An Aerial-Computing-Assisted Architecture for Large-Scale Sensor Networks

Gu, Zhaoquan; Li, Dongda; Guizani, Nadra; Du, Xiaojiang; Tian, Zhihong

doi:10.1109/mwc.101.2100045

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Recently, the fast development of deep neural networks (DNNs) has resulted in DNN-based models being applied in many scenarios around the Internet of Things, such as smart transportation [ 1 , 2 ], intelligence healthcare [ 3 ], social networks [ 4 ], and information encryption [ 5 , 6 ]. At the same time, the rapid proliferation of attacks against DNN-based models has raised greater security concerns [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems

Zhang

Tan

Zhu

et al. 2023

Journal of Healthcare Engineering

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Deep neural networks (DNNs) have been widely adopted in many fields, and they greatly promote the Internet of Health Things (IoHT) systems by mining health-related information. However, recent studies have shown the serious threat to DNN-based systems posed by adversarial attacks, which has raised widespread concerns. Attackers maliciously craft adversarial examples (AEs) and blend them into the normal examples (NEs) to fool the DNN models, which seriously affects the analysis results of the IoHT systems. Text data is a common form in such systems, such as the patients’ medical records and prescriptions, and we study the security concerns of the DNNs for textural analysis. As identifying and correcting AEs in discrete textual representations is extremely challenging, the available detection techniques are still limited in performance and generalizability, especially in IoHT systems. In this paper, we propose an efficient and structure-free adversarial detection method, which detects AEs even in attack-unknown and model-agnostic circumstances. We reveal that sensitivity inconsistency prevails between AEs and NEs, leading them to react differently when important words in the text are perturbed. This discovery motivates us to design an adversarial detector based on adversarial features, which are extracted based on sensitivity inconsistency. Since the proposed detector is structure-free, it can be directly deployed in off-the-shelf applications without modifying the target models. Compared to the state-of-the-art detection methods, our proposed method improves adversarial detection performance, with an adversarial recall of up to 99.7% and an F1-score of up to 97.8%. In addition, extensive experiments have shown that our method achieves superior generalizability as it can be generalized across different attackers, models, and tasks.

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Section: Introductionmentioning

confidence: 99%

Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems

Zhang

Tan

Zhu

et al. 2023

Journal of Healthcare Engineering

Self Cite

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“…Recently, deep neural networks (DNNs) have achieved remarkable success in classifcation tasks in various felds, such as computer vision [1], network anomalous behavior [2][3][4], and medical domain [5,6]. Te widespread use of electronic health record (EHR) systems has made the task of medical specialty classifcation become more important in modern healthcare.…”

Section: Introductionmentioning

confidence: 99%

Medical Specialty Classification Based on Semiadversarial Data Augmentation

Zhang,

Zhu,

Tan

et al. 2023

Computational Intelligence and Neuroscience

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Rapidly increasing adoption of electronic health record (EHR) systems has caused automated medical specialty classification to become an important research field. Medical specialty classification not only improves EHR system retrieval efficiency and helps general practitioners identify urgent patient issues but also is useful in studying the practice and validity of clinical referral patterns. However, currently available medical note data are imbalanced and insufficient. In addition, medical specialty classification is a multicategory problem, and it is not easy to remove sensitive information from numerous medical notes and tag them. To solve those problems, we propose a data augmentation method based on adversarial attacks. The semiadversarial examples generated during the dynamic process of adversarial attacking are added to the training set as augmented examples, which can effectively expand the coverage of the training data on the decision space. Besides, as nouns in medical notes are critical information, we design a classification framework incorporating probabilistic information of nouns, with confidence recalculation after the softmax layer. We validate our proposed method on an 18-class dataset with extremely unbalanced data, and comparison experiments with four benchmarks show that our method improves accuracy and F1 score to the optimal level, by an average of 14.9%.

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Pricing-based resource allocation in three-tier edge computing for social welfare maximization

Xia²,

Duan³

et al. 2022

Computer Networks

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An Aerial-Computing-Assisted Architecture for Large-Scale Sensor Networks

Cited by 10 publications

References 15 publications

Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems

Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems

Medical Specialty Classification Based on Semiadversarial Data Augmentation

Pricing-based resource allocation in three-tier edge computing for social welfare maximization

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