Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham
{"title":"Fed-Evolver: An automated evolving approach for federated Intrusion Detection System using adversarial autoencoder in SDN-enabled networks","authors":"Phan The Duy, Do Thi Thu Hien, Tran Duc Luong, Nguyen Huu Quyen, Van-Hau Pham","doi":"10.1016/j.iot.2024.101397","DOIUrl":null,"url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) have garnered escalating significance in response to the evolving landscape of cyberattacks, driven by the adaptability and versatility of Software Defined Networking (SDN)-based networks in enhancing security orchestration. Although Machine Learning (ML) models have been developed for IDS, they require large amounts of labeled data to achieve high performance. However, acquiring labels for attacks is a time-consuming process and can cause problems in deploying the existing ML models in new systems or lower performance due to a shortage of labeled data on pre-trained datasets. Additionally, such ML-based IDS models lack the self-learning function to automatically adapt to new cyberattacks during network operations. To overcome these challenges, our work proposes Fed-Evolver, an automated evolving approach for federated IDS that combines Generative Adversarial Networks (GANs) with Auto Encoder (AE) and a semi-supervised adversarial Autoencoder (SSAAE) for spotting intrusion actions. Our Fed-Evolver leverages supervised and unsupervised learning strategies to build efficient IDS models in the context of labeled data scarcity with the help of Federated Learning (FL). It allows data owners to collaborate for training intrusion detection models to provide the self-evolving capability in SDN-enabled networks. Our proposed framework is evaluated on 6 cyberattack datasets, including CICIDS2018, CIC-ToN-IoT, NF-UNSW-NB15, InSDN, InSecLab-IDS2021, DNP3 Intrusion Detection, and it outperforms other ML methods even when trained with only 1% proportion of labeled data, achieving consistently high performance across all metrics on the datasets.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S254266052400338X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Intrusion Detection Systems (IDS) have garnered escalating significance in response to the evolving landscape of cyberattacks, driven by the adaptability and versatility of Software Defined Networking (SDN)-based networks in enhancing security orchestration. Although Machine Learning (ML) models have been developed for IDS, they require large amounts of labeled data to achieve high performance. However, acquiring labels for attacks is a time-consuming process and can cause problems in deploying the existing ML models in new systems or lower performance due to a shortage of labeled data on pre-trained datasets. Additionally, such ML-based IDS models lack the self-learning function to automatically adapt to new cyberattacks during network operations. To overcome these challenges, our work proposes Fed-Evolver, an automated evolving approach for federated IDS that combines Generative Adversarial Networks (GANs) with Auto Encoder (AE) and a semi-supervised adversarial Autoencoder (SSAAE) for spotting intrusion actions. Our Fed-Evolver leverages supervised and unsupervised learning strategies to build efficient IDS models in the context of labeled data scarcity with the help of Federated Learning (FL). It allows data owners to collaborate for training intrusion detection models to provide the self-evolving capability in SDN-enabled networks. Our proposed framework is evaluated on 6 cyberattack datasets, including CICIDS2018, CIC-ToN-IoT, NF-UNSW-NB15, InSDN, InSecLab-IDS2021, DNP3 Intrusion Detection, and it outperforms other ML methods even when trained with only 1% proportion of labeled data, achieving consistently high performance across all metrics on the datasets.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.