High-performance, Platform-Independent DDoS Detection for IoT Ecosystems

Santoyo-Gonzalez, A., Cervello-Pastor, C. and Pezaros, D. P. (2020) High-performance, Platform-Independent DDoS Detection for IoT Ecosystems. In: 44th IEEE Conference on Local Computer Networks (LCN 2019), Osnabrück, Germany, 14-17 Oct 2019, pp. 69-75. ISBN 9781728110288 (doi: 10.1109/LCN44214.2019.8990862)

189892.pdf - Accepted Version



Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 − 95% and a bandwidth overhead of less than 1%.

Item Type:Conference Proceedings
Additional Information:This work has been supported by the Ministerio de Econom´ıa y Competitividad of the Spanish Government under the project TEC2016-76795- C6-1-R and AEI/FEDER, UE. Additionally it has been supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/R511936/1, EP/N033957/1, and EP/P004024/1; by BT (Voucher No. 17000117); by the Huawei Innovation Research Program (Grant No. 300952); and by the European Cooperation in Science and Technology (COST) Action CA 15127: RECODIS – Resilient communication and services.
Glasgow Author(s) Enlighten ID:Pezaros, Professor Dimitrios
Authors: Santoyo-Gonzalez, A., Cervello-Pastor, C., and Pezaros, D. P.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2019 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
709131Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1COM - COMPUTING SCIENCE
722161FRuIT: The Federated RaspberryPi Micro-Infrastructure TestbedJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/P004024/1COM - COMPUTING SCIENCE
300952HIRP 2017 - Distributed Intelligence for Network ControlDimitrios PezarosHuawei Technologies (CN) (HUAWE-CN)N/AComputing Science