{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:31:16Z","timestamp":1790814676260,"version":"4.1.0"},"reference-count":38,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004728","name":"Vellore Institute of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004728","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>As cloud computing becomes increasingly prevalent, the detection and prevention of phishing URL attacks are essential, particularly in the Internet of Vehicles (IoV) environment, to maintain service reliability. In such a scenario, an attacker could send misleading phishing links, potentially compromising the system\u2019s functionality or, at worst, leading to a complete shutdown. To address these emerging threats, this study introduces a novel Dynamic Arithmetic Optimization Algorithm with Deep Learning-Driven Phishing URL Classification (DAOA-DLPC) model for cloud-enabled IoV infrastructure. The candidate\u2019s research utilizes character-level embeddings instead of word embeddings, as the former can capture intricate URL patterns more effectively. These embeddings are integrated with a deep learning model, the Multi-Head Attention and Bidirectional Gated Recurrent Units (MHA-BiGRU). To improve precision, hyperparameter tuning has been done using DAOA. The proposed method offers a feasible solution for identifying the phishing URLs, and the method achieves computational efficiency through the attention mechanism and dynamic hyperparameter optimization. The need for this work comes from the observation that the traditional machine learning approaches are not effective in dynamic environments like phishing threat landscapes in a dynamic environment such as the one of phishing threats. The presented DLPC approach is capable of learning new forms of phishing attacks in real time and reduce false positives. The experimental results show that the proposed DAOA-DLPC model outperforms the other models with an accuracy of 98.85%, recall of 98.49%, and F1-score of 98.38% and can effectively detect safe and phishing URLs in dynamic environments. These results imply that the proposed model is useful in distinguishing between safe and unsafe URLs than the conventional models.<\/jats:p>","DOI":"10.7717\/peerj-cs.2640","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T08:07:13Z","timestamp":1747642033000},"page":"e2640","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments"],"prefix":"10.7717","volume":"11","author":[{"given":"Vishnukumar","family":"Ravula","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3088-6001","authenticated-orcid":true,"given":"Mangayarkarasi","family":"Ramaiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"10.7717\/peerj-cs.2640\/ref-1","article-title":"A deep learning-based CAE approach for simulating 3D vehicle wheels under real-world conditions","author":"Akande","year":"2022"},{"issue":"4","key":"10.7717\/peerj-cs.2640\/ref-2","doi-asserted-by":"publisher","first-page":"109407","DOI":"10.1016\/j.comnet.2022.109407","article-title":"Phishnot: a cloud-based machine-learning approach to phishing URL detection","volume":"218","author":"Alani","year":"2022","journal-title":"Computer Networks"},{"key":"10.7717\/peerj-cs.2640\/ref-3","doi-asserted-by":"publisher","first-page":"103114","DOI":"10.1016\/j.seta.2023.103114","article-title":"Metaheuristics with deep learning driven phishing detection for sustainable and secure environment","volume":"56","author":"Alohali","year":"2023","journal-title":"Sustainable Energy Technologies and Assessments"},{"issue":"9","key":"10.7717\/peerj-cs.2640\/ref-4","doi-asserted-by":"publisher","first-page":"3373","DOI":"10.3390\/s22093373","article-title":"Cyber threat intelligence-based malicious url detection model using ensemble learning","volume":"22","author":"Alsaedi","year":"2022","journal-title":"Sensors"},{"issue":"11","key":"10.7717\/peerj-cs.2640\/ref-5","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.3390\/electronics10111285","article-title":"An optimized stacking ensemble model for phishing websites detection","volume":"10","author":"Al-Sarem","year":"2021","journal-title":"Electronics"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-6","doi-asserted-by":"publisher","first-page":"107868","DOI":"10.1016\/j.compeleceng.2022.107868","article-title":"Character-level word encoding deep learning model for combating cyber threats in phishing URL detection","volume":"100","author":"Alshehri","year":"2022","journal-title":"Computers and Electrical Engineering"},{"issue":"1","key":"10.7717\/peerj-cs.2640\/ref-7","doi-asserted-by":"publisher","first-page":"232","DOI":"10.3390\/electronics12010232","article-title":"A deep learning-based phishing detection system using CNN, LSTM, and lSTM-CNN","volume":"12","author":"Alshingiti","year":"2023","journal-title":"Electronics"},{"key":"10.7717\/peerj-cs.2640\/ref-8","doi-asserted-by":"publisher","first-page":"8373","DOI":"10.1109\/ACCESS.2024.3351946","article-title":"Enhancing phishing detection: a novel hybrid ndeep learning framework for cybercrime forensics","volume":"12","author":"Alsubaei","year":"2024","journal-title":"IEEE Access"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-9","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3390\/sym16020248","article-title":"Antiphishstack: LSTM-based stacked generalization model for optimized phishing URL detection","volume":"16","author":"Aslam","year":"2024","journal-title":"Symmetry"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-10","doi-asserted-by":"publisher","first-page":"103553","DOI":"10.1016\/j.jisa.2023.103553","article-title":"A high-accuracy phishing website detection method based on machine learning","volume":"77","author":"Bahaghighat","year":"2023","journal-title":"Journal of Information Security and Applications"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-11","doi-asserted-by":"publisher","first-page":"114","DOI":"10.47852\/bonviewAIA3202441","article-title":"Evaluation of deep learning CNN model for recognition of devanagari digit","volume":"1","author":"Bhosle","year":"2023","journal-title":"Artificial Intelligence and Applications"},{"key":"10.7717\/peerj-cs.2640\/ref-12","doi-asserted-by":"publisher","first-page":"30754","DOI":"10.1109\/JIOT.2024.3412925","article-title":"Semi-supervised adaptive pseudo-label feature learning for hyperspectral image classification in Internet of Things","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE Internet of Things Journal"},{"issue":"9","key":"10.7717\/peerj-cs.2640\/ref-13","doi-asserted-by":"publisher","first-page":"12517","DOI":"10.1109\/TITS.2024.3373510","article-title":"MOQEA\/D: multi-objective QEA with decomposition mechanism and excellent global search and its application","volume":"25","author":"Deng","year":"2024","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"3","key":"10.7717\/peerj-cs.2640\/ref-14","doi-asserted-by":"publisher","first-page":"2701","DOI":"10.32604\/csse.2023.028984","article-title":"Optimal deep belief network enabled cybersecurity phishing email classification","volume":"44","author":"Dutta","year":"2023","journal-title":"Computer Systems Science & Engineering"},{"issue":"1","key":"10.7717\/peerj-cs.2640\/ref-15","doi-asserted-by":"publisher","first-page":"11488","DOI":"10.1038\/s41598-023-37552-9","article-title":"Life-long phishing attack detection using continual learning","volume":"13","author":"Ejaz","year":"2023","journal-title":"Scientific Reports"},{"issue":"10s","key":"10.7717\/peerj-cs.2640\/ref-16","first-page":"1208","article-title":"Advancement of phishing attack detection using machine learning","volume":"20","author":"Elumalai","year":"2024","journal-title":"Journal of Electrical Systems"},{"key":"10.7717\/peerj-cs.2640\/ref-17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2402.17249","article-title":"Deep learning-based speech and vision synthesis to improve phishing attack detection through a multi-layer adaptive framework","author":"Ige","year":"2024"},{"key":"10.7717\/peerj-cs.2640\/ref-18","doi-asserted-by":"publisher","first-page":"8033","DOI":"10.1002\/cpe.8033","article-title":"Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence","volume":"36","author":"Jafari","year":"2024","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"10.7717\/peerj-cs.2640\/ref-19","first-page":"130","article-title":"Detecting cloud-based phishing attacks by combining deep learning models","author":"Jha","year":"2022"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-20","doi-asserted-by":"publisher","first-page":"2350002","DOI":"10.1142\/S1469026823500025","article-title":"Malicious software detection based on URL-API intensity feature selection using deep spectral neural classification for improving host security","volume":"22","author":"Lavanya","year":"2023","journal-title":"International Journal of Computational Intelligence and Applications"},{"issue":"2129","key":"10.7717\/peerj-cs.2640\/ref-21","doi-asserted-by":"publisher","first-page":"117611","DOI":"10.1016\/j.oceaneng.2024.117611","article-title":"Investigation of ice wedge bearing capacity based on an anisotropic beam analogy","volume":"302","author":"Li","year":"2024","journal-title":"Ocean Engineering"},{"issue":"12","key":"10.7717\/peerj-cs.2640\/ref-22","doi-asserted-by":"publisher","first-page":"799926","DOI":"10.3389\/fpsyg.2021.799926","article-title":"Position-enhanced multi-head self-attention based bidirectional gated recurrent unit for aspect-level sentiment classification","volume":"385","author":"Li","year":"2022","journal-title":"Frontiers in Psychology"},{"issue":"11","key":"10.7717\/peerj-cs.2640\/ref-23","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1177\/14759217241254121","article-title":"Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data","volume":"55","author":"Li","year":"2024","journal-title":"Structural Health Monitoring"},{"key":"10.7717\/peerj-cs.2640\/ref-24","doi-asserted-by":"publisher","first-page":"920835","DOI":"10.3389\/fenrg.2022.920835","article-title":"Research on ultra-short-term prediction model of wind power based on attention mechanism and CNN-BiGRU combined","volume":"10","author":"Meng","year":"2022","journal-title":"Frontiers in Energy Research"},{"issue":"7","key":"10.7717\/peerj-cs.2640\/ref-25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-021-06401-z","article-title":"A hybrid DNN-LSTM model for detecting phishing URLs","volume":"35","author":"Ozcan","year":"2023","journal-title":"Neural Computing and Applications"},{"issue":"7","key":"10.7717\/peerj-cs.2640\/ref-26","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.comcom.2022.12.027","article-title":"deepBF: malicious URL detection using learned bloom filter and evolutionary deep learning","volume":"200","author":"Patgiri","year":"2023","journal-title":"Computer Communications"},{"issue":"3","key":"10.7717\/peerj-cs.2640\/ref-27","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1049\/ise2.12106","article-title":"An enhanced deep learning-based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders","volume":"17","author":"Prabakaran","year":"2023","journal-title":"IET Information Security"},{"issue":"4","key":"10.7717\/peerj-cs.2640\/ref-28","doi-asserted-by":"publisher","first-page":"103545","DOI":"10.1016\/j.cose.2023.103545","article-title":"Phiusiil: a diverse security profile empowered phishing URL detection framework based on similarity index and incremental learning","volume":"136","author":"Prasad","year":"2024","journal-title":"Computers & Security"},{"issue":"2","key":"10.7717\/peerj-cs.2640\/ref-29","doi-asserted-by":"publisher","first-page":"119","DOI":"10.47852\/bonviewAIA2202293","article-title":"Region-based convolutional neural network for segmenting text in epigraphical images","volume":"1","author":"Preethi","year":"2023","journal-title":"Artificial Intelligence and Applications"},{"issue":"6","key":"10.7717\/peerj-cs.2640\/ref-30","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.18576\/amis\/180624","article-title":"Enhanced phishing detection: an ensemble stacking model with DT-RFECV and SMOTE","volume":"18","author":"Ramaiah","year":"2024","journal-title":"Applied Mathematics"},{"issue":"4","key":"10.7717\/peerj-cs.2640\/ref-31","doi-asserted-by":"publisher","first-page":"e4221","DOI":"10.1002\/ett.4221","article-title":"An intrusion detection system using optimized deep neural network architecture","volume":"32","author":"Ramaiah","year":"2021","journal-title":"Transactions on Emerging Telecommunications Technologies"},{"key":"10.7717\/peerj-cs.2640\/ref-32","doi-asserted-by":"publisher","first-page":"8052","DOI":"10.1109\/ACCESS.2024.3352629","article-title":"Dephides: deep learning based phishing detection system","volume":"12","author":"Sahingoz","year":"2024","journal-title":"IEEE Access"},{"issue":"4","key":"10.7717\/peerj-cs.2640\/ref-33","doi-asserted-by":"publisher","first-page":"102643","DOI":"10.1016\/j.asej.2024.102643","article-title":"Detecting phishing websites through improving convolutional neural networks with Self-Attention mechanism","volume":"15","author":"Said","year":"2024","journal-title":"Ain Shams Engineering Journal"},{"issue":"4","key":"10.7717\/peerj-cs.2640\/ref-34","doi-asserted-by":"publisher","first-page":"100036","DOI":"10.1016\/j.csa.2024.100036","article-title":"An application for predicting phishing attacks: a case of implementing a support vector machine learning model","volume":"2","author":"Shombot","year":"2024","journal-title":"Cyber Security and Applications"},{"issue":"14s","key":"10.7717\/peerj-cs.2640\/ref-35","first-page":"213","article-title":"Deep learning empowered phishing URL detection: an exhaustive approach","volume":"12","author":"Subashini","year":"2024","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"issue":"1","key":"10.7717\/peerj-cs.2640\/ref-36","doi-asserted-by":"publisher","first-page":"113","DOI":"10.12928\/telkomnika.v22i1.25516","article-title":"Deep learning based phishing website detection","volume":"22","author":"Subhashini","year":"2024","journal-title":"Telkomnika (Telecommunication Computing Electronics and Control)"},{"key":"10.7717\/peerj-cs.2640\/ref-37","article-title":"Enhancing phishing detection, leveraging deep learning techniques","volume-title":"Journal of Computing & Biomedical Informatics, Special Issue on Artificial Intelligence and Emerging Technologies (SICAIET)","author":"Ullah","year":"2024"},{"issue":"20","key":"10.7717\/peerj-cs.2640\/ref-38","doi-asserted-by":"publisher","first-page":"32804","DOI":"10.1109\/JIOT.2024.3409823","article-title":"Defect detection using shuffle Net-CA-SSD lightweight network for turbine blades in IoT","volume":"11","author":"Zhao","year":"2024","journal-title":"IEEE Internet of Things Journal"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-2640.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2640.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2640.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-2640.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T08:07:31Z","timestamp":1747642051000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-2640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,19]]},"references-count":38,"alternative-id":["10.7717\/peerj-cs.2640"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.2640","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,19]]},"article-number":"e2640"}}