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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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An Adaptive DoH Encrypted Tunnel Detection Method Based on Contrastive Learning

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IEEE Internet of Things Journal 2025年

作者： Tong, Jiacheng Zhao, Yilin Jin, Chongju Chen, Wei Zhang, Yiting Wu, Lifa Nanjing University of Posts and Telecommunications School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

The percentage of encrypted network traffic has constantly increased as network security has been continuously improved. Attackers can, however, utilize encrypted DNS over HTTPS (DoH) to conceal their malicious traffic, which makes it more difficult to identify malicious tunnels. To address this issue, we first examine the encryption features of DoH tunnel traffic. Due to the incapability of current detection techniques to properly fuse traffic attributes, a fusion learning-based method is proposed to detect DoH encrypted tunnel traffic. At the same time, we discover that the DoH traffic samples may exhibit concept drift. As a result, we present a concept drift detection approach based on a contrastive sparse autoencoder. In addition to the above method, a model retraining strategy is also suggested to improve the model’s capacity to identify new DoH encrypted tunnel traffic while reducing its reliance on expert label data. This strategy involves incrementally training the model using as few samples as possible. Experiments demonstrate that the proposed method can significantly enhance detection performance. When 7% of drift samples are used during incremental training, the detection accuracy of the model recovers from 74.02% to 99.96%. © 2014 IEEE.

关键词： Contrastive Learning

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ALEDAR: An Attentions-based Encoder-Decoder and Autoregressive model for workload Forecasting of Cloud data Center 25

ALEDAR: An Attentions-based Encoder-Decoder and Autoregressi...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Sun, Wei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing China

ISBN: (纸本)9781665405270

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated architecture design and provides more diverse computing services. The traditional load forecasting models need too much manual intervention to set up the parameters, meanwhile, the emerging neural network methods are not sensitive to the prediction scale and cannot capture the long-term associations between input features. To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning methods, to analyze the linear and nonlinear characteristics of the load sequence over time in a multi-cloud data center environment. ALEDAR uses a dual attention-based Encoder-Decoder architecture to extract the relationships between historical workload data, the design can also avoid the deterioration of prediction effect caused by long-range data scale, the output layer is composed of a three-layer perceptron. Moreover, ALEDAR employs an autoregressive module to capture the linear trend of the load sequence and eliminate scale insensitivity of input and output data. The experimental results show that our approach is adaptive and can improve the performance of host workload prediction in both single cloud data center and multi-cloud data center environments. We evaluate the performance of the proposed algorithm on real-world data sets and observe consistent improvement of 9.7% - 34.2% over the state-of-the-art baselines. © 2022 IEEE.

关键词： Forecasting

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification 25

A New Semi-supervised Approach for Network Encrypted Traffic...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Lin, Kunda Xu, Xiaolong Jiang, Yu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Encrypted network traffic classification is an essential task in modern communications, which is used in a wide range of applications, such as network resource allocation, QoS (Quality of Service), malicious detection, etc. With the continuous evolution of network technology, approaches used to classify network encrypted traffic have become increasingly complex, and model training relies heavily on a large amount of labeled data. However, the acquisition of correct and massive labeled data of network traffic in the real environment remains a major challenge in this field. On the opposite, unlabeled traffic is extremely easy to obtain in a network. Therefore, the effective use of unlabeled data is of great significance to the development of modern communications. In this paper, we propose the Sauce model, which can effectively use unlabeled information to obtain high-quality clustering space. It is composed of Aux (auxiliary network) and AE (auto encoder), where Aux is designed for collaborative training with AE, affecting the distribution of samples in the latent space generated by AE. Sauce uses t-SNE (t- distributed stochastic neighbor embedding) to perform secondary dimensionality reduction on the latent code, and then uses a clustering algorithm for cluster analysis. Besides, sauce applies the self-training technique, replacing the real labeled data with pseudo-labeled data to reduce the reliance on labeled data and improve the utilization of unannotated data. We conduct experiments on two real network datasets. The experiments show that Sauce can achieve clustering accuracies of 98.4% (VPN dataset) and 98.0% (TOR dataset), outperforming other unsupervised or semi-supervised learning methods. © 2022 IEEE.

关键词： Cluster analysis

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Four-dimensional Floquet topological insulator with an emergent second Chern number

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Physical Review B 2024年第12期109卷 125303-125303页

作者： Zheng-Rong Liu Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

Floquet topological insulators have been widely investigated in lower-dimensional systems. However, Floquet topological insulators induced by time-periodic driving in higher-dimensional systems remain unexplored. In this work, we study the effects of time-periodic driving in a four-dimensional (4D) normal insulator, focusing on topological phase transitions at the resonant quasienergy gap. We consider two types of time-periodic driving, including a time-periodic onsite potential and a time-periodic vector potential. We reveal that both types of time-periodic driving can transform the 4D normal insulator into a 4D Floquet topological insulator characterized by an emergent second Chern number. Moreover, it is found that the topological phase of the 4D system can be modulated by tuning the strength and frequency of the time-periodic driving. Our work will be helpful for the future investigation of Floquet topological insulators in higher dimensions.

关键词： Topological insulators Topological phase transition Topological phases of matter Floquet systems

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BadActs: A Universal Backdoor Defense in the Activation Space

arXiv

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arXiv 2024年

作者： Yi, Biao Chen, Sishuo Li, Yiming Li, Tong Zhang, Baolei Liu, Zheli College of Cyber Science Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University China Center for Data Science Peking University China Nanyang Technological University Singapore

Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as a promising defense mechanism, aiming to eliminate backdoor triggers while preserving the integrity of the clean content in the samples. However, existing approaches have been predominantly focused on the word space, which are ineffective against featurespace triggers and significantly impair performance on clean data. To address this, we introduce a universal backdoor defense that purifies backdoor samples in the activation space by drawing abnormal activations towards optimized minimum clean activation distribution intervals. The advantages of our approach are twofold: (1) By operating in the activation space, our method captures from surface-level information like words to higher-level semantic concepts such as syntax, thus counteracting diverse triggers;(2) the fine-grained continuous nature of the activation space allows for more precise preservation of clean content while removing triggers. Furthermore, we propose a detection module based on statistical information of abnormal activations, to achieve a better trade-off between clean accuracy and defending performance. Extensive experiments on diverse datasets and against diverse attacks (including syntax and style attacks) demonstrate that our defense achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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In-depth analysis of core-shell filaments in nonvolatile NbO_(x) memristive device as an artificial synapse for multifunctional bionic applications

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Science China(Technological Sciences) 2023年第12期66卷 3596-3603页

作者： JIANG Bei KE ShanWu TAO ZiPei JIN YaoYao MENG JiaHao SONG XingJuan XIAO YongYue JIANG Li WANG JinZhao CHEN Yong LI YueBin WEN Xin YE Cong School of Microelectronics Key Laboratory of Intelligent Sensing System and SecurityMinistry of EducationHubei UniversityWuhan 430062China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and TechnologyWuhan 430074China School of Science Hubei University of TechnologyWuhan 430068China Faculty of Chemical Technology and Engineering West Pomeranian University of Technology in SzczecinSzczecin 71-065Poland

As a key building block of the biological cortex,synapses are powerful information processing units that enable highly complex nonlinear *** realization of artificial synapses with similar capabilities has important implications for building intelligent,neuromorphic ***,we demonstrate an artificial synapse based on NbO_(x) nonvolatile memristor to mimic multifunctional bionic applications such as nociceptor and associative *** experimental characterization with COMSOL simulation,the traditional resistance switching characteristics,which are the decisive factor for the synapse properties are in-depth *** can be proposed that the I-V characteristics of Pt/NbO_(x)/TiN memristor are governed by core-shell filaments consisting of the shell region of sub-stoichiometric Nb_(2)O_(5-δ)and the core of NbO_(2).On the basis of the core-shell filament model,it can be reasonably explained that Ohmic conduction and Poole-Frenkel conduction take turns to dominate the current flowing in the memristive device,leading to the zigzag evolution of current during the operation process of NbO_(x)-based *** simulations of synaptic plasticity,including long-term potentiation/depression(LTP/LTD),paired-pulse facilitation(PPF),and spike-timing-dependent plasticity(STDP),exhibiting that the NbO_(x) can be utilized for an artificial ***,bionic functions such as hyperalgesia and allodynia of a nociceptor and a series of associative learning behaviors in Pavlovian dog experiment are mimicked,illustrating that the Pt/NbO_(x)/TiN have great potential for highly simplified artificial neural network applications.

关键词： memristor core-shell filament synaptic plasticity nociceptor associative learning

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Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781643684369

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model's uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. © 2023 The Authors.

关键词： Reinforcement learning

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