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Demystifying the Transferability of Adversarial Attacks in computer networks

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arXiv 2021年

作者： Nowroozi, Ehsan Mekdad, Yassine Berenjestanaki, Mohammad Hajian Conti, Mauro Fergougui, Abdeslam E.L. Sabanci University Istanbul Turkey Sabanci University Istanbul34956 Turkey University of Padua Padua35121 Italy The Laboratory of Computer Networks and Systems Moulay Ismail University of Meknes Zitoune Meknes11201 Morocco The Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States The Department of Mathematics University of Padua Padua35121 Italy

Convolutional Neural networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (LBFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three wellknown datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the IFGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture. © 2021, CC BY.

关键词： Gradient methods

人类泛基因组参考图谱发布开启基因组学领域的新篇章

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Science Bulletin 2023年第14期68卷 1484-1487页

作者：王博党宁馨杨晓飞徐书华叶凯 School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterCenter for Evolutionary BiologySchool of Life SciencesFudan UniversityShanghai 200032China Ministry of Education Key Laboratory of Contemporary Anthropology Collaborative Innovation Center for Genetics and DevelopmentFudan UniversityShanghai 200032China Department of Liver Surgery and Transplantation Liver Cancer InstituteZhongshan HospitalFudan UniversityShanghai 200032China School of Life Science and Technology Shanghai Tech UniversityShanghai 201210China

The significance of DNA sequencing in advancing medicalresearch,improving healthcare practices,and enhancing ourunderstanding of human evolution cannot be *** yearmarks the 20th anniversary of the completion of the Human Genome Project(HGP)in 2003,with the draft human genome reference published in 2001[1,2].However,even with the latesthuman reference genome,GRCh38,there remain gaps totalingapprox.150 Mb,involving complex sequence in the pericentromeric and subtelomeric regions,as well as recent segmentalduplications and both ampliconic gene and ribosomal DNA(rDNA)arrays[3].A groundbreaking achievement was recently accomplished by the Telomere-to-Telomere(T2T)consortium whosuccessfully closed the remaining gaps and completed the firstend-to-end assembly of a human haploid genome,known as T2TCHM13[3].

关键词：基因组学 breaking accomplished

Finite-Blocklength Multi-Antenna Covert Communication Aided By A UAV Relay

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Finite-Blocklength Multi-Antenna Covert Communication Aided ...

2021 IEEE Global Communications Conference (GLOBECOM)

作者： Xinying Chen Min Sheng Nan Zhao Wei Xu Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland State Key Laboratory of Integrated Services Networks Xidian University Xi'an China National Mobile Communications Research Lab. Southeast University Nanjing Jiangsu China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781728181059

We propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission rate from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signaling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

关键词： Uncertainty Transmitters Conferences Receivers Autonomous aerial vehicles Entropy Global communication

Frequency synchronization for uplink massive MIMO with adaptive MUI suppression in angle-domain

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arXiv 2018年

作者： Ge, Yinghao Zhang, Weile Gao, Feifei Li, Geoffrey Ye MOE Key Lab for Intelligent Networks and Network Security School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) multiuser network, which is applicable for users with either separate or overlapped angle-of-arrival (AoA) regions. For each user, we first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-filter (MF) beamformers pointing to the AoAs of the interested user. Then, the adaptive beamformer can be acquired by appropriately designing the ADAF vectors. Such beamformer can achieve two-stage adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing the MUI from non-overlapping users by ACM in the first stage and substantially mitigating the MUI from adjacent overlapping users by ADAF vector in the second stage. For both separate and mutually overlapping users, the carrier frequency offset (CFO) estimation and subsequent data detection can be performed independently for each user, which significantly reduces computational complexity. Moreover, ADAF is rather robust to imperfect AoA knowledge and side cluster of scatterers, making itself promising for multiuser uplink transmission. We also analyze the performance of CFO estimation and obtain an approximated closed-form expression of mean-squared error (MSE). The effectiveness of the proposed method and its superiority over the existing methods are demonstrated by numerical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Adaptive filters

Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT Communications

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Explainable Asymmetric Auto-Encoder for End-to-End Learning ...

Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Roya Khanzadeh Stefan Angerbauer Jorge Torres Gomez Pit Hofmann Falko Dressler Frank H. P. Fitzek Andreas Springer Werner Haselmayr Institute for Communications Engineering and RF-Systems JKU LIT SAL eSPML Lab Johannes Kepler University Linz Linz Austria School of Electrical Engineering and Computer Science TU Berlin Berlin Germany Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Dresden Germany Institute for Communications Engineering and RF-Systems Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular Communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.

关键词： Training Transmitters Precoding Symbols Molecular communication Artificial neural networks Receivers

Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC networks

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arXiv 2023年

作者： Wang, Shao-Di Wang, Hui-Ming Li, Zhetao Leung, Victor C.M. The School of Information and Communication Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China The Key Laboratory of Hunnan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent Network School of Computer Science Xiangtan University Xiangtan411105 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

Securing face liveness detection using unforgeable lip motion patterns

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arXiv 2021年

作者： Zhou, Man Wang, Qian Li, Qi Jiang, Peipei Yang, Jingxiao Shen, Chao Wang, Cong Ding, Shouhong The School of Cyber Science and Engineering Wuhan University Wuhan430072 China The Institute for Network Sciences and Cyberspace Tsinghua University Beijing100084 China The School of Computer Science Wuhan University Wuhan430072 China The MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China The School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an710049 China The Department of Computer Science City University of Hong Kong Hong Kong Youtu Lab Tencent Shanghai China

Face authentication usually utilizes deep learning models to verify users with high recognition accuracy. However, face authentication systems are vulnerable to various attacks that cheat the models by manipulating the digital counterparts of human faces. So far, lots of liveness detection schemes have been developed to prevent such attacks. Unfortunately, the attacker can still bypass these schemes by constructing wide-ranging sophisticated attacks. We study the security of existing face authentication services (e.g., Microsoft, Amazon, and Face++) and typical liveness detection approaches. Particularly, we develop a new type of attack, i.e., the low-cost 3D projection attack that projects manipulated face videos on a 3D face model, which can easily evade these face authentication services and liveness detection approaches. To this end, we propose FaceLip, a novel liveness detection scheme for face authentication, which utilizes unforgeable lip motion patterns built upon well-designed acoustic signals to enable a strong security guarantee. The unique lip motion patterns for each user are unforgeable because FaceLip verifies the patterns by capturing and analyzing the acoustic signals that are dynamically generated according to random challenges, which ensures that our signals for liveness detection cannot be manipulated. Specially, we develop robust algorithms for FaceLip to eliminate the impact of noisy signals in the environment and thus can accurately infer the lip motions at larger distances. We prototype FaceLip on off-the-shelf smartphones and conduct extensive experiments under different settings. Our evaluation with 44 participants validates the effectiveness and robustness of FaceLip. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework

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VELO: A Vector Database-Assisted Cloud-Edge Collaborative LL...

IEEE International Conference on Web Services (ICWS)

作者： Zhi Yao Zhiqing Tang Jiong Lou Ping Shen Weijia Jia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China School of Artificial Intelligence Beijing Normal University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs.

关键词： Training Costs Databases Web services Collaboration Quality of service Feature extraction Vectors Delays Optimization

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic networks

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GCN-GAN: A Non-linear Temporal Link Prediction Model for Wei...

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kai Lei Meng Qin Bo Bai Gong Zhang Min Yang PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China ICNLAB School of Electronics and Computer Engineering (SECE) Peking University Shenzhen China Future Network Theory Lab 2012 Labs Huawei Technologies Co. Ltd Hong Kong China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

关键词： Predictive models Hidden Markov models Task analysis Topology Network topology Generative adversarial networks Gallium nitride