IoTGaze: IoT Security Enforcement via Wireless Context Analysis

Gu, Tao; Fang, Zheng; Abhishek, Allaukik; Fu, Hao; Hu, Pengfei; Mohapatra, Prasant

doi:10.1109/infocom41043.2020.9155459

Cited by 42 publications

(32 citation statements)

References 20 publications

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“…HoMonit [44], another work of deterministically detecting IoT device activity, monitors encrypted wireless traffic of some home apps and infers smart app activities based on the deterministic finite automaton (DFA) model of smart app behavior and wireless side-channel analysis. IoTGaze [12] also builds up a system to identify IoT device activities using the sniffed wireless traffic. Inspired and motivated by these studies on identifying IoT device types and/or activities, our proposed IoTAthena system is focused on understanding traffic signatures of IoT device activities and accurately extracting device activities from IoT network traffic.…”

Section: Related Workmentioning

confidence: 99%

IoTAthena: Unveiling IoT Device Activities From Network Traffic

Wan

Wang

et al. 2022

IEEE Trans. Wireless Commun.

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The recent spate of cyber attacks towards Internet of Things (IoT) devices in smart homes calls for effective techniques to understand, characterize, and unveil IoT device activities. In this paper, we present a new system, named IoTAthena, to unveil IoT device activities from raw network traffic consisting of timestamped IP packets. IoTAthena characterizes each IoT device activity using an activity signature consisting of an ordered sequence of IP packets with inter-packet time intervals. IoTAthena has two novel polynomial time algorithms, sigMatch and actExtract. For any given signature, sigMatch can capture all matches of the signature in the raw network traffic. Using sigMatch as a subfunction, actExtract can accurately unveil the sequence of various IoT device activities from the raw network traffic. Using the network traffic of heterogeneous IoT devices collected at the router of a real-world smart home testbed and a public IoT dataset, we demonstrate that IoTAthena is able to characterize and generate activity signatures of IoT device activities and accurately unveil the sequence of IoT device activities from raw network traffic.

show abstract

Section: Related Workmentioning

confidence: 99%

IoTAthena: Unveiling IoT Device Activities From Network Traffic

Wan

Wang

et al. 2022

IEEE Trans. Wireless Commun.

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show abstract

“…Besides, the attacker can physically access outside, unprotected IoT devices, such as a doorbell or outdoor surveillance cameras. We also assume the attacker is able to extract IoT app semantics because he can install sniffers and infer event type from the sniffed packets [24]. We treat the remote IoT cloud as trustworthy and do not consider the compromise of the cloud itself.…”

Section: Threat Modelmentioning

confidence: 99%

Iota: A Framework for Analyzing System-Level Security of IoTs

Zheng¹,

Hao²,

Gu³

et al. 2022

Preprint

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Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose IOTA, a logic programming-based framework to perform system-level security analysis for IoT systems. IOTA generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building IOTA, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. To evaluate vulnerabilities' system-wide impact, we propose two metrics based on the attack graph, which provide guidance on fortifying IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that IOTA's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply IOTA to 37 synthetic smart home IoT systems based on realworld IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

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“…Using wireless communications, (Gu et al, 2020) introduced the "wireless context" concept that represented the actual packets workflow of IoT apps. By applying the machine learning model, system anomalies (e.g., App misbehavior, Event spoofing, Over-privilege, Device failure, and Hidden vulnerabilities) can be detected by comparing the user IoT context against the wireless context.…”

Section: Multiple User Iot Apps Conflicts Detectionmentioning

confidence: 99%

Peer Review #1 of "A conflicts’ classification for IoT-based services: a comparative survey (v0.1)"

2021

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IoTGaze: IoT Security Enforcement via Wireless Context Analysis

Cited by 42 publications

References 20 publications

IoTAthena: Unveiling IoT Device Activities From Network Traffic

IoTAthena: Unveiling IoT Device Activities From Network Traffic

Iota: A Framework for Analyzing System-Level Security of IoTs

Peer Review #1 of "A conflicts’ classification for IoT-based services: a comparative survey (v0.1)"

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