CNN-Based Attack Defense for Device-Free Localization

Han, Zhaoyang; Lin, Li; Wang, Ziyue; Lian, Zhuotao; Qiu, Chengfeng; Huang, Hua; Zhao, Lingjun; Su, Chunhua

doi:10.1155/2022/2323293

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…In this section, we simulate the experiment on the real‐world KITTI dataset [7] under the LiDAR spoofing attack (LSA) and GPS spoofing attack (GSA). Our method was trained on 650 samples and achieved an average accuracy of 100%, which is 56% higher than deep learning methods [4] with the same amount of data. Based on the analysis of real‐world sensor attack models [2], we set the anomaly rate to 0.025%.GSA is design based on experiments [8].…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…Sensor redundancy enhances environmental perception through the integration of multiple sensors [1]. Deep learning approaches train on large data sets to perform advanced attack recognition tasks [4]. However, sensor redundancy methods require high sensor accuracy and consistency, which may be affected by sensor noise and attacks in real scenarios and increase the cost [3].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive attack recognition method based on probability model for autonomous vehicle

Miao,

Shao,

et al. 2024

Electronics Letters

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The perception system is essential for autonomous vehicle safety and stability. However, on‐board sensors are vulnerable to external attacks, compromising driving strategies and security. Traditional attack detection methods, relying on static anomaly thresholds from data distributions, falter in complex driving scenarios. Recent studies have sought more nuanced detection techniques, including deep learning and inter‐vehicle communication, but these approaches face limitations related to data security and environmental dependency. This letter introduces an innovative, adaptive method for sensor attack detection in autonomous vehicles. By modelling the perception system's functions and employing the Gaussian process for probabilistic modelling, we generate dynamic uncertainty estimates that serve as adaptable anomaly boundaries. Our method's efficacy and resilience are validated through quantitative analysis of the real‐world KITTI dataset, demonstrating superior adaptive detection capabilities.

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Section: Simulation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive attack recognition method based on probability model for autonomous vehicle

Miao,

Shao,

et al. 2024

Electronics Letters

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show abstract

“…Wei et al [17] implemented a phase calibration method to mitigate phase shifts caused by imperfect synchronization and used a structural similarity-based enhancement method to extend datasets and obtain additional fingerprint feature information, enhancing localization accuracy. Han et al [18] developed a defense method using Convolutional Neural Networks (CNN) to counter device-free localization attacks. They transformed the localization problem into an image classification problem and employed CNN for anomaly detection, enhancing system robustness and security.…”

Section: A Csi-based Device-free Passive Fingerprinting Localizationmentioning

confidence: 99%

MFFALoc: CSI-Based Multifeatures Fusion Adaptive Device-Free Passive Indoor Fingerprinting Localization

Rao,

Luo,

Luo

et al. 2024

IEEE Internet Things J.

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In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and "unconscious cooperative sensing" to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-ofthe-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

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“…Localization Techniques. Numerous wireless sensor network (WSN) applications rely on localization to locate a target by comparing the signal strengths of transmitters and receivers already set up in the region of interest [33,34]. Some algorithms are essential for finding and assessing the location and position of the nodes and security enhance-ment for precise location of the target.…”

Section: Cluster Formation and Data Aggregationmentioning

confidence: 99%

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

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Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

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CNN-Based Attack Defense for Device-Free Localization

Cited by 6 publications

References 24 publications

Adaptive attack recognition method based on probability model for autonomous vehicle

Adaptive attack recognition method based on probability model for autonomous vehicle

MFFALoc: CSI-Based Multifeatures Fusion Adaptive Device-Free Passive Indoor Fingerprinting Localization

Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

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