Search Results for author: Jiliang Zhang

Found 11 papers, 1 papers with code

Microwave QR Code: An IRS-Based Solution

no code implementations5 Aug 2022 Sai Xu, Yanan Du, Jiliang Zhang, Jie Zhang

This letter proposes to employ intelligent reflecting surface (IRS) as an information media to display a microwave quick response (QR) code for Internet-of-Things applications.

An IRS Backscatter Enabled Integrated Sensing, Communication and Computation System

no code implementations20 Jul 2022 Sai Xu, Yanan Du, Jiliang Zhang, Jiangzhou Wang, Jie Zhang

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT).

On the Performance of Data Compression in Clustered Fog Radio Access Networks

no code implementations1 Jul 2022 Haonan Hu, Yan Jiang, Jiliang Zhang, Yanan Zheng, Qianbin Chen, Jie Zhang

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency.

Data Compression

Adaptive Modulation for Wobbling UAV Air-to-Ground Links in Millimeter-wave Bands

no code implementations13 Apr 2022 Songjiang Yang, Zitian Zhang, Jiliang Zhang, Xiaoli Chu, Jie Zhang

Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the data transmission time subject to the maximum tolerable BEP.

Impact of Rotary-Wing UAV Wobbling on Millimeter-wave Air-to-Ground Wireless Channel

no code implementations14 Jul 2021 Songjiang Yang, Zitian Zhang, Jiliang Zhang, Jie Zhang

Our contributions of this paper lie in: i) modeling the wobbling process of a hovering RW UAV; ii) developing an analytical model to derive the channel temporal autocorrelation function (ACF) for the millimeter-wave RW UAV A2G link in a closed-form expression; and iii) investigating how RW UAV wobbling impacts the Doppler effect on the millimeter-wave RW UAV A2G link.

Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

1 code implementation21 Jul 2020 Yansong Gao, Bao Gia Doan, Zhi Zhang, Siqi Ma, Jiliang Zhang, Anmin Fu, Surya Nepal, Hyoungshick Kim

We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor. Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks.

Wireless Performance Evaluation of Building Layouts: Closed-Form Computation of Figures of Merit

no code implementations20 Jul 2020 Jiliang Zhang, Andrés Alayón Glazunov, Jie Zhang

This paper presents a part of our ground-breaking work on evaluation of buildings in terms of wireless friendliness in the building-design stage.

DeepCheck: A Non-intrusive Control-flow Integrity Checking based on Deep Learning

no code implementations6 May 2019 Jiliang Zhang, Wuqiao Chen, Yuqi Niu

Code reuse attack (CRA) is a powerful attack that reuses existing codes to hijack the program control flow.

Adversarial Examples: Opportunities and Challenges

no code implementations13 Sep 2018 Jiliang Zhang, Chen Li

Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis.

Autonomous Vehicles Medical Diagnosis

Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods

no code implementations5 Aug 2018 Xinbo Liu, Yapin Lin, He Li, Jiliang Zhang

As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role. However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks. Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks. We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization method. By introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88. 7% and 74. 1% on average in different ML-based detection methods.

Cryptography and Security

Set-based Obfuscation for Strong PUFs against Machine Learning Attacks

no code implementations6 Jun 2018 Jiliang Zhang, Chaoqun Shen

In order to address these issues, we propose a Random Set-based Obfuscation (RSO) for Strong PUFs to resist machine learning attacks.

Cryptography and Security Hardware Architecture

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