Introducing Secure Provenance in IoT: Requirements and Challenges

Suhail, Sabah; Hong, Choong Seon; Ahmad, Zuhaib Uddin; Zafar, Faheem; Khan, Abid

doi:10.1109/siot.2016.011

Cited by 24 publications

(23 citation statements)

References 21 publications

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“…ProvThings solves unique challenges associated with building a general provenance framework for IoT platforms and further enables provenance-based applications in the domain of IoT platforms. Provenance solutions have been proposed in previous works [39], [32], [71] for IoT devices. However, these solutions are targeted towards IoT devices and cannot be directly applied to IoT platforms which is the main focus of this paper.…”

Section: Related Workmentioning

confidence: 99%

Fear and Logging in the Internet of Things

Wang¹,

Hassan²,

Bates³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

147

103

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Abstract-As the Internet of Things (IoT) continues to proliferate, diagnosing incorrect behavior within increasinglyautomated homes becomes considerably more difficult. Devices and apps may be chained together in long sequences of triggeraction rules to the point that from an observable symptom (e.g., an unlocked door) it may be impossible to identify the distantly removed root cause (e.g., a malicious app). This is because, at present, IoT audit logs are siloed on individual devices, and hence cannot be used to reconstruct the causal relationships of complex workflows. In this work, we present ProvThings, a platform-centric approach to centralized auditing in the Internet of Things. ProvThings performs efficient automated instrumentation of IoT apps and device APIs in order to generate data provenance that provides a holistic explanation of system activities, including malicious behaviors. We prototype ProvThings for the Samsung SmartThings platform, and benchmark the efficacy of our approach against a corpus of 26 IoT attacks. Through the introduction of a selective code instrumentation optimization, we demonstrate in evaluation that ProvThings imposes just 5% overhead on physical IoT devices while enabling real time querying of system behaviors, and further consider how ProvThings can be leveraged to meet the needs of a variety of stakeholders in the IoT ecosystem.

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Section: Related Workmentioning

confidence: 99%

Fear and Logging in the Internet of Things

Wang¹,

Hassan²,

Bates³

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

147

103

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show abstract

“…RPL is a standardized routing protocol for the IP-connected IoT. It creates a Destination-Oriented Directed Acyclic Graph (DODAG) and supports 2 modes of operation (1,2,5,6) in NT and also routing entries (5,6,8,9,10) in RT.…”

Section: Overview Of the Rpl Protocolmentioning

confidence: 99%

“…addition to these attacks, the untrustworthy data traversal across unattended sensor nodes may cause issues during data debugging, data verification and data authenticity [6].…”

mentioning

confidence: 99%

Provenance-enabled packet path tracing in the RPL-based internet of things

et al. 2020

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The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make them vulnerable to attacks including data forging, false data injection, and packet drop that affects applications with critical decisionmaking processes. For data trustworthiness, reliance on provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenges, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to network disruptions) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, a system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of the packet sequence number is enclosed as a routing entry in the routing table of each participating node. Lossless in nature, both approaches conserve the provenance size satisfying processing and storage requirements of IoT devices. Finally, we evaluate the efficacy of the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.

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“…In [3], the authors have identified the requirements and challenges of implementing a secure provenance system for IoT based networks. They claim that if data traces of IoT devices are recorded then provenance can play a vital role as it solves many issues related to data trustworthiness, decisionmaking, data reconciliation and data replication.…”

Section: Related Workmentioning

confidence: 99%

“…The data received by these devices is vulnerable to attacks. The most critical attacks in the field of monitoring and surveillance are false data injection and the corruption of data [3]. For ensuring the data integrity and sender"s authentication, researchers have proposed data provenance mechanisms, which can identify a change in the data and provide a list of sender and forwarder of the data [6].…”

Section: Introductionmentioning

confidence: 99%

Secure Data Provenance in Internet of Things based Networks by Outsourcing Attribute based Signatures and using Bloom Filters

Siddiqui¹,

Nadeem²,

Alzahrani³

2019

IJACSA

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With the dawn of autonomous organization and network and service management, the integration of existing networks with Internet of Things (IoT) based networks is becoming a reality. With minimal human interaction, the security of IoT data moving through the network becomes prone to attacks. IoT networks require a secure provenance mechanism, which is efficient and lightweight because of the scarce computing and storage resources at the IoT nodes. In this paper, we have proposed a secure mechanism to sign and authenticate provenance messages using Ciphertext-Policy Attribute Based Encryption (CP-ABE) based signatures. The proposed technique uses Bloom filters to reduce storage requirements and an outsourced ABE mechanism to use lessen the computational requirements at the IoT devices. The proposed technique helps in reducing the storage requirements and computation time in IoT devices. The performance of the proposed mechanism is evaluated and the results show that the proposed solution is best suited for resourced constrained IoT network.

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Introducing Secure Provenance in IoT: Requirements and Challenges

Cited by 24 publications

References 21 publications

Fear and Logging in the Internet of Things

Fear and Logging in the Internet of Things

Provenance-enabled packet path tracing in the RPL-based internet of things

Secure Data Provenance in Internet of Things based Networks by Outsourcing Attribute based Signatures and using Bloom Filters

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