{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:56:31Z","timestamp":1774965391789,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SAUDI ARAMCO Cybersecurity Chair at Imam Abdulrahman Bin Faisal University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In today\u2019s digitalized era, the world wide web services are a vital aspect of each individual\u2019s daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users\u2019 personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), na\u00efve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.<\/jats:p>","DOI":"10.3390\/s23073467","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T03:01:14Z","timestamp":1679886074000},"page":"3467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Naya","family":"Nagy","sequence":"first","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Networks and Communication, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9255-6094","authenticated-orcid":false,"given":"Malak","family":"Aljabri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-6015","authenticated-orcid":false,"given":"Afrah","family":"Shaahid","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amnah Albin","family":"Ahmed","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1571-7050","authenticated-orcid":false,"given":"Fatima","family":"Alnasser","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda","family":"Almakramy","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6874-5745","authenticated-orcid":false,"given":"Manar","family":"Alhadab","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahad","family":"Alfaddagh","sequence":"additional","affiliation":[{"name":"SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8842","DOI":"10.1038\/s41598-022-10841-5","article-title":"An effective detection approach for phishing websites using URL and HTML features","volume":"12","author":"Aljofey","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_2","unstructured":"(2022, December 19). Number of Global Phishing Sites 2021|Statista. Available online: https:\/\/www.statista.com\/statistics\/266155\/number-of-phishing-domain-names-worldwide\/."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Aljabri, M., and Mirza, S. (2022, January 1\u20133). Phishing Attacks Detection using Machine Learning and Deep Learning Models. Proceedings of the 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CDMA54072.2022.00034"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121395","DOI":"10.1109\/ACCESS.2022.3222307","article-title":"Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions","volume":"10","author":"Aljabri","year":"2022","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s13278-022-01020-5","article-title":"Machine learning-based social media bot detection: A comprehensive literature review","volume":"13","author":"Aljabri","year":"2023","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alzahrani, R.A., and Aljabri, M. (2022). AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions. J. Sens. Actuator Networks, 12.","DOI":"10.3390\/jsan12010004"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aljabri, M., Aldossary, M., Al-Homeed, N., Alhetelah, B., Althubiany, M., Alotaibi, O., and Alsaqer, S. (2022, January 4\u20136). Testing and Exploiting Tools to Improve OWASP Top Ten Security Vulnerabilities Detection. Proceedings of the 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), Al-Khobar, Saudi Arabia.","DOI":"10.1109\/CICN56167.2022.10008360"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aljabri, M., Aljameel, S.S., Mohammad, R.M.A., Almotiri, S.H., Mirza, S., Anis, F.M., Aboulnour, M., Alomari, D.M., Alhamed, D.H., and Altamimi, H.S. (2021). Intelligent Techniques for Detecting Network Attacks: Review and Research Directions. Sensors, 21.","DOI":"10.3390\/s21217070"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aljabri, M., Alahmadi, A.A., Mohammad, R.M.A., Aboulnour, M., Alomari, D.M., and Almotiri, S.H. (2022). Classification of Firewall Log Data Using Multiclass Machine Learning Models. Electronics, 11.","DOI":"10.3390\/electronics11121851"},{"key":"ref_10","first-page":"45","article-title":"Phishing Website Detection using Machine Learning Algorithms","volume":"181","author":"Mahajan","year":"2018","journal-title":"Int. J. Comput. Appl."},{"key":"ref_11","first-page":"548","article-title":"Detection of Phishing Websites Using Machine Learning Algorithms","volume":"5","author":"Mausam","year":"2022","journal-title":"Int. J. Sci. Res. Eng. Dev."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dutta, A.K. (2021). Detecting phishing websites using machine learning technique. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0258361"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Salahdine, F., El Mrabet, Z., and Kaabouch, N. (2021, January 1\u20134). Phishing Attacks Detection A Machine Learning-Based Approach. Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON53757.2021.9666627"},{"key":"ref_14","first-page":"3880","article-title":"Detection of Phishing Websites Using Deep Learning Techniques. 2021, 12, 3880\u20133892","volume":"12","author":"Khana","year":"2021","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"ref_15","first-page":"0100702","article-title":"Phishing Websites Detection using Machine Learning","volume":"10","author":"Kulkarni","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","first-page":"0110945","article-title":"Machine Learning-Based Phishing Attack Detection","volume":"11","author":"Hossain","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_17","first-page":"2373","article-title":"Using Machine Learning to Find Phishing Websites","volume":"13","author":"Vennam","year":"2022","journal-title":"J. Algebraic Stat."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.procs.2020.02.251","article-title":"Comparison of Adaboost with MultiBoosting for Phishing Website Detection","volume":"168","author":"Subasi","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Subasi, A., Molah, E., Almkallawi, F., and Chaudhery, T.J. (2017, January 21\u201323). Intelligent phishing website detection using random forest classifier. Proceedings of the 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates.","DOI":"10.1109\/ICECTA.2017.8252051"},{"key":"ref_20","first-page":"1462","article-title":"Deep Learning Approach for Phishing Attacks","volume":"8","author":"CH","year":"2021","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"83425","DOI":"10.1109\/ACCESS.2020.2991403","article-title":"PhishHaven\u2014An Efficient Real-Time AI Phishing URLs Detection System","volume":"8","author":"Sameen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"213","article-title":"Phishing Attack Detection Using Deep Learning","volume":"21","author":"Alzahrani","year":"2021","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103545","DOI":"10.1016\/j.jnca.2022.103545","article-title":"HELPHED: Hybrid Ensemble Learning PHishing Email Detection","volume":"210","author":"Bountakas","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tajaddodianfar, F., Stokes, J.W., and Gururajan, A. (2020, January 4\u20138). Texception: A Character\/Word-Level Deep Learning Model for Phishing URL Detection. Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053670"},{"key":"ref_25","first-page":"553","article-title":"Parallel processing using big data and machine learning techniques for intrusion detection","volume":"9","author":"Boukhalfa","year":"2020","journal-title":"IAES Int. J. Artif. Intell. (IJ-AI)"},{"key":"ref_26","first-page":"773","article-title":"Intelligent Model for Classification of SPAM and HAM","volume":"8","author":"Rajput","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1155\/2022\/3241216","article-title":"An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models","volume":"2022","author":"Aljabri","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106304","DOI":"10.1016\/j.dib.2020.106304","article-title":"Malicious and Benign Webpages Dataset","volume":"32","author":"Singh","year":"2020","journal-title":"Data Brief"},{"key":"ref_29","unstructured":"Singh, A.K., and Goyal, N. (2016). Distributed Computing and Internet Technology, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2017). Data Mining Practical Machine Learning Tools and Techniques, Elsevier.","DOI":"10.1016\/B978-0-12-804291-5.00010-6"},{"key":"ref_31","first-page":"0287","article-title":"A Comparison Between Na\u00efve Bayes and Random Forest to Predict Breast Cancer","volume":"12","author":"Lemons","year":"2020","journal-title":"Int. J. Undergrad. Res. Creative Act."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"997","DOI":"10.11591\/ijece.v10i1.pp997-1005","article-title":"Detecting malicious URLs using binary classification through adaboost algorithm","volume":"10","author":"Khan","year":"2020","journal-title":"Int. J. Electr. Comput. Eng. (IJECE)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"42044","DOI":"10.1109\/ACCESS.2022.3168161","article-title":"Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System","volume":"10","author":"Sahu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","first-page":"35","article-title":"Automatic License Plate Recognition System for Vehicles Using a CNN","volume":"71","author":"Ranjithkumar","year":"2022","journal-title":"Comput. Mater. Contin."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:03:24Z","timestamp":1760123004000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,26]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073467"],"URL":"https:\/\/doi.org\/10.3390\/s23073467","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,26]]}}}