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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Huang, Zhendong Fan, Xuyan Li, Zhiqi Zhao, Changzhi Chen, Guangxuan Liu, Yuling People's Public Security University of China Department of Information and Network Security Beijing China Ruian Public Security Bureau Wenzhou China Zhejiang Police College Department of Computer and Information Security Hangzhou China The National Information Center of China Customs Beijing China Beijing State Grid Siji Network Security Technology Limited Company Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350333664

Web API networks comprise a multitude of API services, and compared to traditional network environments, they possess unique security requirements and face distinct threats. This article delves into the security characteristics of Web API networks and the particular threats they encounter, establishing evaluation criteria for anomaly detection models tailored to Web API networks and their associated scenarios. Through an integrated assessment of numerous studies, we meticulously compare and analyze various models in terms of false positive rates, robustness, detection latency, and their adaptability and detection capabilities. The findings reveal that while many models excel in detecting singular attacks within the Web API network context, their scope of attack detection is often constrained, making it challenging to comprehensively address the myriad threats Web API networks confront. Moreover, several unresolved challenges persist in the research, such as the scarcity of real-world datasets, inadequate model generalization capabilities, and subpar robustness. © 2023 IEEE.

关键词： anomaly detection API calls model evaluation scenario application Web API network

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Data Analytics for Identifying and Reducing Hospital Readmissions

Data Analytics for Identifying and Reducing Hospital Readmis...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Vidhya K Srinu N Ashok D Arun Sekar Rajasekaran Ashok Kumar S Rajasekaran U Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of AIMLDS QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Nathakadaiyur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Data analytics examines healthcare data to identify patterns and personalize care plans ultimately by reducing hospital readmissions. Readmissions after discharge increase healthcare costs, lower quality, and dissatisfied patients. To overcome these issues, a novel Data analytics approach has been proposed to reduce hospital readmissions. This inquiry begins with a complete dataset of clinical data, patient demographics, and Electronic Health Records (EHRs). Initially, the data gathered from the clinical data, patient demographics, and electronic health records are fed to the preprocessing module for transforming raw healthcare data into a structured form for analysis. After preprocessing, an Exploratory Data Analysis (EDA) is performed which simplifies the initial insights and hospital readmission risk factor identification. The most essential features are selected from the analyzed outcomes of EDA for readmission prediction. After feature selection, the development of hospital readmission models uses three data analytics methods. At first, the Machine Learning (ML) based methods predict the readmission status and the sequential relationships and anticipate readmission risk over time are captured using a neural network. Finally, the patient groups with comparable readmission trends are identified through hierarchical clustering. After data analytics, those outcomes are fed to the real-time patient monitoring system to reveal readmission factors such as discharge planning and timely drug management. The proposed method is validated by using healthcare datasets. In comparison, the proposed method achieves an accuracy of 97% whereas, the predictive modeling, risk factor, care process, and alert method achieves 85%, 78%, 90%, and 83% respectively.

关键词： Analytical models Data analysis Costs Accuracy Hospitals Machine learning Predictive models Feature extraction Data models Electronic medical records

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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A surrogate assisted evolutionary multitasking optimization algorithm[Formula presented]

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Applied Soft Computing 2023年 132卷

作者： Yang, Shangqi Qi, Yutao Yang, Rui Ma, Xiaoliang Zhang, Haibin School of Computer Science and Technology Xidian University Xi'an China School of Cyber Security Xidian University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Evolutionary algorithms (EAs) have been applied with strong abilities to solve a wide range of applications, but it can solve one problem at a time. To improve efficiency, an emerging research paradigm in the field of evolutionary computation, Evolutionary multi-tasking (EMT) was proposed. EMT solves multiple optimization tasks simultaneously. The effectiveness of EMT is to improve the solutions for each task via inter-task knowledge transfer. Multifactorial evolutionary algorithms (MFEAs) is the first algorithm proposed to solve multi-task optimization problems. However, it tends to suffer from the issue of negative knowledge transfer. To address this issue and improve the performance of MFEA, we propose to construct a surrogate model as a helper task is optimized and target task simultaneously in MFEA. According to the proposed method, the surrogate model is a related task for each corresponding target task to enhance positive inter-task knowledge transfer. Besides, the surrogate model can reduce the number of local optima and has a simple structure. Experiments are conducted on benchmarks and real-world reservoir flood generation power problems to examine the performance of the proposed algorithm. Comparative experiments on several widely used test problems demonstrated that surrogate models as helper tasks enable significantly improve the performance of MFEA. © 2022

关键词： Evolutionary algorithms

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Inaudible Sound Covert Channel with Anti-Jamming Capability: Attacks vs. Countermeasure

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Journal of Electronic Science and technology 2022年第4期20卷 396-415页

作者： Xiao-Hang Wang Shou-Bin Li Ying-Tao Jiang Amit Kumar Singh Bi-Yun Ma Le-Tian Huang Mei Yang Fen Guo School of Cyber Science and Technology Zhejiang UniversityHangzhou 310012 ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310012 School of Software Engineering South China University of TechnologyGuangzhou 510006 Department of Electrical and Computer Engineering University of NevadaLas Vegas 89154 School of Computer Science and Electronic Engineering University of EssexColchester CO43SQ School of Electronics and Information Engineering South China University of TechnologyGuangzhou 510640 School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054

When an inaudible sound covert channel(ISCC)attack is launched inside a computer system,sensitive data are converted to inaudible sound waves and then *** receiver at the other end picks up the sound signal,from which the original sensitive data can be *** a forceful countermeasure against the ISCC attack,strong noise can be used to jam the channel and literally shut down any possible sound data *** this paper,enhanced ISCC is proposed,whose transmission frequency can be dynamically ***,if the transmitter detects that the covert channel is being jammed,the transmitter and receiver both will switch to another available frequency and re-establish their communications,following the proposed communications *** results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise ***,a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound *** the proposed countermeasure,the bit error rate(BER)of the data communications over enhanced ISCC soars to more than 48%,essentially shutting down the data transmission,and thus neutralizing the security threat.

关键词： Countermeasure inaudible sound covert channel(ISCC)

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Patterns of Terror: A Comparative Predictive Model 5

Patterns of Terror: A Comparative Predictive Model

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Olayinka, Tosin Comfort Adeniyi, Lawal Oghorodi, Duke Anazia, Kizito Eluemunor Onyijen, Ojei Harrison Nwankwo, Chukwuemeka Pascal Olayinka, Akinola Samson Nwankwo, Wilson Ukaoha, Kingsley Wellspring University Department of Computer Science Benin City Nigeria National Open University of Nigeria Department of Information Technology Abuja Nigeria Delta State University of Science and Technology Department of Computer Science Ozoro Nigeria Delta State University of Science & Technology Department of Information Systems & Technology Ozoro Nigeria Glorious Vision University Department of Computer Science Ogwa Nigeria Nigerian British University Department of Information Technology Asa Nigeria State University Department of Physics Edo Uzairue Nigeria Delta State University of Science & Technology Department of Cyber Security Ozoro Nigeria Wigwe University Department of Computer Science Isiokpo Nigeria

ISBN: (纸本)9798331542559

This study aims to develop an intelligent model for counter-terrorism operations using a dataset of 50,064 entries from the Armed Conflict Location and Event Data (ACLED) covering terrorism activities from 1997 to 2022. The entropy-based feature engineering approach, utilizing information gain, was employed for its robustness to noisy data and ability to prioritize the most predictive features. Three machine learning classifiers - REPtrree, AdaBoost, and Meta-Stacking - were compared, with REPtree achieving the highest accuracy of 96.81% in predicting terrorist incidents. This study contributes to enhancing national security by leveraging machine learning techniques to improve counter-terrorism efforts. © 2024 IEEE.

关键词： Information Gain Predictive Analytics security Terrorism

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The Intersection of AI and IoT for Clinical Digitalization in Healthcare Domain

The Intersection of AI and IoT for Clinical Digitalization i...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Kavitha, K. Sangeethapriya, R. Malathi, V. Mylsami, T. Hazra, Riyanka Mohankumar, R. Mohan Babu University School of Computing Department of AI and ML Tirupati India Sona College of Technology Department of Information Technology Tamil Nadu Salem India Dr. N.G.P Arts and Science College Department of Computer Science With Cyber Security Tamil Nadu Coimbatore India Dr. G.R. Damodaran College of Science Department of Computer Science Tamil Nadu Coimbatore India Brainware University Department of Computational Science West Bengal Barasat India Paavai Engineering College Department of Computer Science and Engineering PachalTamilnadu Namakkal India

ISBN: (纸本)9798331512088

People in modern world suffer from a multitude of diseases, the most common of which is diabetes, which affects most senior citizens. Diabetes is associated with kidney problems, cancer, and heart disease. Combining Internet of Things (IoT) with Artificial intelligence (AI) to improve the functionality of remote systems for monitoring health for doctors, patients and patient attendants is one of the most significant technological advancements to date. The integration of AI and IoT also improves clinical workflow efficiency. Intelligent hospital management systems use AI-powered IoT networks to increase resource allocation, shorten wait times, and improve outcomes for patients. Predictive analytics helps to estimate the number of patients admitted and manage hospital inventories effectively. This paper explores the development of an innovative patient monitoring system that focuses on analyzing patient diabetes to facilitate timely medical interventions. In this research work applied three types of Learning methods and subsequently some main parameters were utilized to assess the efficiency of methods. The different machine learning and Deep learning algorithms are compared with one another and created new model with IoT advantages. © 2025 IEEE.

关键词： Artificial Intelligence Diabetes Health Domain Internet of Things Monitoring system

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Guarding Voices, Protecting Homes: A Comprehensive Case Study on Voice Assistant security in Smart Living

Guarding Voices, Protecting Homes: A Comprehensive Case Stud...

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International Symposium on Digital Forensic and security (ISDFS)

作者： Fazalur Rehman Joudat Hashmi Moiz Abdullah Hafsah Zaman School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan Department of Analytics College of Engineering Georgia Tech University USA Department of Cyber Security Faculty of Computing and Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem.

关键词： Data privacy Privacy Ecosystems Personal voice assistants Focusing Smart homes Distance measurement

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E-DoH: Elegantly Detecting the Depths of Open DoH Service on the Internet

arXiv

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arXiv 2024年

作者： Dong, Cong Yang, Jiahai Li, Yun Wu, Yue Chen, Yufan Li, Chenglong Jiao, Haoran Yin, Xia Liu, Yuling Zhongguancun Laboratory China Department of Computer Science and Technology Tsinghua University China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China

In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfuscation to avoid censorship by multiplexing port 443 with web services. This development introduced certain inconveniences in discovering publicly available DoH services. In this paper, we propose the E-DoH method for elegant and efficient DoH service detection. First, we optimized the probing mechanism to enable a single DoH connection to accomplish multiple tasks including service discovery, correctness validation and dependency construction. Second, we propose an efficient DoH detection tool. This tool can enhance probing efficiency while significantly reduce the required traffic volume. Third, based on the above optimization methods, we conducted an exploration of the IPv4 space and performed an in-depth analysis of DoH based on the collected information. Through experiments, our approach demonstrates a remarkable 80% improvement in time efficiency, and only requires 4%-20% traffic volume to complete the detection task. In wild detection, our approach discovered 46k DoH services, which nearly doubles the number discovered by the state-of-the-art. Based on the collected data, we present several intriguing conclusions about the current DoH service ecosystem. Copyright © 2024, The Authors. All rights reserved.

关键词： Ecosystems

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RRDD: An ATT&CK-based ICS Network security Risk Assessment Method 23

RRDD: An ATT&CK-based ICS Network Security Risk Assessment M...

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2nd International Conference on Networks, Communications and Information technology, CNCIT 2023

作者： Wang, Yuman Wang, Wenhui Bai, Xiaoxue Hu, Bo Ge, Guangkai Qian, Kexiang State Grid Smart Grid Research Institute Co. Ltd Sgri Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China State Grid Liaoning Electric Power Supply Co. Ltd China School of Information and Communication Engineering Beijing University of Posts and Telecommunications State Grid Smart Grid Research Institute Co. Ltd Sgri Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China

ISBN: (纸本)9798400700620

The current network security situation of industrial control systems is becoming increasingly severe, and the ICS ATT&CK framework provides a unified knowledge base for attack tactics and techniques for industrial control system network security. This paper presents a RRDD model for network security risk assessment of ICS, which consists of four parts: Risk identification (R), Risk calculation (R), Defense strategy (D), and Defense measures (D). Risk identification is based on the ICS ATT&CK to analyze the network security risk assessment indicators of the ICS, to clarify the vulnerability of the industrial control system, the importance of assets, the threats faced and the existing mitigation measures. Risk calculation is based on the analysis results of risk assessment indicators, establishing a comparative judgment matrix to analyze the relative importance of each indicator, calculating the weight of each indicator, and calculating and grading the overall risk of ICS. Furthermore, the model adopts corresponding safety control strategies and measures based on the risk calculation results, and conducts closed-loop risk assessment and elimination. It can effectively evaluate and disposal the network security risks in ICS. © 2023 ACM.

关键词： Network security

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