{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:17:02Z","timestamp":1770329822729,"version":"3.49.0"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>As networks expand and evolve, their increasing complexity introduces significant security challenges, necessitating robust Intrusion Detection Systems (IDS). Traditional IDS often struggle to detect sophisticated cyberattacks due to their reliance on raw network data and primitive feature extraction techniques. To address these limitations, we propose an Image-enhanced Encoder-based Deep Learning scheme for Intrusion Detection Systems (IEDL-IDS), which combines image-based transformation and encoder-based feature extraction to detect complex intrusion patterns in network traffic. Technically, IEDL-IDS consists of three sequential modules. The preprocessing module transforms raw network traffic into RGB images to reveal temporal and spatial patterns. Thereafter, the encoder module processes the RGB images to extract latent features. Finally, the classifier module utilizes the latent features for high-accuracy intrusion detection. Notably, IEDL-IDS is highly flexible, as its built-in classifier can be easily replaced with any neural network-based model. This feature highlights the adaptability of IEDL-IDS in balancing detection performance with resource constraints, thereby meeting the diverse needs of network security applications. Our experimental results demonstrate that IEDL-IDS outperforms the state-of-the-art IDS schemes. On the CICIoT dataset, IEDL-IDS achieves a classification accuracy of 99.91% for binary classification and 95.66% for multi-class classification. Similarly, it attains 99.61% and 98.25% accuracy on the NSL-KDD dataset, and 99.27% and 96.42% on the ToN_IoT dataset, for binary and multi-class tasks, respectively. Notably, despite its high detection performance, IEDL-IDS maintains a competitive computational footprint, making it a practical and scalable solution for real-world intrusion detection deployments.<\/jats:p>","DOI":"10.1145\/3779432","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T09:12:58Z","timestamp":1765271578000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["IEDL-IDS: An Image-Enhanced Encoder-Based Deep Learning Scheme for Intrusion Detection Systems"],"prefix":"10.1145","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1890-6350","authenticated-orcid":false,"given":"Shiyun","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University - Halifax Campus","place":["Halifax, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6711-7818","authenticated-orcid":false,"given":"Qiang","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University - Halifax Campus","place":["Halifax, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9086-7948","authenticated-orcid":false,"given":"Yujie","family":"Tang","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University - Halifax Campus","place":["Halifax, Canada"]}]}],"member":"320","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"e_1_3_1_2_2","series-title":"ARES \u201921","volume-title":"Proceedings of the 16th International Conference on Availability, Reliability and Security","author":"Abdallah Mahmoud","year":"2021","unstructured":"Mahmoud Abdallah, Nhien An Le Khac, Hamed Jahromi, and Anca Delia Jurcut. 2021. A hybrid CNN-LSTM based approach for anomaly detection systems in SDNs. In Proceedings of the 16th International Conference on Availability, Reliability and Security (Vienna, Austria) (ARES \u201921). Association for Computing Machinery, New York, NY, USA, Article 34, 7 pages. DOI:10.1145\/3465481.3469190"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/978-3-031-34111-3_43","volume-title":"Artificial Intelligence Applications and Innovations","author":"Al-Omar Ban","year":"2023","unstructured":"Ban Al-Omar and Zouheir Trabelsi. 2023. Intrusion detection using attention-based CNN-LSTM model. In Artificial Intelligence Applications and Innovations, Ilias Maglogiannis, Lazaros Iliadis, John MacIntyre, and Manuel Dominguez (Eds.). Springer Nature Switzerland, Cham, 515\u2013526."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05764-5"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3022862"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3282662"},{"key":"e_1_3_1_7_2","first-page":"26831","volume-title":"Advances in Neural Information Processing Systems","author":"Bai Yutong","year":"2021","unstructured":"Yutong Bai, Jieru Mei, Alan L Yuille, and Cihang Xie. 2021. Are transformers more robust than CNNs?. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 26831\u201326843. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/e19347e1c3ca0c0b97de5fb3b690855a-Paper.pdf"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104146"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3340142"},{"key":"e_1_3_1_10_2","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13, Feb (2012), 281\u2013305.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3234530"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"10211","DOI":"10.1109\/ICCV48922.2021.01007","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Bhojanapalli Srinadh","year":"2021","unstructured":"Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, and Andreas Veit. 2021. Understanding robustness of transformers for image classification. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 10211\u201310221. DOI:10.1109\/ICCV48922.2021.01007"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3290650"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3078292"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3169001"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3154884"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3344842"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3441862"},{"key":"e_1_3_1_19_2","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3332284"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3049583"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3165809"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2024.3403394"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.069"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2024.3366848"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3318960"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3114203"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3088149"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2024.101312"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3129775"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2995258"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3336678"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3117028"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.04.020"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3181436"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3056384"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3124331"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/s25072197"},{"issue":"13","key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"5941","DOI":"10.3390\/s23135941","article-title":"CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment","volume":"23","author":"Neto Euclides Carlos Pinto","year":"2023","unstructured":"Euclides Carlos Pinto Neto, Sajjad Dadkhah, Raphael Ferreira, Alireza Zohourian, Rongxing Lu, and Ali A Ghorbani. 2023. CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 23, 13 (2023), 5941.","journal-title":"Sensors"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3233775"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3025087"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3211346"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3059742"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3396461"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2024.3395419"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05994-9"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3071441"},{"key":"e_1_3_1_49_2","first-page":"108","volume-title":"Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP)","author":"Sharafaldin Iman","year":"2018","unstructured":"Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani. 2018. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP) (Portugal). SCITEPRESS, 108\u2013116. DOI:10.5220\/0006639801080116"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3078381"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2972627"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2024.3402158"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3293776"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"e_1_3_1_55_2","first-page":"5759","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Tu Zhengzhong","year":"2022","unstructured":"Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. 2022. MAXIM: Multi-axis MLP for image processing. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5759\u20135770. DOI:10.1109\/CVPR52688.2022.00568"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05685-3"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3342638"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3215244"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2020.3001017"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3334028"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2020.3039526"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3218339"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3116612"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3121131"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3083422"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3019973"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2977007"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3271866"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3396624"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3396624"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3288986"}],"container-title":["ACM Transactions on Privacy and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3779432","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:38:45Z","timestamp":1770287925000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3779432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2,28]]}},"alternative-id":["10.1145\/3779432"],"URL":"https:\/\/doi.org\/10.1145\/3779432","relation":{},"ISSN":["2471-2566","2471-2574"],"issn-type":[{"value":"2471-2566","type":"print"},{"value":"2471-2574","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"assertion":[{"value":"2025-03-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}