{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:56:12Z","timestamp":1780775772355,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC\u2019s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder\u2013Multi-Layer Perceptron (AE\u2013MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE\u2013MLP model leverages autoencoders\u2019 feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE\u2013MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.<\/jats:p>","DOI":"10.3390\/computers13010025","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T08:12:37Z","timestamp":1705392757000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Securing Mobile Edge Computing Using Hybrid Deep Learning Method"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3480-7069","authenticated-orcid":false,"given":"Olusola","family":"Adeniyi","sequence":"first","affiliation":[{"name":"School of Engineering, Computing and Mathematical Sciences, University of Wolverhampton, Wolverhampton WV1 1LY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5746-0257","authenticated-orcid":false,"given":"Ali Safaa","family":"Sadiq","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashant","family":"Pillai","sequence":"additional","affiliation":[{"name":"School of Engineering, Computing and Mathematical Sciences, University of Wolverhampton, Wolverhampton WV1 1LY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9486-3533","authenticated-orcid":false,"given":"Mohammad","family":"Aljaidi","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9669-8244","authenticated-orcid":false,"given":"Omprakash","family":"Kaiwartya","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MWC.2018.1700291","article-title":"Security in mobile edge caching with reinforcement learning","volume":"25","author":"Xiao","year":"2018","journal-title":"IEEE Wirel. 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