Unveiling the Dark Side of ChatGPT: Exploring Cyberattacks and Enhancing User Awareness

Information, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 27

Published: Jan. 2, 2024

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across variety of domains. However, this popularity has attracted the attention malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines tactics adversaries use leverage in Attackers pose regular users and manipulate ChatGPT’s vulnerability interactions, particularly context cyber assault. presents illustrative examples cyberattacks are possible with discusses realm ChatGPT-fueled cybersecurity threats. investigates extent user awareness relationship between A survey 253 participants was conducted, their responses were measured on three-point Likert scale. results provide comprehensive understanding how be used improve business processes identify areas for improvement. Over 80% agreed criminals purposes. finding underscores importance improving security novel model. Organizations must take steps protect computational infrastructure. analysis highlights opportunities streamlining processes, service quality, increasing efficiency. Finally, provides recommendations using secure manner, outlining ways mitigate potential strengthen defenses against adversaries.

Language: Английский

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When LLMs meet cybersecurity: a systematic literature review

Cybersecurity, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 5, 2025

Abstract The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies. Despite initial explorations into the application LLMs in there is a lack comprehensive overview this research area. This paper addresses gap by providing systematic literature review, covering analysis over 300 works, encompassing 25 more than 10 downstream scenarios. Our three key questions: construction cybersecurity-oriented LLMs, to cybersecurity tasks, challenges further study aims shed light on extensive potential enhancing practices serve as valuable resource applying field. We also maintain regularly update list practical guides at https://github.com/tmylla/Awesome-LLM4Cybersecurity .

Language: Английский

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Cybercrime Resilience in the Era of Advanced Technologies: Evidence from the Financial Sector of a Developing Country

Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 38 - 38

Published: Jan. 27, 2025

Technological advancements have helped all sectors to evolve. This advancement has widened the cyberspace and attack surface, which led a drastic increase in cyberattacks. Cybersecurity solutions also evolved. The is relatively slower developing countries. However, financial sector countries shown resistance paper investigates reasons for this resistance. Despite using legacy systems, banking Pakistan demonstrated research used qualitative approach. Semi-structured interviews were conducted with nine cybersecurity experts illustrate focused on sector, recognizing that industry particularly prone cyberattacks global scale. study utilised thematic analysis technique find factors. suggests opportunity cost of lower surface like are main losses. findings will encourage adoption advanced technologies such as artificial intelligence (AI) machine learning (ML) countries’ sectors.

Language: Английский

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AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms– A comprehensive review

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102922 - 102922

Published: Jan. 1, 2025

Language: Английский

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Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0318075 - e0318075

Published: Jan. 27, 2025

To enhance the intelligent classification of computer vulnerabilities and improve efficiency accuracy network security management, this study delves into application a comprehensive system that integrates Memristor Neural Network (MNN) an improved Temporal Convolutional (TCNN) in management. This not only focuses on precise vulnerability data but also emphasizes its core role strengthening management framework. Firstly, designs implements neural model based memristors. The MNN, by simulating memory effect biological neurons, effectively captures complex nonlinear relationships within data, thereby enhancing insight capabilities system. Subsequently, structural optimization parameter adjustments are made to TCNN model, incorporating residual connections attention mechanisms performance, making it more adaptable dynamically changing environment. Through preprocessing, feature extraction, training, conducts experimental validation public dataset. results indicate that: MNN demonstrates excellent performance across evaluation metrics such as Accuracy (ACC), Precision (P), Recall (R), F1 Score, achieving ACC 89.5%, P 90.2%, R 88.7%, 89.4%. shows even outstanding aforementioned metrics. After adjustments, model’s increases 93.8%, significantly higher than model. value improves, reaching 91.5%, indicating enhanced capability reducing false positives improving identification accuracy. integrated system, leveraging strengths both models, achieves 95.2%. improvement system’s superior accurately classifying proves synergistic models addressing environments. proposed enhances vulnerabilities, providing robust technical support for exhibits stability handling datasets, highly valuable practical applications research.

Language: Английский

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Evaluating Antivirus Effectiveness Against Malware in Ascending Order for Increasing Blockchain Endpoint Protection

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 150 - 166

Published: Jan. 29, 2024

Blockchain represents a new promising technology with huge economic impact resulting from its uses in various fields such as digital currency and banking; malware serious threat to users, there are many differences the effectiveness of antivirus software used deal problem malware. This chapter has developed coefficient for measuring software. evaluates by conducting tests on group protection programs using folder containing an amount data. These applied combat viruses contained this folder. The study revealed that is follows: AVG scored 0%, Advanced System Protector 20%, Avast 60%, Malwarebytes 80%, respectively.

Language: Английский

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Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries

Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(7), P. 1666 - 1679

Published: July 25, 2024

This paper explores frameworks for effective data governance, emphasizing the importance of robust policies, processes, roles, and metrics. It outlines best practices ensuring high quality, privacy, security while highlighting stakeholder engagement role technology. The also discusses implementation challenges, including organizational, technical, regulatory, cultural obstacles. presents tailored strategies various industries such as financial services, healthcare, retail, manufacturing, public sector. Future directions research include integration AI machine learning, evolving privacy regulations, challenges posed by big IoT. Effective governance is crucial managing risks, compliance, unlocking full potential assets across industries. Keywords: Data Governance, Quality Management, Privacy, Regulatory Compliance.

Language: Английский

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Challenges and Solutions in Network Security for Serverless Computing

International Journal of Current Science Research and Review, Journal Year: 2024, Volume and Issue: 07(01)

Published: Jan. 11, 2024

This research study explores the challenges and solutions related to serverless computing so that computer systems connected network can be protected. Serverless defined as a method of managing services without need have fixed servers. The qualitative is used by this study, which does not include any numerical data involves examination non-number security identified in detail. In literature review, past studies from 2019 2023 are reviewed identify gaps foundation for investigating security. review based on thematic analysis, all organized into meaningful themes. findings like privacy, insecure dependencies limited control. strategies overcome these encryption, strong monitoring other relevant strategies. also suggests use blockchain technology Artificial Intelligence. short, provides insights improve guides future researchers innovate creative developing challenges.

Language: Английский

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Deep learning hybridization for improved malware detection in smart Internet of Things

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 3, 2024

Abstract The rapid expansion of AI-enabled Internet Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. dynamic increase in big data generated by IoT poses a persistent problem, particularly making decisions based on the continuously growing data. To address this challenge environment, study introduces specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for scenarios. In evaluation, novel framework with distinct modules is employed thorough analysis 8 datasets, each representing different type malware. BEFSONet optimized using Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes malware Thorough exploratory analyses comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under ROC Curve(AUC-ROC), 95.89 Cohen’s Kappa. This research positions as robust defense mechanism era security, offering an effective solution evolving challenges decision-making environments.

Language: Английский

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A Deep Learning-Based Approach for the Detection of Various Internet of Things Intrusion Attacks Through Optical Networks

Photonics, Journal Year: 2025, Volume and Issue: 12(1), P. 35 - 35

Published: Jan. 3, 2025

The widespread use of the Internet Things (IoT) has led to significant breakthroughs in various fields but also exposed critical vulnerabilities evolving cybersecurity threats. Current Intrusion Detection Systems (IDSs) often fail provide real-time detection, scalability, and interpretability, particularly high-speed optical network environments. This research introduces XIoT, which is a novel explainable IoT attack detection model designed address these challenges. Leveraging advanced deep learning methods, specifically Convolutional Neural Networks (CNNs), XIoT analyzes spectrogram images transformed from traffic data detect subtle complex patterns. Unlike traditional approaches, emphasizes interpretability by integrating AI mechanisms, enabling analysts understand trust its predictions. By offering actionable insights into factors driving decision making, supports informed responses cyber Furthermore, model’s architecture leverages high-speed, low-latency characteristics networks, ensuring efficient processing large-scale streams supporting diverse ecosystems. Comprehensive experiments on benchmark datasets, including KDD CUP99, UNSW NB15, Bot-IoT, demonstrate XIoT’s exceptional accuracy rates 99.34%, 99.61%, 99.21%, respectively, significantly surpassing existing methods both interpretability. These results highlight capability enhance security addressing real-world challenges, robust, scalable, interpretable protection for networks against sophisticated

Language: Английский

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