research-article

Detecting Volumetric Attacks on loT Devices via SDN-Based Monitoring of MUD Activity

Authors:

Hassan Habibi Gharakheili,

Theophilus A. Benson,

Vijay SivaramanAuthors Info & Claims

SOSR '19: Proceedings of the 2019 ACM Symposium on SDN Research

Pages 36 - 48

https://doi.org/10.1145/3314148.3314352

Published: 03 April 2019 Publication History

Abstract

Smart environments equipped with IoT devices are increasingly under threat from an escalating number of sophisticated cyber-attacks. Current security approaches are inaccurate, expensive, or unscalable, as they require static signatures of known attacks, specialized hardware, or full packet inspection. The IETF Manufacturer Usage Description (MUD) framework aims to reduce the attack surface on an IoT device by formally defining its expected network behavior. In this paper, we use SDN to monitor compliance with the MUD behavioral profile, and develop machine learning methods to detect volumetric attacks such as DoS, reflective TCP/UDP/ICMP flooding, and ARP spoofing to IoT devices.

Our first contribution develops a machine for detecting anomalous patterns of MUD-compliant network activity via coarse-grained (device-level) and fine-grained (flow-level) SDN telemetry for each IoT device, thereby giving visibility into flows that contribute to a volumetric attack. For our second contribution we measure network behavior of IoT devices by collecting benign and volumetric attacks traffic traces in our lab, label our dataset, and make it available to the public. Our last contribution prototypes a full working system (built with an OpenFlow switch, Faucet SDN controller, and a MUD policy engine), demonstrates its application in detecting volumetric attacks on several consumer IoT devices with high accuracy, and provides insights into cost and performance of our system. Our data and solution modules are released as open source to the community.

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cover image ACM Conferences

SOSR '19: Proceedings of the 2019 ACM Symposium on SDN Research

April 2019

166 pages

ISBN:9781450367103

DOI:10.1145/3314148

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 April 2019

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SOSR '19: Symposium on SDN Research

April 3 - 4, 2019

CA, San Jose, USA

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Overall Acceptance Rate 7 of 43 submissions, 16%

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Cited By

Hafid BEzzouhairi AHaddouch K(2025)Toward the Use of Machine Learning and Ensemble Learning Algorithms for IDS in the IoT DomainBig Data and Internet of Things10.1007/978-3-031-74491-4_74(966-979)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-74491-4_74
Zhong YPan MChen YXu W(2024)Res-DFNN: An NN-Based Device Fingerprint Extraction Method Using Network Packet DataSymmetry10.3390/sym1604044316:4(443)Online publication date: 6-Apr-2024
https://doi.org/10.3390/sym16040443
Almasre MSubahi A(2024)Create a Realistic IoT Dataset Using Conditional Generative Adversarial NetworkJournal of Sensor and Actuator Networks10.3390/jsan1305006213:5(62)Online publication date: 3-Oct-2024
https://doi.org/10.3390/jsan13050062
Khazane HRidouani MSalahdine FKaabouch N(2024)A Holistic Review of Machine Learning Adversarial Attacks in IoT NetworksFuture Internet10.3390/fi1601003216:1(32)Online publication date: 19-Jan-2024
https://doi.org/10.3390/fi16010032
Azizi SOkui NNakahara MKubota ABatista GGharakheili HSekar VYu MSeneviratne AVeitch D(2024)Poster: Understanding and Managing Changes in IoT Device Behaviors for Reliable Network Traffic InferenceProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673723(25-27)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673723
Feraudo APopescu DYadav PMortier RBellavista P(2024)Mitigating IoT Botnet DDoS Attacks through MUD and eBPF based Traffic FilteringProceedings of the 25th International Conference on Distributed Computing and Networking10.1145/3631461.3631549(164-173)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3631461.3631549
Wang YSu P(2024)Collaborative Defense Against Hybrid Network Attacks by SDN Controllers and P4 SwitchesIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.332432911:2(1480-1495)Online publication date: Mar-2024
https://doi.org/10.1109/TNSE.2023.3324329
Duan CLi SLin HChen WSong GLi CYang JWang Z(2024)IoTa: Fine-Grained Traffic Monitoring for IoT Devices via Fully Packet-Level ModelsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.334056321:4(3931-3947)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3340563
Mashnoor NCharyyev B(2024)Network Traffic Analysis of Medical Devices2024 International Conference on Smart Applications, Communications and Networking (SmartNets)10.1109/SmartNets61466.2024.10577713(1-6)Online publication date: 28-May-2024
https://doi.org/10.1109/SmartNets61466.2024.10577713
Oredola CAshraf A(2024)A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA64295.2024.00056(317-324)Online publication date: 28-Aug-2024
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