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Boosting Robustness in Automatic Modulation Recognition for Wireless Communications

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1635-1648页

作者： Zhao, Yuhang Wang, Yajie Zhang, Chuan Li, Chunhai Xiong, Zehui Zhu, Liehuang Niyato, Dusit Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin541004 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

In the radio frequency field, deep neural networks have been widely used for automatic modulation recognition tasks due to their superior accuracy. However, it has been shown that these models are susceptible to adversarial examples, which are the kinds of carefully crafted perturbations that can lead to model misclassification and raise security issues in applications. To solve this problem, we propose an Ultra-Fusion Adversarial Training method, which combines adversarial training and ensemble learning to enable the model robustness to withstand different attack strengths. We explore the number and distribution of ensembled attacks and introduce a Fermi-function-like distribution to optimally balance the performance of different attack strengths. Additionally, we investigate the effect of the signal-to-noise ratio (SNR) interval on the model accuracy and robustness, suggesting the effective SNR interval for training. Considering the demand for practical application scenarios of modulation recognition, we propose a comprehensive robustness metric based on weighted integral to evaluate the robustness of the trained models. Numerical experiments demonstrate that our method improves the model's robustness by 31.89% against white-box attacks, and achieves up to an 80.54% improvement in black-box scenarios. These results show that our method has the ability to resiliently resist potential attacks of various strengths and can be applied to spectrum application scenarios with high-security requirements. © 2015 IEEE.

关键词： Signal modulation

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network

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science China(Information sciences) 2023年第1期66卷 324-325页

作者： Jie WU Runmin CONG Leyuan FANG Chunle GUO Bob ZHANG Pedram GHAMISI College of Electrical and Information Engineering Hunan University Institute of Information Science Beijing Jiaotong University College of Computer Science Nankai University Department of Computer and Information Science University of Macau Helmholtz-Zentrum Dresden-Rossendorf Helmholtz Institute Freiberg for Resource Technology Institute of Advanced Research in Artificial Intelligence (IARAI)

Dear editor,Image super-resolution(SR) uses image processing methods to increase the resolution of the image without increasing any hardware cost. Remote sensing images with high spatial resolution contain rich imagery information, which can be utilized for object detection, building segmentation, building extraction, change detection, and so on.

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Collaborative Search With Knowledge Sharing And Summarization 4

Collaborative Search With Knowledge Sharing And Summarizatio...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohan, Venkateshwaran Sivaganesan, D. Balasubramaniam, S. Vikhas, S.G. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Dr Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Pollachi India Psg College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798331540364

A collaborative search engine is used for a group of users who want to search information together for various group activities. This will be time saving as the users can split the work between them. Summarization is the process of condensing a document into a shorter version which will contain the important details about the document. This will be useful to quickly get the main ideas without having to go through the entire document. Our main aim is to combine collaborative search with document summarization so that it will be helpful when users in a group want to quickly read the summary of the documents whenever required. In the collaborative search, the results will be re-ranked based on the user's interaction with the system, so that feedback driven ranking of results is attained and the users in a session will be able to view pages that were even ranked by their co-mates. This re-raking process is done by obtaining the user's interest on a particular page using decision logic and updating it in the priority queue. User's meta-data like page size, click-through rate, time spent and scrolling speed of the document will be calculated and these will be used as parameters for the decision logic to find the interest level of the user. Extractive summarization is implemented with the concept of Maximum Independent Set. The raw text document is first converted into a graph having its sentences as nodes and the edge weight is the similar instances between sentences. Then graph MIS is identified and removed after which the top-n sentences are fetched upon its eigenvector centrality value and the summary is generated. © 2024 IEEE.

关键词： Computation theory

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in Engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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A Novel Collective User Web Behavior Simulation Method

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computers, Materials & Continua 2021年第3期66卷 2539-2553页

作者： Hongri Liu Xu Zhang Jingjing Li Bailing Wang College of Computer Science&Technology Harbin Institute of Technology at WeihaiWeihai264209China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen518000China Research Institute of Cyberspace Security Harbin Institute of TechnologyHarbin150001China

A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber *** studies almost focus on individual web behavior analysis and prediction,which cannot simulate human dynamics that widely exist in large-scale users’*** address these issues,we propose a novel collective user web behavior simulation method,in which an algorithm for constructing a connected virtual social network is proposed,and then a collective user web behavior simulation algorithm is designed on the virtual social *** the simulation method,a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social *** specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the *** experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.

关键词： Web behavior simulation virtual social network human dynamics power-law distribution

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Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Defending against data reconstruction attacks in federated learning: an information theory approach 24

Defending against data reconstruction attacks in federated l...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Qi Tan Qi Li Yi Zhao Zhuotao Liu Xiaobing Guo Ke Xu Department of Computer Science and Technology Tsinghua University Institute for Network Science and Cyberspace Tsinghua University School of Cyberspace Science and Technology Beijing Institute of Technology Lenovo Research

ISBN: (纸本)9781939133441

Federated Learning (FL) trains a black-box and high-dimensional model among different clients by exchanging parameters instead of direct data sharing, which mitigates the privacy leak incurred by machine learning. However, FL still suffers from membership inference attacks (MIA) or data reconstruction attacks (DRA). In particular, an attacker can extract the information from local datasets by constructing DRA, which cannot be effectively throttled by existing techniques, e.g., Differential Privacy (DP).In this paper, we aim to ensure a strong privacy guarantee for FL under DRA. We prove that reconstruction errors under DRA are constrained by the information acquired by an attacker, which means that constraining the transmitted information can effectively throttle DRA. To quantify the information leakage incurred by FL, we establish a channel model, which depends on the upper bound of joint mutual information between the local dataset and multiple transmitted parameters. Moreover, the channel model indicates that the transmitted information can be constrained through data space operation, which can improve training efficiency and the model accuracy under constrained information. According to the channel model, we propose algorithms to constrain the information transmitted in a single round of local training. With a limited number of training rounds, the algorithms ensure that the total amount of transmitted information is limited. Furthermore, our channel model can be applied to various privacy-enhancing techniques (such as DP) to enhance privacy guarantees against DRA. Extensive experiments with real-world datasets validate the effectiveness of our methods.

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