Identifying malicious nodes in wireless sensor networks based on correlation detection

Lai, Yingxu; Tong, Liyao; Liu, Jing; Wang, Yipeng; Tang, Tong; Zhao, Zijian; Qin, Hua

doi:10.1016/j.cose.2021.102540

Cited by 39 publications

(10 citation statements)

References 12 publications

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“…Islam et al [ 23 ] utilized distributed algorithms based on spatiotemporal correlation to detect data anomalies in large-scale intelligent transportation systems. Lai et al [ 24 ] suggested a distributed approach to detecting FDIAs in WSNs using temporal, spatial, and event-based correlation. In this paper, our framework is based on a distributed approach, where detection methods can be executed at separate edge devices to reduce the network pressure associated with processing data generated by large-scale sensors.…”

Section: Related Workmentioning

confidence: 99%

“…An adversary may employ stealthy attacks, such as constructing coherent attack signals. Most of the works [ 17 , 18 , 19 , 20 , 22 , 23 , 24 ] mentioned based on sensor measurements themselves are effective in detecting simple FDIAs, but not stealthy ones. For instance, in [ 22 ], based on the spatiotemporal correlation of sensor data, the authors used exponential weighted moving average and principal component analysis to establish a rotated ellipse area for each pair of sensors in a correlation group and detected FDIAs by determining whether the current sensor readings for each pair of sensors were located within the corresponding area of the rotated ellipse.…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

Hu,

Yang,

Yang

2024

Sensors

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False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

Hu,

Yang,

Yang

2024

Sensors

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“…Ref. [30] suggested a method using temporal, spatial, and event-based correlations to prevent FDIAs in WSNs.…”

Section: Fdias Detection Of Wsnsmentioning

confidence: 99%

A Novel Diagnosis Scheme against Collusive False Data Injection Attack

Xiao

Yang

2023

Sensors

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The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for judging whether an adjacent pair of sensors are consistent in terms of their readings. Inspired by the system-level fault diagnosis, we introduce a set of watchdogs into a WSN as comparators between adjacent pairs of sensors of the WSN, and we propose an algorithm for diagnosing the WSN based on the collection of the consistency outcomes. Simulation results show that the proposed diagnosis scheme achieves a higher probability of correct diagnosis.

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“…The nodes' mobility is primarily to blame for this. These networks' nodes share a single random-access wireless channel and cooperate amicably to engage in multi hop forwarding [2]. In addition to serving as hosts, network nodes also function as routers, sending and receiving data to and from other nodes.…”

Section: Introductionmentioning

confidence: 99%

Routing and Security Based Ad-hoc Networks Configuration for Identification of Attack Using Reinforcement Learning Approach

Avinash

2024

Preprint

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In many fields of Research, wireless sensor networks have grown in popularity. Depending on the dangerous situation the networks fields, there are more chances to attack wireless sensor network. This research explains how to locate rogue nodes in the networks based on investigation and responses from each node in the networks. In generally a malicious node blocks the transmission of signal to other nodes. So, in order to increase network performance, avoid much traffic for the server to buffer, causing them to slow down and eventually stop. Hence to identify the fake node in wireless sensor network is main aim of this research through Network Simulator with the help of Reinforcement Learning based routing algorithms. Further, attack vectors allow hackers to exploit system’s vulnerabilities including manual elements. It is suggested that a workable security framework for the WSN (Wireless Sensor Network) used to estimate traffic or packet loss and throughput ratio in order to detect fault identity. So enhancing the networks performance is based on request-response mechanism of the nodes, which engages with features at various levels of the protocol's system, monitors and analyzes typical patterns and alerts node to ensure their dangerous activities cannot transmit across the network. Some of monitoring basic functions through routing algorithm and NS2 include overseeing server CPUs, paying attention to network traffic, identifying patterns in error rates, alerting you about slow pages and combing through your access logs to find out how long requests usually take.

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Identifying malicious nodes in wireless sensor networks based on correlation detection

Cited by 39 publications

References 12 publications

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

A Novel Diagnosis Scheme against Collusive False Data Injection Attack

Routing and Security Based Ad-hoc Networks Configuration for Identification of Attack Using Reinforcement Learning Approach

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