{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T12:09:27Z","timestamp":1786450167782,"version":"3.56.0"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Excellence Initiative\u2014Research University (IDUB) at AGH University of Krakow"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers.<\/jats:p>","DOI":"10.3390\/jtaer20030214","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T14:51:46Z","timestamp":1755183106000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["AI-Driven Anomaly Detection in E-Commerce Services: A Deep Learning and NLP Approach to the Isolation Forest Algorithm Trees"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6851-1518","authenticated-orcid":false,"given":"Pascal Muam","family":"Mah","sequence":"first","affiliation":[{"name":"Department of Information and Communication Technology, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-7525","authenticated-orcid":false,"given":"Iwona","family":"Skalna","sequence":"additional","affiliation":[{"name":"Department of Business Informatics and Management Engineering, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2212-7806","authenticated-orcid":false,"given":"Tomasz","family":"Pelech-Pilichowski","sequence":"additional","affiliation":[{"name":"Department of Applied Computer Science, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s10257-015-0291-6","article-title":"A pluggable service platform architecture for e-commerce","volume":"14","author":"Aulkemeier","year":"2016","journal-title":"Inf. Syst. e-Bus. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102287","DOI":"10.1016\/j.jretconser.2020.102287","article-title":"Managing the effectiveness of e-commerce platforms in a pandemic","volume":"58","author":"Tran","year":"2021","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101240","DOI":"10.1016\/j.elerap.2023.101240","article-title":"From e-commerce to m-commerce: An analysis of the user\u2019s experience with different access platforms","volume":"58","author":"Lucas","year":"2023","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_4","first-page":"96","article-title":"National AI strategies","volume":"27","author":"Mah","year":"2024","journal-title":"Eur. Res. Stud. J."},{"key":"ref_5","first-page":"1","article-title":"Machine Learning and Big Data Approaches to Enhancing E-commerce Anomaly Detection and Proactive Defense Strategies in Cybersecurity","volume":"7","author":"Karunaratne","year":"2023","journal-title":"J. Adv. Cybersecur. Sci. Threat Intell. Countermeas."},{"key":"ref_6","first-page":"67","article-title":"Artificial intelligence, natural language processing, and machine learning to enhance e-service quality on e-commerce platforms","volume":"4","author":"Rane","year":"2024","journal-title":"Intell. Mach. Learn."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Girimurugan, B., Kumaresan, V., Nair, S.G., Kuchi, M., and Kholifah, N. (2024). AI and Machine Learning in E-Commerce Security: Emerging Trends and Practices. Strategies for E-Commerce Data Security: Cloud, Blockchain, AI, and Machine Learning, IGI Global.","DOI":"10.4018\/979-8-3693-6557-1.ch002"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mudgal, A. (2025). Leveraging AI and ML for Proactive Threat Detection for E-Commerce. Strategic Innovations of AI and ML for E-Commerce Data Security, IGI Global.","DOI":"10.4018\/979-8-3693-5718-7.ch012"},{"key":"ref_9","unstructured":"Kalusivalingam, A.K., Sharma, A., Patel, N., and Singh, V. (2022). Enhancing B2B Fraud Detection Using Ensemble Learning and Anomaly Detection Algorithms. Int. J. AI ML, 3."},{"key":"ref_10","unstructured":"Gracious, L.A., Sudha, L., Chitra, B., Kaur, G., Sathya, V., Kabitha, P., and Subramanian, R.S. (2025). Advancing E-Commerce Security: Strategic Innovations and Future Directions in AI and ML. Strategic Innovations of AI and ML for E-Commerce Data Security, IGI Global."},{"key":"ref_11","first-page":"32","article-title":"Dynamic cybersecurity strategies for ai-enhanced ecommerce: A federated learning approach to data privacy","volume":"2","author":"Khurana","year":"2019","journal-title":"Appl. Res. Artif. Intell. Cloud Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"29830","DOI":"10.1109\/ACCESS.2025.3541979","article-title":"Integrating Explainable Artificial Intelligence in Anomaly Detection for Threat Management in E-Commerce Platforms","volume":"13","author":"Navarro","year":"2025","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"1044","article-title":"Recent Machine-Learning-Driven Developments in E-Commerce: Current Challenges and Future Perspectives","volume":"28","author":"Bunian","year":"2023","journal-title":"Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.3390\/jtaer18040110","article-title":"A brief survey of machine learning and deep learning techniques for e-commerce research","volume":"18","author":"Zhang","year":"2023","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_15","unstructured":"Kalla, D. (2024). Improving E-Commerce Organization Performance Using Big Data Analytics and Artificial Intelligence. [Ph.D. Thesis, Colorado Technical University]."},{"key":"ref_16","first-page":"1041741","article-title":"Data Analysis and Prediction Modeling Based on Deep Learning in E-Commerce","volume":"2022","author":"Feng","year":"2022","journal-title":"Sci. Program."},{"key":"ref_17","unstructured":"Yu, W., Sun, Z., Liu, H., Li, Z., and Zheng, Z. (2018, January 12). Multi-level Deep Learning based e-Commerce Product Categorization. Proceedings of the eCOM@ SIGIR, Ann Arbor, MI, USA."},{"key":"ref_18","unstructured":"Shankar, D., Narumanchi, S., Ananya, H., Kompalli, P., and Chaudhury, K. (2017). Deep learning based large scale visual recommendation and search for e-commerce. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126","DOI":"10.32996\/jbms.2024.6.2.12","article-title":"Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs","volume":"6","author":"Nabi","year":"2024","journal-title":"J. Bus. Manag. Stud."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.future.2021.06.058","article-title":"E-commerce products recognition based on a deep learning architecture: Theory and implementation","volume":"125","author":"Zhang","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_21","first-page":"166","article-title":"Sentiment analysis for E-commerce products using natural language processing","volume":"25","author":"Jha","year":"2021","journal-title":"Ann. Rom. Soc. Cell Biol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, X. (2020, January 12\u201314). Sentiment analysis of e-commerce customer reviews based on natural language processing. Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligenceand Internet of Things Engineering (ICBAIE 2020), Fuzhou, China.","DOI":"10.1145\/3436286.3436293"},{"key":"ref_23","unstructured":"Soundarapandian, R. (2024). Natural Language Processing in E-Commerce-Enhancing Customer Experience, Academic Guru Publishing House."},{"key":"ref_24","first-page":"60","article-title":"Enhancing customer experience through sentiment analysis and natural language processing in e-commerce","volume":"15","author":"Ismail","year":"2024","journal-title":"J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ins.2023.01.104","article-title":"Effective enhancement of isolation Forest method based on Minimal Spanning tree clustering","volume":"628","author":"Karczmarek","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_26","unstructured":"Marteau, P.F., Soheily-Khah, S., and B\u00e9chet, N. (2017). Hybrid isolation forest-application to intrusion detection. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Zou, C., and Dong, J. (2019, January 24\u201327). Outlier detection using isolation forest and local outlier factor. Proceedings of the Conference on Research in Adaptive and Convergent Systems (RACS \u201919), Chongqing, China.","DOI":"10.1145\/3338840.3355641"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15\u201319). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TKDE.2019.2947676","article-title":"Extended isolation forest","volume":"33","author":"Hariri","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3182\/20130902-3-CN-3020.00044","article-title":"An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window","volume":"46","author":"Ding","year":"2013","journal-title":"IFAC Proc. Vol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Heigl, M., Anand, K.A., Urmann, A., Fiala, D., Schramm, M., and Hable, R. (2021). On the improvement of the isolation forest algorithm for outlier detection with streaming data. Electronics, 10.","DOI":"10.3390\/electronics10131534"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dzemydien\u0117, D., Burinskien\u0117, A., \u010ci\u017ei\u016bnien\u0117, K., and Miliauskas, A. (2023). Development of E-service provision system architecture based on IoT and WSNs for monitoring and management of freight intermodal transportation. Sensors, 23.","DOI":"10.3390\/s23052831"},{"key":"ref_33","unstructured":"Pathan, A.S.K., Islam, H.K., Sayeed, S.A., Ahmed, F., and Hong, C.S. (2007). A framework for providing e-services to the rural areas using wireless ad hoc and sensor networks. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"414","DOI":"10.11591\/eei.v11i1.3255","article-title":"Deployment of e-services based contextual smart agro system using internet of things","volume":"11","author":"Sattar","year":"2022","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Arnab, A.A., Shuvro, A.A., Ma, K., and Leung, H. (2023, January 12\u201327). A Deep Learning Approach for a QoS Prediction System in Cellular Networks. Proceedings of the 2023 IEEE 9th World Forum on Internet of Things (WF-IoT), Aveiro, Portugal.","DOI":"10.1109\/WF-IoT58464.2023.10539507"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rehman, I.U., Nasralla, M.M., and Philip, N.Y. (2019). Multilayer perceptron neural network-based QoS-aware, content-aware and device-aware QoE prediction model: A proposed prediction model for medical ultrasound streaming over small cell networks. Electronics, 8.","DOI":"10.3390\/electronics8020194"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"33600","DOI":"10.1109\/ACCESS.2024.3420103","article-title":"Predicting Quality of Multimedia Experience using Electrocardiogram and Respiration Signals","volume":"13","author":"Vijayakumar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/TCE.2017.015076","article-title":"Seamless human-device interaction in the internet of things","volume":"63","year":"2017","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"S\u00f6ldner, R., Rheinl\u00e4nder, S., Meyer, T., Olszowy, M., and Austerjost, J. (2022). Human\u2013device interaction in the life science laboratory. Smart Biolabs of the Future, Springer.","DOI":"10.1007\/10_2021_183"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16149","DOI":"10.1007\/s00521-020-04805-x","article-title":"Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction","volume":"32","author":"Ni","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Majewski, M., and Kacalak, W. (2016). Intelligent speech interaction of devices and human operators. Software Engineering Perspectives and Application in Intelligent Systems, Springer.","DOI":"10.1007\/978-3-319-33622-0_42"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/THMS.2015.2487509","article-title":"A human operator model for medical device interaction using behavior-based hybrid automata","volume":"46","author":"Niezen","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_43","unstructured":"Axelbrooke, S. (2025, July 09). Customer Support on Twitter. Available online: https:\/\/www.kaggle.com\/datasets\/thoughtvector\/customer-support-on-twitter."},{"key":"ref_44","unstructured":"Leveni, F., Cassales, G.W., Pfahringer, B., Bifet, A., and Boracchi, G. (2025). Online isolation forest. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Geng, G., Wang, P., Sun, L., and Wen, H. (2025). Enhanced isolation forest-based algorithm for unsupervised anomaly detection in lidar SLAM localization. World Electr. Veh. J., 16.","DOI":"10.3390\/wevj16040209"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Q., Dong, B., Su, Y., and Sun, Y. (2025, January 14\u201316). A Fuzz Testing Method for Smart Grid Terminals Based on the Isolation Forest Algorithm. Proceedings of the 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China.","DOI":"10.1109\/ITOEC63606.2025.10968759"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Herreros-Mart\u00ednez, A., Magdalena-Benedicto, R., Vila-Franc\u00e9s, J., Serrano-L\u00f3pez, A.J., P\u00e9rez-D\u00edaz, S., and Mart\u00ednez-Herr\u00e1iz, J.J. (2025). Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes: A Hybrid Approach Using Clustering and Isolation Forest. Information, 16.","DOI":"10.3390\/info16030177"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ka\u0161\u0161aj, M., and Per\u00e1\u010dek, T. (2024). Sustainable connectivity\u2014Integration of mobile roaming, WiFi4EU and smart city concept in the European union. Sustainability, 16.","DOI":"10.3390\/su16020788"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mah, P.M., Skalna, I., Pe\u0142ech-Pilichowski, T., Derlecki, T., Mah, V.A., and Nyamka, K. (2023). Enabling Digital Transformation and Knowledge Migration: The Impact of NLP, AI, and ML in Mobile Applications, Faculty of Organization and Management, Silesian University of Technology.","DOI":"10.29119\/1641-3466.2023.184.14"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Du\u0161ek, J. (2023). Data Boxes as a Part of the Strategic Concept of Computerization of Public Administration in the Czech Republic. Adm. Sci., 13.","DOI":"10.3390\/admsci13060154"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"102724","DOI":"10.1109\/ACCESS.2025.3577968","article-title":"Machine Learning in Intelligent Networks: Architectures, Techniques, and Use Cases","volume":"13","author":"Dritsas","year":"2025","journal-title":"IEEE Access"}],"container-title":["Journal of Theoretical and Applied Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/0718-1876\/20\/3\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:27:21Z","timestamp":1760034441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/0718-1876\/20\/3\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["jtaer20030214"],"URL":"https:\/\/doi.org\/10.3390\/jtaer20030214","relation":{},"ISSN":["0718-1876"],"issn-type":[{"value":"0718-1876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]}}}