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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

Distributionally Robust Optimization for Computation Offloading in Aerial Access Networks

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Distributionally Robust Optimization for Computation Offload...

IEEE Conference on Global Communications (GLOBECOM)

作者： Guanwang Jiang Ziye Jia Lijun He Chao Dong Qihui Wu Zhu Han Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Software Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature.

关键词： Measurement Uncertainty Multi-access edge computing Simulation Quality of service Linear programming Probability distribution Robustness Delays Optimization

Determination of chloride concentrations in cement paste with terahertz time-domain spectroscopy(THz-TDS)

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Determination of chloride concentrations in cement paste wit...

IEEE International Conference on Plasma Science (ICOPS)

作者： H. Zhai Y. Qiu L. Gong R. Dan W. Liu L. Hu X. Li Y. Hang F. Shi Y. Li Department of Microelectronics School of Electronic and InformationEngineering Key Laboratory of Micro-Nano Electronics and System Integration of Xi’an City Xi’an Jiaotong University Xi’an China Faculty of Electronic and Information Engineering Key Laboratory of Physical Electronics and Devices Ministry of Education Xi’an Jiaotong University Xi’an China School of Software Engineering Xi’an Jiaotong University Xi’an China Suzhou Nuclear Power Research Institute Suzhou Nuclear Power Research Institute Suzhou China

ISBN: (数字)9798350360912

ISBN: (纸本)9798350360929

Cement is a crucial building material. As the service life of a building progresses, erosion can cause changes in the structure and components of the cement, posing a significant safety risk. Therefore, testing the quality of cement is essential to ensure the safety of the building. Especially, in coastal buildings, cement is more susceptible to chloride ion intrusion due to its special porous structure and the higher chloride ion content in the air. Chloride ions binding to calcifiers is the primary cause of cement degradation, reducing its service life and safety. Therefore, chloride content is the main method for characterising cement erosion. However, current inspection methods are difficult to measure in-situ, which can be destructive to the structure and more costly. Terahertz waves can be used to calculate the physical properties of materials directly. This is achieved through the simultaneous extraction of amplitude and phase without contact, which avoids damage to the structure. [1–4] This work utilised terahertz time-domain spectroscopy to measure cement samples containing various chlorine elements. The obtained frequency and absorption spectra were used to characterise the modulation of terahertz spectra by chlorine elements. The results indicate that higher levels of elemental chlorine content result in a weaker spectral intensity. Additionally, an increase in elemental chlorine content within a specific band range leads to a higher absorption coefficient. To better identify the pattern of standard chlorine variation, we calculated the refractive index and dielectric constant. We used the Random Forest algorithm to construct a model that relates terahertz spectra to chlorine variation. The trained model enables a more precise analysis of the chlorine content. In addition, molecular dynamics simulations were used to confirm the correlation between the chlorine content and the spectral variations.

关键词： Spectroscopy Absorption Chlorine Cement industry Ions Safety Time-domain analysis

A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

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arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

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Gaussian Decay Function-based Improved Moment Matching for O...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jiang, Hua Shi, Qiuxia Liu, Siying Zhang, Jiuying Zhao, Qinglin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene. © 2022 IEEE.

关键词： Electroencephalography

Att-TCN: An Advertising Attribution Method Based on Temporal Convolutional Network with Attention Mechanism

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Att-TCN: An Advertising Attribution Method Based on Temporal...

International Conference on Computer and Communications (ICCC)

作者： Bowen Xie Yi Sun Zhaowen Lin Baoquan Ma School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Technologies Beijing China National Computer System Engineering Research Institute of China Beijing China National Engineering Laboratory for Industry Control System Information Security Technology Beijing China

Advertising attribution is an effective method to measure the effect of various advertisements in the era of Internet advertising. It enables scientific tracking back and refined operation of advertising by quantitatively evaluating the contribution of different advertising channels to reach the target. It is challenging to predict conversion results based on users' journey data accurately, reflect the impact of ads on user conversions, and appropriately assign attribution values to advertising channels. To achieve the above objectives, we propose an advertising attribution method based on a temporal convolutional network with an attention mechanism (Att-TCN). By mining the hidden information in time and attribution channels using the attention mechanism, our proposed method can improve the accuracy of conversion prediction and thus more rationally assign attribution values across channels. Experimental validation on a real dataset obtained from an operational commercial advertising monitoring company shows that our proposed attribution method has better accuracy than other state-of-the-art attribution methods and gives an open-ended approach to attribution value assignment.

关键词： Target tracking Costs Companies Predictive models Internet Convolutional neural networks Data mining

Characterization of antioxidant concentration using terahertz time-domain spectroscopy (THz-TDS) in Ethylene-Vinyl-Acetate-Copolymer (EVA)

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Characterization of antioxidant concentration using terahert...

IEEE International Conference on Plasma Science (ICOPS)

作者： L. Gong Y. Qiu H. Zhai R. Dan Y. Dang Y. Hang F. Shi Y. Zhai L. Hu W. Liu X. Li Y. Li Faculty of Electronic and Information Engineering Key Laboratory of Physical Electronics and Devices Ministry of Education Xi’an Jiaotong University Xi’an China Department of Microelectronics School of Electronic and Information Engineering Key Laboratory of Micro-Nano Electronics and System Integration of Xi’an City Xi’an Jiaotong University Xi’an China School of Software Engineering Xi’an Jiaotong University Xi’an China China Guangdong Nuclear Power Suzhou Nuclear Power Research Institute Suzhou China

ISBN: (数字)9798350360912

ISBN: (纸本)9798350360929

Ethylene-vinyl acetate copolymer (EVA) is a significant material for sealing and insulation with crucial applications in the nuclear power industry. As the service life increases, aerobic conditions induce an oxidation reaction that accelerates the aging process of material properties. This leads to a gradual deterioration of physical and mechanical properties, which can result in safety hazards. [1–3] Adding antioxidants to the material can effectively slow down the aging process. Terahertz spectroscopy is a noncontact, fast, and non-destructive testing method with great potential for simultaneous material ageing testing. [4 - 5] Thus, EVA samples with varying concentrations of 1010 antioxidant were analyzed using terahertz time-domain and dielectric constant spectra of the samples. The results indicated that the refractive indices and dielectric constants of the EVA samples decreased within the tested frequency range as the antioxidant content increased. Furthermore, the absorption coefficient, dispersion coefficient, and dielectric loss were used as criteria for evaluation. Various methods were used to construct a multi-input model for antioxidant content, including Random Forest (RF), Multilayer Perceptron (MLP), Partial Least Squares (PLSR), and eXtreme Gradient Boosting (XGboost). Classification and discrimination results were obtained through visual processing. The RF and xgboost models achieved the highest accuracy rate. Also, molecular dynamics simulations were used to model the effect of functional group content in 1010 antioxidants, ranging from $0.5-2.5 \mathrm{THz}$, on the dielectric constant. The purpose was to explain the changes in physical parameters caused by the antioxidant content. Our work contributes to the fusion of terahertz time-domain spectroscopy and artificial intelligence techniques, this paves a way to fast and nondestructive measurements of insulating materials.

关键词： Antioxidants Radio frequency Spectroscopy Visualization Dielectric constant Power industry Velocity measurement

MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING

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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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