Data tampering attacks diagnosis in dynamic wireless sensor networks

Huang, Da-Wen; Liu, Wanping; Bi, Jichao

doi:10.1016/j.comcom.2021.03.007

Cited by 22 publications

(12 citation statements)

References 41 publications

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“…Berjab et al [ 19 ] presented a method for detecting FDIAs in WSNs, which uses observed spatiotemporal and multivariate attribute sensor correlations. Huang et al [ 20 ] addressed the problem of detecting FDIAs in dynamic WSNs based on spatial correlation. Based on the spatiotemporal correlation, Hu et al [ 21 ] explored the idea of fault diagnosis to detect collusive FDIAs in WSNs.…”

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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“…The adversary has access to sensitive data from the sensor node, such as security keys and data. Da-Wen Huang et al, 2021 76 propose an algorithm for hybrid diagnosis to detect attacks on dynamic WSNs. The suggested hybrid diagnostic method involves three stages: data comparison, distributed diagnosis, and global diagnosis.…”

Section: Physical Layer Attacksmentioning

confidence: 99%

Wireless sensor network security: A recent review based on state-of-the-art works

Faris

Mahmud

Salleh

et al. 2023

International Journal of Engineering Business Management

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Wireless sensor networks (WSNs) are a major part of the telecommunications sector. WSN is applied in many aspects, including surveillance battlefields, patient medical monitoring, building automation, traffic control, environmental monitoring, and building intrusion monitoring. The WSN is made up of a vast number of sensor nodes, which are interconnected through a network. However, despite the growing usage of applications that rely on WSNs, they continue to suffer from restrictions, such as security issues and limited characteristics due to low memory and calculation power. Security issues lead to a lack of communication between sensors, wasting more energy. The need for efficient solutions has increased, especially with the rise of the Internet of Things, which relies on the effectiveness of WSNs. This review focuses on security issues by reviewing and addressing diverse types of WSN assaults that happened on each layer of the WSN that were published in security issues in the previous 3 years. As a consequence, this paper gives a taxonomy of security threats for each layer and different algorithmic solutions that numerous researchers who seek to counter this attack have explored. This study also presents a framework for constructing an intrusion detection system in the WSN by emphasising the drawbacks of each approach suggested by researchers to defend against specific forms of assault. In order to diminish the impact of this attack, this summary shows which attacks the majority of researchers have dealt with as well as which ones they have not yet addressed in their academic work.

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“…Black Hole and Gray Hole attacks are active attacks since they influence network performance and create crashes [45].…”

Section: Holes Attacksmentioning

confidence: 99%

Machine Learning Methods of IoT Security and Future Application

ALI

NAEEM

Anam

et al. 2022

PPASA

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One of the technologies that are now expanding rapidly is called the Internet of Things (IoT). It is a technology that enables billions of smart devices or things, collectively referred to as “Things,” to collect a variety of data about themselves and the environment in which they are located using a variety of sensors. They can then share data with parties who have been permitted to do so for a variety of objectives, such as the management and monitoring of industrial services or the expansion of company services or operations. However, there are presently more security risks associated with the Internet of Things than ever. The field of machine learning (ML) has recently experienced significant advancement in technology, which has resulted in the opening of various new lines of inquiry that may be used to address existing and upcoming issues related to the Internet of Things. Nevertheless, machine learning is a robust technology that can recognize suspicious dangers and activities in smart devices and grids. The authors of this paper conducted an extensive literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. After that, they compared several different ML algorithms regarding the detection of attacks and anomalies. Additionally, many machines learning-based Internet of Things protection systems have been presented.

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Data tampering attacks diagnosis in dynamic wireless sensor networks

Cited by 22 publications

References 41 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

Wireless sensor network security: A recent review based on state-of-the-art works

Machine Learning Methods of IoT Security and Future Application

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