Detection of jamming attack using timestamp for WSN

Rose, S G Hymlin; Jayasree, T.

doi:10.1016/j.adhoc.2019.101874

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…The article by Rose et al [24] introduces a technique for WSN with clustering-based topologies. They propose the use of timestamps to detect the presence of malicious nodes, which generate a jamming attack.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Del-Valle-Soto

Mex-Perera

Nolazco-Flores

et al. 2021

Sensors

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Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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“…Osanaiye, O. et al [ 20 ] have used an exponentially weighted moving average method to detect jamming using the packet inter-arrival time of the packets received from the sensor nodes. The authors in study [ 27 ] propose a technique based on clustering approach and timestamp which made a contribution to the grouping of sensor nodes and the timestamp calculated from one node to another node. A PDR and RSSI metrics are used by Vijayakumar, K. et al [ 28 ] to detect jamming with two methods: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS).…”

Section: Related Workmentioning

confidence: 99%

Performance Metric Analysis for a Jamming Detection Mechanism under Collaborative and Cooperative Schemes in Industrial Wireless Sensor Networks

Cortés-Leal

Del-Valle-Soto

Cardenas

et al. 2021

Sensors

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The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.

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“…At this point, every node updates its local clock time by using pairwise or weighted averaging strategies until converging to the average of the initial clock among all nodes. The generic mathematical model of clock update rule for CTS can be written as [19]: where C i (k) is the local time at node i during each iteration 'k' and 'ε' is the constant step size for each iteration. N i is the set of neighboring nodes that can communicate reliably with node i.…”

Section: Consensus Time Synchronization Modelmentioning

confidence: 99%

Security Threat Analysis and Countermeasures on Consensus-Based Time Synchronization Algorithms for Wireless Sensor Network

Jha

Gupta

Panigrahi³

2021

SN COMPUT. SCI.

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Time synchronization is an indispensable and fundamental requirement for all types of wireless sensor networks (WSNs) protocols and applications. The consistency and correctness of other protocols like localization, routing, etc., are highly dependent on time synchronization. However, due to the hostile deployment of wireless sensor networks, they are prone to cyber-physical attacks that create different threats to time synchronization protocols as well. Recently, consensus-based time synchronization (CTS) algorithms are gaining popularity due to their distributed nature and robustness toward different types of threats such as denial-of-service and node destruction. But, the inherent properties of the consensus-based approach of making in-network averaging make these algorithms vulnerable to message manipulation attacks. In this paper, an in-depth simulation-based analysis is conducted using pymote, a python-based discrete event simulator for WSN, on state-of-the-art CTS algorithms in the presence of message manipulation attacks. A novel and generic algorithm, Message Manipulation Attack Resilient CTS (MMAR-CTS) is proposed embedding which the state-of-the-art algorithms are extensively evaluated based on standard performance metrics, e.g., convergence speed, global synchronization error, and local synchronization error in the presence of the attack, and sufficient observations are derived to show the behavior of these algorithms. The comparison of protocols is validated with simulation results. Simulation results show that MMAR-CTS embedded SATS algorithm is 75% more efficient as compared to other candidate algorithms, measured in terms of convergence speed (number of iterations) and nearly 40% improvement in global and local synchronization error.

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“…These schemes are often based on login credentials with stored authentication values or private/public key schemes. Attacks can originate from traditionally expected adversaries located inside or outside the network or from previously trusted nodes acting maliciously [21], [22], [23]. Recent advancements in wearable wireless sensors with quality requirements namely, energy, memory, and computational efficiency further incorporate ZKP to provide lightweight authentication with appropriate commitment schemes [24], [25].…”

Section: Related Workmentioning

confidence: 99%

Non-interactive zero knowledge proofs for the authentication of IoT devices in reduced connectivity environments

et al. 2019

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“…Likewise, Casado-Vara et al [20] exhibits an architecture that combines WSN and the blockchain mechanism, presenting the functionality regarding the decentralization of the system, as well as the operation of collection data, but leaving aside characteristics such as integrity, reliability, and security in data transmission and the use of encryption tools. Rose et al [21] clusters the sensor nodes and calculates the timestamp from one node to another. In this methodology, the network nodes are considered infected if the measured transmission time exceeds some threshold.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Mechanism and Symmetric Encryption in A Wireless Sensor Network

Guerrero-Sánchez

Rivas-Araiza

Gonzalez-Cordoba

et al. 2020

Sensors

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The Internet of Things (IoT) paradigm allows the connection and exchange of information between millions of smart devices. This paradigm grows and develops exponentially as do the risks and attacks on IoT infrastructures. Security, privacy, reliability, and autonomy are the most important requirements in IoT Systems. If these issues are not guaranteed, the IoT system could be susceptible to malicious users and malicious use. In centralized IoT systems, attacks and risks are greater, especially when data is transmitted between devices and shared with other organizations. To avoid these types of situations, this work presents a decentralized system that guarantees the autonomy and security of an IoT system. The proposed methodology helps to protect data integrity and availability based on the security advantages provided by blockchain and the use of cryptographic tools. The accuracy of the proposed methodology was measured on a temperature and humidity sensing IoT-based Wireless Sensor Network (WSN). The obtained results prove that the proposal fulfils the main requirements of an IoT system. It is autonomous, secure to share and send information between devices and users, has privacy, it is reliable, and the information is available in the infrastructure. Furthermore, this research demonstrates that the proposal is less susceptible to the most frequent attacks against IoT systems, such as linking attack, man in the middle, and Distributed Denial of Service (DDoS) attack.

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Detection of jamming attack using timestamp for WSN

Cited by 18 publications

References 3 publications

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Performance Metric Analysis for a Jamming Detection Mechanism under Collaborative and Cooperative Schemes in Industrial Wireless Sensor Networks

Security Threat Analysis and Countermeasures on Consensus-Based Time Synchronization Algorithms for Wireless Sensor Network

Non-interactive zero knowledge proofs for the authentication of IoT devices in reduced connectivity environments

Blockchain Mechanism and Symmetric Encryption in A Wireless Sensor Network

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