Unity is strength!

Illiano, Vittorio P.; Steiner, Rodrigo Vieira; Lupu, Emil

doi:10.1145/3098243.3098249

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Subsequent studies have explored techniques for detecting FDIAs from sensor measurements, with the majority of these works based on exploring inter-measurement correlations. Illiano et al [ 16 ] presented an approach to detecting FDIAs in WSNs that combines measurement checks and authentication strategies. Aboelwafa et al [ 17 ] addressed an approach to detecting FDIAs in the industrial Internet of Things that exploits sensor data correlation in time and space.…”

Section: Related Workmentioning

confidence: 99%

“…While some works [ 16 , 21 ] have considered the collusive scenario, further development is needed for when an attacker employs stealthy attacks that construct coherent attack signals (mimicking genuine data distributions and time series patterns). Therefore, in this paper, we propose a generalized detection framework that can be used to detect FDIAs in large-scale WSNs, including stealthy FDIAs.…”

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. [26] presented a method combining a measurement check and authentication strategies to detect FDIAs in WSNs. Ref.…”

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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“…Although real deployments may be considerably larger (e.g., in the case of forests), within the chosen deployment size we can reasonably rely on measurements inspection only. Within larger deployment areas (e.g., in the order of 10 hectares), we need to complement measurements inspection with other techniques, such as software attestation, as shown in Illiano et al [25].…”

Section: Case Study: Wildfire-monitoring Wireless Sensor Networkmentioning

confidence: 99%

Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks

Illiano

Paudice

Muñoz-González

et al. 2018

ACM Trans. Sen. Netw.

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Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack's chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system's resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.

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Unity is strength!

Cited by 4 publications

References 23 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

Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks

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