: Английский

Citations

1