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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Jin, Shi-Mei Wang, Yi-Han Li, Yongzhen Yanbian University Information Technology Education Yanji133000 China Yanbian University Network Communication and Information Security Lab. Dept. of Computer Science and Technology Yanji133000 China

ISBN: (纸本)9781510663374

With the rapid development and upgrade of the social network and IT, information security has become increasingly significant. Hash function has become a research key-point in the field of information security. To meeting the requirements of various circumstances, this dissertation puts forward Multilevel security Hash Function (MSHF) and conduct a comparison experiment between MSHF and traditional methods to generate Merkle trees. The experimental results show that the Merkle tree generated based on MSHF has smaller depth, shorter path length, and shorter time used to find the wrong data. © 2023 SPIE.

关键词： Hash functions

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Speed Master: Quick or Slow Play to Attack Speaker Recognition 39

Speed Master: Quick or Slow Play to Attack Speaker Recogniti...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Zhe Zhang, Wenjie Ren, Ying Kang, Xiangui Yan, Diqun Ma, Bin Wang, Shiqi Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks pose a significant threat during the model’s training phase. Attackers craft pre-defined triggers to break deep neural networks, ensuring the model accurately classifies clean samples during inference yet erroneously classifies samples added with these triggers. Recent studies have shown that speaker recognition systems trained on large-scale data are susceptible to backdoor attacks. Existing attackers employ unnoticed ambient sounds as triggers. However, these sounds are not inherently part of the training samples themselves. In essence, triggers can be designed to maintain an intrinsic connection with the original speech to enhance stealthiness. Our paper presents a novel attack methodology named Speed Master, which undermines deep neural networks by manipulating the speed of speech samples. Specifically, we execute poison-only backdoor attacks using speed or tempo adjustment. Changes in speech rate have become a common occurrence, as seen on platforms that allow users to adjust playback speed. In real-world scenarios, people naturally adjust their speaking rate depending on the context. As a result, changes in a speaker’s speech rate are typically perceived as normal and are unlikely to raise suspicion. Furthermore, detecting such subtle adjustments becomes challenging for users without reference speech. Our comprehensive experiments demonstrate that Speed Master can achieve an ASR over 99% in the digital domain, with only a 0.6% poisoning rate. Additionally, we validate the feasibility of Speed Master in the real world and its resistance to typical defensive measures. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE)

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computer Systems Science & Engineering 2023年第9期46卷 3261-3284页

作者： Lelisa Adeba Jilcha Deuk-Hun Kim Julian Jang-Jaccard Jin Kwak ISAA Lab. Department of AI Convergence NetworkAjou UniversitySuwon16499Korea ISAA Lab. Institute for Information and CommunicationAjou UniversitySuwon16499Korea Department of Computer Science and Information Technology Massey UniversityAuckland0745New Zealand Department of Cyber Security Ajou UniversitySuwon16499Korea

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or *** growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge *** such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection *** address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned *** collectively refer to the convergence of different technology sectors as the internet of blended *** proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational *** extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection ***,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational *** proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.

关键词： network intrusion detection anomaly detection TON_IoT dataset smart grid smart city smart factory digital healthcare autoencoder variational autoencoder LSTM convolutional variational autoencoder ensemble learning

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Secure fusion of encrypted remote sensing images based on Brovey

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Science China(information Sciences) 2021年第2期64卷 243-245页

作者： Junzhi YANG Guohua CHENG Meng SHEN School of Information and Electronics Beijing Institute of Technology School of Computer Science Beijing Institute of Technology Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security)

Dear editor,In the field of image fusion, panchromatic images captured by the Satellite Pour l'Observation de la Terre(SPOT) [1]have high spatial resolutions, whereas their spectral resolutions are relatively low. On the other hand, multispectral images have high spectral resolutions; however, their spatial resolutions are low. Because the two types of images captured by satellites cannot meet the demand [2], image fusion is generally necessary in practice.

关键词： information Systems and Communication Service

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A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Wang, YuBo Zhang, TaoYi Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyber Security China Hebei Prov. Eng. Res. Center for Supply Chain Big Data Analytics Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network. © 2023 IEEE.

关键词： Reinforcement learning

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Taxi Station Location Model Based on Spatio-Temporal Demand Cube

Taxi Station Location Model Based on Spatio-Temporal Demand ...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Gao, LuLu Li, XiaoNan Wei, ZhiCheng Key Lab of Network and Information Security Hebei Province Shijiazhuang China College of Computer and Cyber Security Hebei Province Shijiazhuang China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

With the widespread use of mobile apps, empty taxis can easily receive new orders without having to roam around. Therefore, it is necessary to plan taxi stations where empty taxis can park. In the location selection problem, the spatial and temporal distribution of passenger demand needs to be considered to ensure that the selected location can meet the actual needs of passengers. In this paper, a Spatio-Temporal Cube Demand Coverage Model (STC-DCM) is proposed for taxi station location selection. Firstly, a spatiotemporal demand cube is constructed to obtain fine-grained user demands based on time and space, and genetic algorithm is used to maximize the demand coverage rate. Then, the final location selection results are obtained using the fusion module and adjustment optimization module, where the fusion module integrates multiple location selection solutions using ensemble learning method. Experimental results show that our proposed model can better cover dynamic passenger demand compared to traditional models. © 2023 IEEE.

关键词： Genetic algorithms

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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An Efficient Unsupervised Domain Adaptation Deep Learning Model for Unknown Malware Detection 1

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4th EAI International Conference on security and Privacy in New Computing Environments, SPNCE 2021

作者： Wang, Fangwei Chai, Guofang Li, Qingru Wang, Changguang Key Lab of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyberspace Security Hebei Normal University Shijiazhuang050024 China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

Emerging malware and zero-day vulnerabilities present new challenges to malware detection. Currently, numerous proposed malware detection approaches are based on supervised learning. However, these methods rely on a large amount of labeled data, which is usually difficult to obtain. Moreover, since the newly emerging malware has a different data distribution from the original training samples, the detection performance of the model will degrade when facing new malware. To solve the problems mentioned above, this paper proposes an unsupervised domain adaptation-based malware detection method to align the joint distribution of known malware and unknown malware. First, the distribution divergence between the source and target domain is minimized by adversarial learning to learn shared feature representations. Second, to further obtain semantic information of unlabeled target domain data, this paper reduces the class-level distribution divergence by aligning the class centers of labeled source data and pseudo-labeled target data. To improve the ability of the model for extracting feature information, this paper mainly uses a residual network with a self-attention mechanism as a pre-trained model. Extensive experiments are conducted on two datasets. The experimental results illustrate that the proposed method outperforms the state-of-art detection methods with an accuracy of 95.63% and a recall of 95.30% in detecting unknown malware. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Semantics

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An IoMT based Ensemble Classification Framework to Predict Treatment Response in Hepatitis C Patients

An IoMT based Ensemble Classification Framework to Predict T...

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2022 International Conference on Business Analytics for Technology and security, ICBATS 2022

作者： Ghazal, Taher M. Abbas, Sagheer Ahmad, Munir Aftab, Shabib Faculty of Information Science and Technology Ukm Center for Cyber Security Network and Communication Technology Lab Malaysia NCBAandE School of Computer Science Lahore Pakistan

ISBN: (纸本)9781665409209

Hepatitis C is considered a deadly disease as mortality rate in the patients suffering from this disease is very high, if not properly treated. This research proposes an IOMT based ensemble classification framework for the prediction of treatment response of a drug: 'L-Ornithine-L-Aspartate' (LOLA) in hepatitis c patients. The treatment with this drug is called LOLA therapy which is significant for the patients to recover from the effects of hepatitis c disease. To implement the proposed framework, we used the medical data of hepatitis c patients who are being treated with LOLA therapy. The proposed framework integrates the predictive accuracy of two supervised machine learning techniques: Support Vector Machine (SVM) and Decision Tree (DT) by using Voting ensemble technique. The results reflect that the proposed ensemble framework performed well as compared to other published techniques on the prediction of treatment response for hepatitis c disease. © 2022 IEEE.

关键词： Diseases

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Uplink Control information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： information management

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