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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Ravi Kanth, R. Prem Jacob, T.P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

Cloud computing (CC) is vulnerable to existing information technology attacks, since it extends and utilizes information technology infrastructure, applications and typical operating systems. In this manuscript, an Enhanced capsule generative adversarial network (ECGAN) with blockchain based Proof of authority consensus procedure fostered Intrusion detection (ID) system is proposed for enhancing cyber security in CC. The data are collected via NSL-KDD benchmark dataset. The input data is fed to proposed Z-Score Normalization process to eliminate the redundancy including missing values. The pre-processing output is fed to feature selection. During feature selection, extracting the optimum features on the basis of univariate ensemble feature selection (UEFS). Optimum features basis, the data are classified as normal and anomalous utilizing Enhanced capsule generative adversarial networks. Subsequently, blockchain based Proof of authority (POA) consensus process is proposed for improving the cyber security of the data in cloud computing environment. The proposed ECGAN-BC-POA-IDS method is executed in Python and the performance metrics are calculated. The proposed approach has attained 33.7%, 25.7%, 21.4% improved accuracy, 24.6%, 35.6%, 38.9% lower attack detection time, and 23.8%, 18.9%, 15.78% lower delay than the existing methods, like Artificial Neural Network (ANN) with blockchain framework, Integrated Architecture with Byzantine Fault Tolerance consensus, and Blockchain Random Neural Network (RNN-BC) respectively. © 2023 IEEE.

关键词： Blockchain

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Ethereum DeFi Apps in the Wild: Profiling and Implications

Ethereum DeFi Apps in the Wild: Profiling and Implications

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Wang, Ziwei Lu, Haotian Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350333336

Decentralized Finance (DeFi) has been gaining rising importance due to advanced blockchain technology and web3.0. Consequently, many decentralized finance applications (DeFi apps) have emerged, which aim to reconstruct traditional finance and provide financial services with decentralization, transparency, and autonomy. To better understand the characteristics of DeFi apps in this rapidly evolving era, in this paper, we propose to profile DeFi apps based on 666 rigorously classified DeFi apps from a multi-layer perspective, which includes the transaction layer, token layer, and value layer. Our research provides insight into the multiple layers of DeFi apps, including both the breadth and the depth of current transactions, the degree to which apps are interconnected at the level of value flow, and an initial investigation into the relationship between whale trading and market crashes, which better help DeFi app developers with app design, performance evaluation, and optimization. © 2023 IEEE.

关键词： Blockchain

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Bangla Protest News Categorization Using Deep Learning 26

Bangla Protest News Categorization Using Deep Learning

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Mosleh, Mahir Sayem, Md. Abu Shahidur Rahman, M. Islam, Md Saiful Shahjalal University of Science and Technology Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

Protests are a vital form of public expression, often reflecting collective dissent towards specific policies or injustices. In recent years, the significance of protests as a medium for public opinion has escalated, necessitating effective categorization for informed decision-making by policymakers, law enforcement, researchers, and journalists. This paper presents the first study on categorizing Bangla protest-related news articles, addressing the absence of a publicly available annotated dataset in this domain. We collected and annotated a dataset of 8,039 protest-related news articles from prominent Bangla newspapers, such as Prothom Alo, Samakal, Daily Star Bangla, and Ittefaq. We employed a multifaceted approach, leveraging attention mechanisms with deep learning and transformer-based models to categorize protest news into eight predefined protest categories. The fine-tuned multilingual BERT model showcased superior efficacy, achieving an accuracy of 91.58% and an F1 score of 91.43%. This study fills a critical gap in protest categorization research and provides a comprehensive dataset and methodological approach for future exploration. The findings have significant implications for understanding the dynamics of social movements and public protests, offering valuable insights for academics and practitioners alike. This research paves the way for further studies in categorizing protest-related news, particularly in underrepresented languages like Bangla. © 2023 IEEE.

关键词： Bangla Bangla News BERT LSTM NLP Protest Classification Protest News

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A stacked ensemble approach to detect cyber attacks based on feature selection techniques

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual Information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

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Transition metal anchored on C_(9)N_(4) as a single-atom catalyst for CO_(2) hydrogenation: A first-principles study

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Chinese Physics B 2022年第10期31卷 520-528页

作者： Jia-Liang Chen Hui-Jia Hu Shi-Hao Wei Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China Department of Electronic and Information Engineering School of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211China

To alleviate the greenhouse effect and maintain the sustainable development, it is of great significance to find an efficient and low-cost catalyst to reduce carbon dioxide(CO_(2)) and generate formic acid(FA). In this work, based on the first-principles calculation, the catalytic performance of a single transition metal(TM)(TM = Cr, Mn, Fe, Co, Ni, Cu, Zn,Ru, Rh, Pd, Ag, Cd, Ir, Pt, Au, or Hg) atom anchored on C_(9)N_(4) monolayer(TM@C_(9)N_(4)) for the hydrogenation of CO_(2) to FA is calculated. The results show that single TM atom doping in C_(9)N_(4) can form a stable TM@C_(9)N_(4) structure, and Cu@C_(9)N_(4) and Co@C_(9)N_(4) show better catalytic performance in the process of CO_(2) hydrogenation to FA(the corresponding maximum energy barriers are 0.41 eV and 0.43 e V, respectively). The partial density of states(PDOS), projected crystal orbital Hamilton population(p COHP), difference charge density analysis and Bader charge analysis demonstrate that the TM atom plays an important role in the reaction. The strong interaction between the 3d orbitals of the TM atom and the non-bonding orbitals(1πg) of CO_(2) allows the reaction to proceed under mild conditions. In general, our results show that Cu@C_(9)N_(4) and Co@C_(9)N_(4) are a promising single-atom catalyst and can be used as the non-precious metals electrocatalyst for CO_(2) hydrogenation to formic acid.

关键词： first-principles calculation CO_(2)hydrogenation catalysts electronic structure reaction mechanisms reaction paths

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An Approach to Detect Cardiomegaly, COVID-19, Pneumonia, Pneumothorax and Tuberculosis from CXR Images Using Ensembles of Deep Learning 26

An Approach to Detect Cardiomegaly, COVID-19, Pneumonia, Pne...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Muthaki, Tahsina Masuk, Safwan Ibne Maksud, Akila Rafi, Md. Ashfaq Hosain Sakib, Nazmus Ahsanullah University of Science & Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Recent advancements in medical image analysis have harnessed the potential of deep learning, especially in diagnosing thoracic ailments using chest X-ray (CXR) images. This study introduces a novel approach to detecting a range of thoracic diseases, including COVID-19, pneumonia, pneumothorax, cardiomegaly, and tuberculosis, through ensembles of deep learning models. The development of a reliable and efficient diagnostic system is crucial given the increasing significance of early disease detection and the growing number of CXR images in medical databases. The proposed ensemble method combines features of multiple deep learning models to improve overall performance, leveraging their distinct strengths. The study includes extensive experimentation and comparative analyses on a diverse and extensive dataset to highlight the superior performance of this approach. The results demonstrate that the ensemble-based model outperforms individual models, achieving a higher accuracy of 98.37%, sensitivity of 0.9837, and precision of 0.9836, thus presenting significant potential for robust and multi-disease detection in CXR images. Our results hold promise for advancing healthcare and clinical decision-making, underlining the valuable contribution of this research. This work not only underscores the potency of deep learning in medical imaging but also highlights its pivotal role in elevating patient care. © 2023 IEEE.

关键词： Cardiomegaly Chest X-ray Classification COVID-19 Ensemble Pneumonia Pneumothorax Tuberculosis

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Predicting health insurance claim frauds using supervised machine learning technique 8

Predicting health insurance claim frauds using supervised ma...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Veena, K. Supriya, S. Deepa, D. Cruz Antony, J. Karpura Dheepan, G.M. Pavan, Ayila Sri Dharma Siva Pagadalahariprasad Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated detection of healthcare scams uses data mining and machine learning approaches. In this work, an effort is made to provide a review of healthcare industry frauds and the methods for spotting them. A proposed solution to address issues in health insurance claims involves using a machine learning model that focuses on utilizing appropriate methods, identifying key data sources, and examining the properties of healthcare data. To gain insight into the patterns of the features, univariate and bivariate analysis are conducted on the data. Afterwards, proper data visualization is performed to determine which feature has the greatest impact. Based on the pre-processed data, a machine learning model is constructed and compared with four algorithms: Logistic Regression, Random Forest, Decision Tree Classifier, and Naive Bayes. The highest accuracy was achieved through the Decision Tree Classifier algorithm, with an accuracy of 97.03%. © 2023 IEEE.

关键词： Health insurance

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DoS Attack Detection Based on Deep Factorization Machine in SDN

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computer Systems science & engineering 2023年第5期45卷 1727-1742页

作者： Jing Wang Xiangyu Lei Qisheng Jiang Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Software-Defined Network(SDN)decouples the control plane of network devices from the data *** alleviating the problems presented in traditional network architectures,it also brings potential security risks,particularly network Denial-of-Service(DoS)*** many research efforts have been devoted to identifying new features for DoS attack detection,detection methods are less accurate in detecting DoS attacks against client hosts due to the high stealth of such *** solve this problem,a new method of DoS attack detection based on Deep Factorization Machine(DeepFM)is proposed in ***,we select the Growth Rate of Max Matched Packets(GRMMP)in SDN as detection ***,the DeepFM algorithm is used to extract features from flow rules and classify them into dense and discrete features to detect DoS *** training,the model can be used to infer whether SDN is under DoS attacks,and a DeepFM-based detection method for DoS attacks against client host is *** results show that our method can effectively detect DoS attacks in *** with the K-Nearest Neighbor(K-NN),Artificial Neural Network(ANN)models,Support Vector Machine(SVM)and Random Forest models,our proposed method outperforms in accuracy,precision and F1 values.

关键词： Software-defined network denial-of-service attacks deep factorization machine GRMMP

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A Novel Approach for Duty Cycle Optimization in Designing Scalable Wireless Sensor Networks 5th

A Novel Approach for Duty Cycle Optimization in Designing Sc...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： De, Sudip Kumar Banerjee, Avishek Majumder, Koushik Department of Information Technology Asansol Engineering College WB Asansol India Department of Computer Science and Engineering MAKAUT WB Kolkata India

ISBN: (纸本)9789819791118

This paper aims to enhance the efficiency of wireless sensor networks (WSNs) within an area of interest (AOI) by optimizing duty cycles. Focusing on a specific AOI divided into clusters, each with a dynamically selected cluster head, the study emphasizes establishing a time-synchronized duty cycle optimization protocol in the communication between cluster heads and the base stations. Unlike conventional approaches that randomly select nodes for duty cycle optimization, this paper introduces a method to identify the optimal cluster head (CH) of each cluster based on maximum remaining energy and a shorter distance from the Base Station (BS) to maximize the durability of the network. By introducing a time-synchronized Duty Schedule, the proposed methodology ensured that nodes that are not in use will enter a sleep mode, conserving energy. To ensure effective communication between the Data Centre and multiple Base Stations (BSs) of AOIs, the Particle Swarm Optimization (PSO) has been also used. Through simulation and analysis, the effectiveness of the proposed duty cycle optimization method is evaluated, demonstrating its potential to enhance WSN performance in terms of energy efficiency, network longevity, and communication. This research contributes to advancing the design and operation of WSNs, particularly in scenarios where energy conservation and data transmission are paramount concerns. The suggested duty cycle model assures a 20.44% increase in the network’s lifetime as compared to other available studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1051-1063页

作者： Sardar, Alamgir Umer, Saiyed Rout, Ranjeet Kumar Khan, Muhammad Khurram Aliah University Department of Computer Science and Engineering Kolkata700156 India National Institute of Technology Department of Computer Science and Engineering Srinagar190006 India King Saud University Center of Excellence in Information Assurance Riyadh11451 Saudi Arabia

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this article presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-Cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users' tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multiclass linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100% correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system. © 2020 IEEE.

关键词： Palmprint recognition

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