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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of Computer Science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Detecting conflict of heterogeneous access control policies

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Digital Communications and Networks 2022年第5期8卷 664-679页

作者： Mingjie Yu Fenghua Li Nenghai Yu Xiao Wang Yunchuan Guo School of Cyber Security University of Science and Technology of ChinaHefei230027China Institute of Information Engineering Chinese Academy of SciencesBeijing100093China School of Cyber Security University of Chinese Academy of SciencesBeijing100049China

Policy conflicts may cause substantial economic *** a large amount of effort has been spent on detecting intra-domain policy conflict,it can not detect conflicts of heterogeneous *** this paper,considering background knowledge,we propose a conflict detection mechanism to search and locate conflicts of heterogeneous ***,we propose a general access control model to describe authorization mechanisms of cloud service and a translation scheme designed to translate a cloud service policy to an Extensible Access Control Markup Language(XACML)*** the scheme based on Multi-terminal Multi-data-type Interval Decision Diagram(MTMIDD)and Extended MTMIDD(X-MTMIDD)is designed to represent XACML policy and search the conflict among heterogeneous *** reduce the rate of false positives,the description logic is used to represent XACML policy and eliminate false *** results show the efficiency of our scheme.

关键词： Conflict detection Access control Decision diagram Policy translation Heterogeneous policy Ontology reasoner

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Blockchain-Based Cognitive Computing Model for Data security on a Cloud Platform

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Computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized Data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

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Annals of Data Science 2024年第6期11卷 2019-2034页

作者： Loomba, Sankalp Dave, Madhavi Arolkar, Harshal Sharma, Sachin School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar India Adani Institute of Digital Technology Management Gujarat Ahmedabad India Faculty of Computer Application and Information Technology GLS University Gujarat Ahmedabad India Indrashil University Gujarat Mehsana India

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Sentiment analysis

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Co-Dance With Ambiguity: An Ambiguity-Aware Facial Expression Recognition Framework for More Robustness

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5431-5444页

作者： Cao, Xinran Luo, Liang Gu, Yu Ren, Fuji University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

Facial Expression Recognition (FER) has received considerable research attention owing to its poor robustness in real-world scenarios. This issue, defined as the uncertainty problem in FER, is often solved by recognizing the noise samples in FER datasets. Unlike noise samples with incorrect labels, ambiguous samples exhibit mixed emotions that align with multiple basic expressions. It makes them indistinguishable in training and harms model robustness. To address this issue, we propose an ambiguity-aware FER framework called Co-dance with Ambiguity (CoA). CoA combines an Emotion Extraction Module (EEM) and an Expression Description Module (EDM) to leverage ambiguity for better performance and robustness. Specifically, EEM employs a coupled-stream structure to extract both representative and detailed features through diverse-scale fusion and patch-attention sensing. EDM adjusts ground-truth labels of ambiguous samples by introducing label pairs derived from the top two highest predictions, describing the mixed-emotion nature. The pairs guide the model to align feature extraction with the inherent ambiguity of ambiguous samples during training. Extensive experiments on five in-the-wild FER datasets demonstrate the superiority of CoA over advanced methods. Moreover, introducing ambiguity-aware strategies enriches feature representations and significantly enhances robustness when faced with a high ratio of ambiguous samples in FER. © 1991-2012 IEEE.

关键词： Face recognition

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Dissecting zero trust:research landscape and its implementation in IoT

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cybersecurity 2025年第2期8卷 201-228页

作者： Chunwen Liu Ru Tan Yang Wu Yun Feng Ze Jin Fangjiao Zhang Yuling Liu Qixu Liu Institute of Information Engineering Chinese Academy of SciencesBeijing 100085China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China China Cybersecurity Review Technology and Certification Center Beijing 100013China

As a progressive security strategy,the zero trust model has attracted notable attention and importance within the realm of network security,especially in the context of the Internet of Things(IoT).This paper aims to evaluate the current research regarding zero trust and to highlight its practical applications in the IoT sphere through extensive bibliometric *** also delve into the vulnerabilities of IoT and explore the potential role of zero trust security in mitigating these risks via a thorough review of relevant security ***,the challenges associated with implementing zero trust security are *** provide a summary of these issues and suggest possible pathways for future research aimed at overcoming these ***,this study aims to serve as a strategic analysis of the zero trust model,intending to empower scholars in the field to pursue deeper and more focused research in the future.

关键词： Zero trust Research landscape Bibliometrics method Internet of things Hot topics

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On-Demand Emergency Medical Response: A Seamless Android Application for Auto Ambulance System

On-Demand Emergency Medical Response: A Seamless Android App...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Samuel, Prithi Reshmy Arun Kumar, R. Nilay Keshri, Karan SRM Institute of Science and Technology School of Compuitng Department of Computational Intelligence Chennai India University of South Wales Department of Digital Forensics and Cyber Security Treforest United Kingdom

ISBN: (纸本)9798331522988

This Android app has an easy-to-use interface that makes it possible for users to quickly reach local ambulance services. By utilizing location-based services, the application locates available ambulance services quickly and provides users with up-to-date information, including projected arrival times. Customers may easily reserve an ambulance and follow all the specifics of their reservation in real time, including the ambulance's present position and expected arrival time. For people in urgent need of medical assistance, the application's user-friendly features and straightforward design make it a vital resource. The functionality, accessibility, and importance of the Android application in enabling prompt emergency medical aid are highlighted in this abstract, which captures the core of the product. © 2024 IEEE.

关键词： Ambulances

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Research on Site Planning of Mobile Communication Network

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Computers, Materials & Continua 2024年第8期80卷 3243-3261页

作者： Jiahan He Guangjun Liang Meng Li KefanYao Bixia Wang Lu Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210000China Engineering Research Center of Electronic Data Forensics Analysis Nanjing210000China Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjing210000China Basic Course Teaching and Research Department Jiangsu Police InstituteNanjing210000China Communication and Signal College Nanjing Railway Vocational and TechnicalNanjing210000China

In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is ***,based on practical needs,mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives,coverage objectives,and quality ***,a multi-objective optimization model was established by combining threshold and traffic volume *** order to reduce the time complexity of optimization,a non-dominated sorting genetic algorithm(NSGA)is used to solve the multi-objective optimization problem of site ***,a strategy for clustering and optimizing weak coverage areas was *** order to avoid redundant neighborhood retrieval during cluster expansion,the Fast Density-Based Spatial Clustering of Applications with Noise(FDBSCAN)clustering method was *** different sub-objectives as the main objectives,this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations,as well as relevant site planning maps,and provided three planning schemes for different main *** simulation results show that the traffic coverage of the three station planning schemes is above 90%.The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.

关键词： Siting of station multi-objective optimization genetic algorithm NSGA general greed FDBSCAN cluster

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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A verifiable EVM-based cross-language smart contract implementation scheme for matrix calculation

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Digital Communications and Networks 2025年第2期11卷 432-441页

作者： Yunhua He Yigang Yang Chao Wang Anke Xie Li Ma Bin Wu Yongdong Wu Information Science and Technology North China University of TechnologyBeijing100144China Bei Hang University Yunnan Innovation Institute Yunnan650233China Institute of Information Engineering Chinese Academy of SciencesBeijing101408China College of Cyber Security and College of Information Science and Technology Jinan UniversityGuangzhou510632China

The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses,which involve the calculation of complex functions,such as matrix ***,complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine(EVM)-based smart contract platforms due to their distributed security environment *** off-chain methods often result in a significant reduction in contract execution efficiency,thus a platform software development kit interface implementation method has become a feasible way to reduce overheads,but this method cannot verify operation correctness and may leak sensitive user *** solve the above problems,we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations,especially matrix operations,which can guarantee operation correctness and user privacy while ensuring computational *** this scheme,a verifiable interaction process is designed to verify the computation process and results,and a matrix blinding technology is introduced to protect sensitive user data in the calculation *** security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.

关键词： Smart contract Blockchain Cross-language programming Bilinear pairing Publicly verifiable computation

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