research-article

Public Access

HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps

Authors:

Soteris Demetriou,

Carl A. Gunter,

Michael GraceAuthors Info & Claims

WiSec '17: Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Pages 122 - 133

https://doi.org/10.1145/3098243.3098251

Published: 18 July 2017 Publication History

Abstract

A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.

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Cited By

Rajendran NWilliam PTalwar SJadhav VAugustine PNataraj B(2024)Implementation of Multi-Layer Security Network Identification System Using Machine Learning TechniquesInterdisciplinary Approaches to AI, Internet of Everything, and Machine Learning10.4018/979-8-3373-1032-9.ch018(287-302)Online publication date: 20-Dec-2024
https://doi.org/10.4018/979-8-3373-1032-9.ch018
Shelke CSudha DAchary RShrivastava VTiwari TTiwari M(2024)Machine Learning-Based Multi-Layer Security Network Authentication System for Uncertain Attack in the Wireless Communication SystemInterdisciplinary Approaches to AI, Internet of Everything, and Machine Learning10.4018/979-8-3373-1032-9.ch006(117-130)Online publication date: 20-Dec-2024
https://doi.org/10.4018/979-8-3373-1032-9.ch006
Taurshia AKathrine JAndrew JEunice R J(2024)Securing Internet of Things Applications Using Software-Defined Network-Aided Group Key Management with a Modified One-Way Function TreeApplied Sciences10.3390/app1406240514:6(2405)Online publication date: 13-Mar-2024
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Index Terms

HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps
1. Security and privacy
  1. Network security
    1. Mobile and wireless security

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Published In

cover image ACM Conferences

WiSec '17: Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks

July 2017

297 pages

ISBN:9781450350846

DOI:10.1145/3098243

General Chair:
Guevara Noubir
Northeastern University
,
Program Chairs:
Mauro Conti
University of Padua, Italy
,
Sneha Kumar Kasera
University of Utah

Copyright © 2017 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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NSF: National Science Foundation
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New York, NY, United States

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Published: 18 July 2017

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WiSec '17: 10th ACM Conference on Security & Privacy in Wireless and Mobile Networks

July 18 - 20, 2017

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https://doi.org/10.4018/979-8-3373-1032-9.ch018
Shelke CSudha DAchary RShrivastava VTiwari TTiwari M(2024)Machine Learning-Based Multi-Layer Security Network Authentication System for Uncertain Attack in the Wireless Communication SystemInterdisciplinary Approaches to AI, Internet of Everything, and Machine Learning10.4018/979-8-3373-1032-9.ch006(117-130)Online publication date: 20-Dec-2024
https://doi.org/10.4018/979-8-3373-1032-9.ch006
Taurshia AKathrine JAndrew JEunice R J(2024)Securing Internet of Things Applications Using Software-Defined Network-Aided Group Key Management with a Modified One-Way Function TreeApplied Sciences10.3390/app1406240514:6(2405)Online publication date: 13-Mar-2024
https://doi.org/10.3390/app14062405
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Gilani SUsman MDaud SKabir ANawaz QJudit O(2024)SDN-based multi-level framework for smart home servicesMultimedia Tools and Applications10.1007/s11042-023-15678-283:1(327-347)Online publication date: 1-Jan-2024
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