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

Suhail, Sabah; Hussain, Rasheed; Abdellatif, Mohammad M.; Pandey, Shashi Raj; Khan, Abid; Hong, Choong Seon

doi:10.1016/j.comnet.2020.107189

Cited by 21 publications

(11 citation statements)

References 33 publications

(65 reference statements)

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“…Gray Code as data a provenance technique: Data provenance plays a vital role in assuring data trustworthiness (Suhail et al, 2020). Since data are originated by multiple Figure 5 IGTDC framework for trustworthy smart irrigation system.…”

Section: Data Reliability At the Local Aggregatormentioning

confidence: 99%

“…Gray Code as data a provenance technique: Data provenance plays a vital role in assuring data trustworthiness ( Suhail et al, 2020 ). Since data are originated by multiple sensors’ modules and transmitted to the upstream nodes in IGTDC, by utilizing data provenance, we can trace the data source for data reliability validation, such as which sensor participated in the event detection and vice versa.…”

Section: Igtdc: Trustworthy Data Collection Frameworkmentioning

confidence: 99%

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In-network generalized trustworthy data collection for event detection in cyber-physical systems

Rahman

Wang

Bhuiyan

et al. 2021

PeerJ Computer Science

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Sensors in Cyber-Physical Systems (CPS) are typically used to collect various aspects of the region of interest and transmit the data towards upstream nodes for further processing. However, data collection in CPS is often unreliable due to severe resource constraints (e.g., bandwidth and energy), environmental impacts (e.g., equipment faults and noises), and security concerns. Besides, detecting an event through the aggregation in CPS can be intricate and untrustworthy if the sensor's data is not validated during data acquisition, before transmission, and before aggregation. This paper introduces In-network Generalized Trustworthy Data Collection (IGTDC) framework for event detection in CPS. This framework facilitates reliable data for aggregation at the edge of CPS. The main idea of IGTDC is to enable a sensor's module to examine locally whether the event's acquired data is trustworthy before transmitting towards the upstream nodes. It further validates whether the received data can be trusted or not before data aggregation at the sink node. Additionally, IGTDC helps to identify faulty sensors. For reliable event detection, we use collaborative IoT tactics, gate-level modeling with Verilog User Defined Primitive (UDP), and Programmable Logic Device (PLD) to ensure that the event's acquired data is reliable before transmitting towards the upstream nodes. We employ Gray code in gate-level modeling. It helps to ensure that the received data is reliable. Gray code also helps to distinguish a faulty sensor. Through simulation and extensive performance analysis, we demonstrate that the collected data in the IGTDC framework is reliable and can be used in the majority of CPS applications.

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Section: Data Reliability At the Local Aggregatormentioning

confidence: 99%

Section: Igtdc: Trustworthy Data Collection Frameworkmentioning

confidence: 99%

In-network generalized trustworthy data collection for event detection in cyber-physical systems

Rahman

Wang

Bhuiyan

et al. 2021

PeerJ Computer Science

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“…The former is provenance technique and the latter is termed as IP traceback technique. In IoT, provenance based RPL packet path tracing is proposed in [187]. In their novel lossless provenance scheme, the authors set a bit in IPv6 extension header, to acquire data generating node and path provenance.…”

Section: Intrusion Response In Iotmentioning

confidence: 99%

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

2020

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The Internet of Things (IoT) exemplifies a large network of sensing and actuating devices that have penetrated into the physical world enabling new applications like smart homes, intelligent transportation, smart healthcare and smart cities. Through IoT, these applications have consolidated in the modern world to generate, share, aggregate and analyze large amount of security-critical and privacy sensitive data. As this consolidation gets stronger, the need for security in IoT increases. With first line of defense strategies like cryptography being unsuited due to the resource constrained nature, second line of defense mechanisms are crucial to ensure security in IoT networks. This paper presents a comprehensive study of existing second line of defense mechanisms for standardized protocols in IoT networks. The paper analyzes existing mechanisms in three aspects: Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS) and Intrusion Response Systems (IRS). We begin by providing an overview of standardized protocol stack, its layers and defensive security systems in IoT. From there, we build our narrative by presenting an extended taxonomy of IDS, IPS and IRS classifying them on their techniques, deployment, attacks, datasets, evaluation metrics and data pre-processing methods. We then thoroughly review, compare and analyze the research proposals in this context, considering the unique characteristics involved in these systems. Based on the extensive analysis of the existing defensive security systems, the paper also identifies open research challenges and directions for effective design of such systems for IoT networks, which could guide future research in the area.

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“…Globally accessible devices and resource-constrained interconnections via an unreliable and untrusted Internet are vulnerable to attacks using packet drops, false data injection, and data forging, which affect the decision-making processes in applications. The provenance reliance for data trustworthiness is considered an effective method to track data transmission and data acquisition [8,9]. Most traditional global detection methods used in building secure networks focus on the nodes encounter ratio and requires holistic cognition for the network structure.…”

Section: Introductionmentioning

confidence: 99%

“…The efficiency and adaptability of the model is low in the network. The routing protocol method [8] was used for low-power and lossy networks to identify malicious nodes or faults in the nodes. The collected data was analyzed to study the malicious node and improved the efficiency of the model.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

Sumathi

Muthuramalingam

Prado

et al. 2021

Sensors

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Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.

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

Cited by 21 publications

References 33 publications

In-network generalized trustworthy data collection for event detection in cyber-physical systems

In-network generalized trustworthy data collection for event detection in cyber-physical systems

Towards a Secure Internet of Things: A Comprehensive Study of Second Line Defense Mechanisms

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

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