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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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Ensuring data Confidentiality with a Secure XTS-AES Design in Flash Translation Layer 5

Ensuring Data Confidentiality with a Secure XTS-AES Design i...

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5th IEEE International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2020

作者： Zhang, Qionglu Jia, Shijie He, Junlin Zhao, Xinyi Xia, Luning Niu, Yingjiao Jing, Jiwu State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Beijing Certificate Authority Corporation Limited Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160245

Nowadays sensitive data are commonly stored on personal mobile devices (e.g., laptops and smartphones) or public cloud storage platforms. data on these platforms are commonly protected with strong cryptography like full disk encryption (FDE), and XTS-AES is one of the most widely used encryption algorithms in FDE. Unfortunately, realizing XTS-AES above the block device layer on flash-based block devices is problematic: it neglects the unique nature of the underlying flash memory, which is the most widely employed storage in the mobile devices and cloud infrastructure. As the tweak values of the physical flash pages mapping to the same logical block address are same, thus the system will suffer from the chosen-plaintext attacks due to the out-of-place update of the flash memory. In this paper, we first introduce the concrete attacks for the prior XTS-AES implementation due to neglecting the unique nature of the underlying flash memory, and then propose SecureXTS, a secure XTS-AES implementation method in flash translation layer (FTL). SecureXTS takes advantage of physical page number and block erasure number to generate the tweak value, without introducing additional metadata and hardware extensions. We provide a proof-of-concept SecureXTS implementation using OpenNFM. The experimental results show that, compared with conventional XTS-AES implementations, our SecureXTS can provide secure data encryption functions and data confidentiality with negligible performance overhead. © 2020 IEEE.

关键词： Sensitive data

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Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network

arXiv

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arXiv 2024年

作者： Fang, Xiang Fang, Wanlong Wang, Changshuo Liu, Daizong Tang, Keke Dong, Jianfeng Zhou, Pan Li, Beibei Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science of Technology Wuhan China Sichuan University China Nanyang Technological University Singapore Peking University China Guangzhou University China Zhejiang Gongshang University China

Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the relationship between different pairs. We observe that the similar video/query content not only helps the TSG model better understand and generalize the cross-modal representation, but also assists the model in locating some complex video-query pairs. Previous methods follow a single-thread framework that cannot co-train different pairs and usually spends much time re-obtaining redundant knowledge, limiting their real-world applications. To this end, in this paper, we pose a brand-new setting: Multi-Pair TSG, which aims to co-train these pairs. In particular, we propose a novel video-query co-training approach, Multi-Thread Knowledge Transfer Network, to locate a variety of video-query pairs effectively and efficiently. Firstly, we mine the spatial and temporal semantics across different queries to cooperate with each other. To learn intra- and inter-modal representations simultaneously, we design a cross-modal contrast module to explore the semantic consistency by a self-supervised strategy. To fully align visual and textual representations between different pairs, we design a prototype alignment strategy to 1) match object prototypes and phrase prototypes for spatial alignment, and 2) align activity prototypes and sentence prototypes for temporal alignment. Finally, we develop an adaptive negative selection module to adaptively generate a threshold for cross-modal matching. Extensive experiments show the effectiveness and efficiency of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Modal analysis

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Auction based clustered federated learning in mobile edge computing system

arXiv

引用

arXiv 2021年

作者： Lu, Renhao Zhang, Weizhe Li, Qiong Zhong, Xiaoxiong Vasilakos, Athanasios V. School of Computer Science and Technology Harbin Institute of Technology Harbin China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Computer Science and Technology Fuzhou University Fuzhou350116 China Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

In recent years, mobile clients’ computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients’ resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system’s energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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DAMIA: Leveraging domain adaptation as a defense against membership inference attacks

arXiv

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arXiv 2020年

作者： Huang, Hongwei Luo, Weiqi Zeng, Guoqiang Weng, Jian Zhang, Yue Yang, Anjia College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

Deep Learning (DL) techniques allow one to train a model from a dataset, and the model can be adopted to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. Being a core engine of DL, models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the dataset the model trained on. Efforts have been made to hinder the attack but unfortunately, they may be subject to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense to counter membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both the original dataset and the introduced dataset. Seeing that the model is obfuscated, membership inference fails, while the extracted common features provide supports for usability. Extensive experiments have been conducted, which validates our intuition. The model counters the membership inference attacks and has a negligible footprint to the usability. Our experiment also excludes a few factors that may hinder the performance of DAMIA, providing a potential guideline that instructs the vendors and researchers to benefit from our solution in a timely manner. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

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Adaptive Fine-grained Access Control Method in Social Internet of Things

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International Journal of Network security 2021年第1期23卷 42-48页

作者： Zhang, Hongbin Ma, Pengcheng Liu, Bin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal China School of Economics and Management Hebei University of Science China Technology Research Center of Big Data and Social Computing Hebei University of Science China

Social Internet of Things (SloT), as a new carrier of integration of social and Internet of Things, applies the research results of social networks from different aspects of the Internet of Things. Different types of connected intelligent objects interact socially, compared with random data access between them, access control technology is more stringent. This paper integrates social attributes into attributebased access control of Internet of Things, initializes relational attribute tags, and labels social interest attributes for different objects, then quantifes tag similarity and implements initial access control authorization, integrates social attributes into game theory to dynamically adjust access control policies, so the adaptive finegrained division of access control under the Social Internet of Things is effectively realized. The experimental results show that our method can not only effectively carry out initial authorization according to tag similarity, but also further adaptively adjust the permission policy according to social attributes, and further meet the finegrained partition requirements of access control, which is ensure the effective implementation of access control under the Social Internet of things. © 2021. All Rights Reserved.

关键词： Game theory

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Extending Registration and Authentication Processes of FIDO2 External Authenticator with QR Codes

Extending Registration and Authentication Processes of FIDO2...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Chengqian Guo Quanwei Cai Qiongxiao Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering CAS Data Assurance and Communication Security Research Center CAS School of Cyber Security University of Chinese Academy of Sciences School of Cyber Security University of Science and Technology of China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

FIDO2, the newest set of FIDO specifications, enables the user to leverage an external authenticator for the authentication in both mobile and desktop environments (referred to as user agent). For the secure registration and authentication, FIDO2 requires the external authenticator and user agent to establish a confidential and mutually authenticated data transport channel through either USB interfaces, Near Field Communication (NFC) or Bluetooth. However, the external authenticator and host may not be equipped with one of the above physical media simultaneously, for example, a desktop may only have USB interfaces while an external authenticator (e.g., a smartphone) may have no USB inferfaces. This affects the wide adoption of FIDO2. In this paper, we extend the registration and authentication processes of FIDO2 external authenticator with QR code, which enables the external authenticator being equipped with a camera to be used for the authentication at any user agent. During the registration process, our scheme requires the user to provide the original credential and a one-time password displayed on the authenticator, and therefore ensures the correct user will only be bound with the expected authenticator. The security of our scheme has been formally analyzed based on the Dolev-Yao style model, a widely adopted model for the analysis of web systems. We have implemented the prototype, and the performance evaluation demonstrated the efficiency of our scheme, which needs 373 ms for registration and 141 ms for authentication in our environment.

关键词： Analytical models Privacy Authentication Prototypes Near field communication Universal Serial Bus security

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