Published: Nov. 25, 2024
Language: Английский
Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)
Published: Feb. 25, 2025
Language: Английский
Citations
2
Applied Sciences, Journal Year: 2024, Volume and Issue: 14(10), P. 4109 - 4109
Published: May 12, 2024
This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, cloud layers, ensuring efficient scalable detection. Trustworthiness is established through incorporation distributed ledger technology (DLT), leveraging blockchain frameworks to enhance reliability transparency communication among IoT devices. Furthermore, adopts federated learning (FL) techniques address privacy concerns, allowing devices collaboratively learn from decentralized data sources while preserving individual privacy. Validation approach conducted using CICIoT2023 dataset, demonstrating its effectiveness in enhancing posture ecosystems. contributes advancement secure resilient infrastructures, addressing imperative need for lightweight, trust-managing, solutions face evolving cybersecurity challenges. According our experiments, model achieved an average accuracy 97.65%, precision recall 100%, F1-score 98.81% when detecting various attacks on systems with heterogeneous networks. compared traditional that uses centralized terms network latency memory consumption. shows can keep private environment.
Language: Английский
Citations
11
Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113351 - 113351
Published: March 1, 2025
Language: Английский
Citations
1
Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7435 - 7435
Published: Aug. 28, 2024
This paper aims to develop a groundbreaking approach fostering inclusive smart tourism destinations by integrating generative artificial intelligence (Gen AI) with natural language processing (NLP) and the Internet of Things (IoT) into an intelligent platform that supports decision making travel planning in destinations. The acquisition this new technology was conducted using Agile methodology through requirements analysis, system architecture analysis design, implementation, user evaluation. results revealed synergistic combination these technologies organized three tiers. provides information, including place names, images, descriptive text, audio option for users listen supporting tourists disabilities. Employing advanced AI algorithms alongside NLP, developed systems capable generating predictive analytics, personalized recommendations, conducting real-time, multilingual communication tourists. implemented evaluated Suphan Buri Ayutthaya, UNESCO World Heritage sites Thailand, 416 participating. showed satisfaction influenced (1) experience, (2) during-trip factors (attention, interest, usage), (3) emotion. relative Chi-square (χ2/df) 1.154 indicated model suitable. Comparative Fit Index (CFI) 0.990, Goodness-of-Fit (GFI) 0.965, based on research hypothesis consistent empirical data. contributions significant advancements field demonstrating integration Gen AI, IoT offering practical solutions theoretical insights enhance accessibility, personalization, environmental sustainability tourism.
Language: Английский
Citations
9
Informatics, Journal Year: 2024, Volume and Issue: 11(2), P. 32 - 32
Published: May 17, 2024
The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use open source code, lack software updates make it vulnerable to cyberattacks that can compromise access data services, thus making an attractive target for hackers. complexity has increased, posing a greater threat public private organizations. This study evaluated performance deep learning models classifying cybersecurity attacks IoT networks, using CICIoT2023 dataset. Three architectures based on DNN, LSTM, CNN were compared, highlighting their differences layers activation functions. results show architecture outperformed others accuracy computational efficiency, with rate 99.10% multiclass classification 99.40% binary classification. importance standardization proper hyperparameter selection is emphasized. These demonstrate CNN-based model emerges promising option detecting cyber threats environments, supporting relevance network security.
Language: Английский
Citations
7
Future Internet, Journal Year: 2024, Volume and Issue: 16(8), P. 284 - 284
Published: Aug. 8, 2024
This study uses deep learning methods to explore the Internet of Things (IoT) network intrusion detection method based on CIC-IoT-2023 dataset. dataset contains extensive data real-life IoT environments. Based this, this proposes an effective method. Apply seven models, including Transformer, analyze traffic characteristics and identify abnormal behavior potential intrusions through binary multivariate classifications. Compared with other papers, we not only use a Transformer model, but also consider model’s performance in multi-class classification. Although accuracy model used classification is lower than that DNN CNN + LSTM hybrid it achieves better results The our 0.74% higher papers TON-IOT. In classification, best-performing combination which reaches 99.40% accuracy. Its 3.8%, 0.65%, 0.29% 95.60%, 98.75%, 99.11% figures recorded using same dataset, respectively.
Language: Английский
Citations
5
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 117761 - 117786
Published: Jan. 1, 2024
In this study, we present an innovative network intrusion detection system (IDS) tailored for Internet of Things (IoT)-based smart home environments, offering a novel deployment scheme that addresses the full spectrum security challenges. Distinct from existing approaches, our comprehensive strategy not only proposes model but also incorporates IoT devices as potential vectors in cyber threat landscape, consideration often neglected previous research. Utilizing harmony search algorithm (HSA), refined extra trees classifier (ETC) by optimizing extensive array hyperparameters, achieving level sophistication and performance enhancement surpasses typical methodologies. Our was rigorously evaluated using robust real-time dataset, uniquely gathered 105 devices, reflecting more authentic complex scenario compared to simulated or limited datasets prevalent literature. commitment collaborative progress cybersecurity is demonstrated through public release source code. The underwent exhaustive testing 2-class, 8-class, 34-class configurations, showcasing superior accuracy (99.87%, 99.51%, 99.49%), precision (97.41%, 96.02%, 96.07%), recall (98.45%, 87.14%, 87.1%), f1-scores (97.92%, 90.65%, 90.61%) firmly establish its efficacy. Thiswork marks significant advancement security, providing scalable effective IDS solution adaptable intricate dynamics modern networks. findings pave way future endeavors realm defense, ensuring homes remain safe havens era digital vulnerability.
Language: Английский
Citations
4
Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 77 - 124
Published: July 26, 2024
Protecting virtual assets from cyber threats is essential as we live in a digitally advanced world. Providing responsible emphasis on proper network security and intrusion detection imperative. On the other hand, traditional strategies need supportive tool to adapt transforming threat space. New generative AI techniques like adversarial networks (GANs) variational autoencoders (VAEs) are mainstream technologies required meet gap. This chapter deals with how these models can enhance by inspecting traffic for anomalies malicious behaviors detected through unsupervised learning, which considers strange or emerging phenomena. survey features innovations fault detection, behavior control, deep packet inspection, classification, examples of real-world intrusions GAN-based systems. Furthermore, focuses challenges attacks that require development solid defense mechanisms, such networks. Ethics becomes following matter our list discussions, given privacy transparency accountability be observed when working security. Finally, authors examine trends determine cyber-attacks dealt comprehensively.
Language: Английский
Citations
4
International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 22, 2025
Language: Английский
Citations
0
Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 854 - 854
Published: Jan. 30, 2025
Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has heightened expectations, particularly Internet Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, native AI pose threats such eavesdropping, supply chain vulnerabilities, model poisoning, adversarial attacks. increased exposure sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs researchers to embed resilience core components 6G. While research is advancing, establishing comprehensive remains challenge. To address evolving threats, our proposes dynamic that emphasizes integration explainable (XAI) techniques SHAP LIME advanced machine learning models enhance decision-making transparency, improve complex environments, ensure effective detection mitigation emerging cyber threats. By refining accuracy ensuring alignment through recursive feature elimination consistent cross-validation, approach strengthens overall posture IoT–6G ecosystem, making it more resilient attacks other vulnerabilities.
Language: Английский
Citations
0