Intelligent sensor attack detection and identification for automotive cyber-physical systems

Shin, Jong‐Ho; Baek, Youngmi; Eun, Yongsoon; Son, Sang H.

doi:10.1109/ssci.2017.8280915

Cited by 23 publications

(8 citation statements)

References 30 publications

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“…Here, the efficiency of the Fuzzy Logic Concept (14) , GA-SVM (15) , IFSPT-RC-IDeepRT, and IFSPT-RC-PRIDeepRT based CPS data classification methods are analyzed in terms of Accuracy, Precision, Recall, and F_measure. For the experimental purpose Air Quality data and NSF CNS-1446640 datasets (5) are used.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Parameter Reduction Method for Simplifying Improved RC-IDeepRT

Rajalakshmi¹,

Sathappan²

2021

IJST

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Objectives: To decrease the model parameters in Reinforcement learning with Compressed Improved DeepRT (RC-IDeepRT) for Cyber-Physical System (CPS) data analysis, a method is proposed in this study. Methods: A parameter reduction method is proposed to decrease the model parameter by combining pruning and factorization. The pruning may not save the memory usage in traditional computers unless sparse matrices are supported and explicitly used. However, it directly reduces the usage of synapses in neuromorphic architecture. This method minimizes the total number of required neurons and synapses given a trained model. Findings: The integration of factorization and pruning allows creating sparsely connected reinforcement learning with deep learning network from a given trained network. The dangling connections in the network are determined and the remaining connections of the dangling neurons are further pruned. The pruned network is cost-effective and it classifies the CPS data effectively. Novelty: This proposed method tries to minimize the number of parameters in RC-IDeepRT. The number of parameters becomes a good surrogate metric assuming the deep learning architecture accommodates sparse networks effectively. The reduced model parameters are used for CPS data classification.

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Section: Resultsmentioning

confidence: 99%

“…RNN (14) technique was introduced to detect and identify sensor attacks in CPS. In the pre-processing phase, the data collected from the sensor node was normalized and then it was utilized in the learning phase where the weights of the model were fine-tuned with the training data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Parameter Reduction Method for Simplifying Improved RC-IDeepRT

Rajalakshmi¹,

Sathappan²

2021

IJST

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“…After the number of layers is fixed, the architecture is selected by comparing with RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) among DNNs [13,14]. Time step which DNNs consider as historical data is set to 90 by reference to [15]. Validation accuracy is checked with the fixed hyper-parameters.…”

Section: Recurrent Neural Network Architecturementioning

confidence: 99%

Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems

et al. 2018

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The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

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“…Therefore, the detections of attacks related to sensors are of great significance. Recent researches reveal that the detections of sensor attacks are paramount importance for guaranteeing the stabilities and safeties of CPSs . For example, Lu and Yang considered the secure state estimation problem under sparse sensor attacks via a switched observer.…”

Section: Introductionmentioning

confidence: 99%

“…Recent researches reveal that the detections of sensor attacks are paramount importance for guaranteeing the stabilities and safeties of CPSs. [19][20][21][22][23][24][25] For example, Lu and Yang 26,27 considered the secure state estimation problem under sparse sensor attacks via a switched observer. A distributed methodology for detecting and isolating multiple sensor faults for an interconnected cyber-physical system is proposed, 28 and the distributed sensor FDI process is carried out in conducting the superstratum cyber layer.…”

Section: Introductionmentioning

confidence: 99%

Sensor attack detection and isolation based on sliding mode observer for cyber‐physical systems

Zhu

Zhang

2020

Adaptive Control & Signal

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Summary This article investigates the sensor attack detection and isolation problems for cyber‐physical systems under attacked situation. To begin with, an overall system is constructed for the network of dynamic system and all the issues are dealt with for the overall system instead of every subsystem. Then, we prove that the preconditions which are made on the original subsystem can be transferred on to the overall system. After this, for the overall system, a sliding mode observer that is robust to the disturbance but sensitive to the sensor attack is designed as sensor attack detector to issue an alarm when at least one subsystem is attacked. In addition, we cope with the sensor fault isolation problem by building an augmented descriptor system for the overall system and we further prove that the precondition can be kept for the augmented descriptor system. Then, another robust sliding mode observer is designed to reach the goal of attack isolation. Finally, a simulation example is given to verify the effectiveness of the proposed methods.

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Intelligent sensor attack detection and identification for automotive cyber-physical systems

Cited by 23 publications

References 30 publications

A Novel Parameter Reduction Method for Simplifying Improved RC-IDeepRT

A Novel Parameter Reduction Method for Simplifying Improved RC-IDeepRT

Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems

Sensor attack detection and isolation based on sliding mode observer for cyber‐physical systems

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