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Intelligent cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Sriprada Ramesh Simna T M Mohana Department of Chemical Engineering RV College of Engineering® Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The reliance on credit cards for everyday purchases has become ubiquitous in modern society. This poses security threats as cybercriminals are constantly adopting new techniques to commit fraudulent activities. The need for financial organizations to protect their customer's data is at most important. Machine learning (ML) algorithms provide robust fraud detection capabilities and hence can be applied in in developing advanced systems that continuously monitor transactions in real-time, swiftly identifying suspicious patterns indicative of fraudulent activity. Proposed work focuses on comparing for ML models such as Random Forest, Logistic Regression, SVM, and XGBOOST for detecting credit card frauds. The objective is to maximize the detection of fraud transactions while reducing false positives. The f1 score of the models are compared to identify the most efficient algorithm. XGBOOST and Random Forest proved to perform the best in credit card fraud detection with a fl-score of 0.81.

关键词： Support vector machines Logistic regression Machine learning algorithms Forestry Organizations Credit cards Real-time systems Fraud security Random forests

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An Intelligent security-Enabled Home Automation System using Internet of Things Environment 3

An Intelligent Security-Enabled Home Automation System using...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Karthikeyan, P. Dinesh, M. Akila, A. Veeramuthu, A. Giri, Jayant Al Sherideh, Ala'a Prathyusha Engineering College Department of Eee Tiruvallur Chennai India Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Puduvoyal India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University College of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331509675

IoT technologies to bolster residential security, safety, and sustainability. By leveraging facial recognition for door access, enhancing fire safety monitoring, and integrating energy harvesting, it aims to create more resilient and eco-friendly residential environments. These innovations offer comprehensive solutions to address evolving challenges and optimize residential living. The proposed research introduces an innovative smart home automation system leveraging IoT technologies and Google Assistant for the voice-controlled operation of household devices. It integrates a comprehensive array of sensors for safety and security monitoring, facilitating real-time alerts for fire outbreaks, unauthorized entry, and environmental anomalies. MQTT enables smooth remote access and management of devices. The research evaluates IoT routing protocols, highlighting ISHAS-IoTs's benefits in efficiency, delivery ratio, and delay. These findings underscore the protocol's potential to enhance IoT network performance and efficiency, contributing to the design of more robust and reliable smart home systems. © 2025 IEEE.

关键词： Smart homes

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A Machine Learning Approach for Identifying Mental Health Disorders and Depression in Psychoactive Drug Users

A Machine Learning Approach for Identifying Mental Health Di...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Cherukuri Srivardhan Esther Daniel S. Jebapriya T. Mathu Division of Computer Science and Engineering Karunya Institute of Technology & Sciences Coimbatore Division of Data Science and Cyber Security Karunya Institute of Technology & Sciences Coimbatore

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Mental illnesses are among the most prevalent problems worldwide regardless of age. The current investigation examines ML (Machine Learning) methods to predict Depression and CMD (Common Mental Disorders) for people who utilize Psychoactive Substances. An assortment of techniques such as Support Vector Machine(SVM), Random Forest(RF), K-Nearest Neighbour(KNN), and Naive Bayes, as well as a voting classifier that combines Baging Classifier, along with a Decision Tree, are used. Utilizing Sequential Backward Selection (SBS), the findings demonstrated exceptional accuracy, with Support Vector Machine (SVM) achieving 84.33% for Common Mental Disorder (CMD) predictions as well as 83.93 % for Depression. In an attempt to achieve higher accuracy, possibly exceeding 90%, ensemble techniques such as the voting classifier were investigated.

关键词： Support vector machines Drugs Accuracy Mental disorders Machine learning Mental health Predictive models Depression Vectors Real-time systems

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MCM- F: dance hand gestures recognition with vision based features

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Discover Internet of Things 2024年第1期4卷 1-21页

作者： Devi, Mampi Saharia, Sarat Kumar Bhattacharyya, Dhruba Roy, Alak Charanarur, Panem Department of Cyber Security and Digital Forensic National Forensic Sciences University Tripura Campus Agartala India Department of Computer Science and Engineering Tezpur University Tezpur India Department of Information Technology Tripura University Agartala India

To digitize and preserve the cultural heritage in the form of Indian classical dance become apparent area of research. Sattriya classical dance of North-East India (Assam) is one of the eight Indian classical dance forms that requires immediate preservation. Sattriya classical dance consists of 29 Asamyukta hastas (single-hand gestures) and 14 Samyukta hastas (double-hand gestures). Moreover, the foundation of Samyukta hasta depends on understanding Asamyukta hasta. Therefore, the paper aims to classify single-hand gestures of Sattriya classical dance only. Although, a solution based on two level classification method to classify the Sattriya classical dance is available in recent literature, but it requires a trial and error method to select the optimized features. Since, Asamyukta hastas can appear closely similar to each other and therefore misclassification chances are very high. Thus, accuracy rate obtained for the two level classification method was only 75.45%. So, to address this issues in this paper, a Multilevel Classification Model with Vision based Features (MCM- $$V_b$$ F) has been proposed to classify the Asamyukta hastas of Sattriya classical dance. This model uses two types of feature matching, viz., high-level feature matching and low-level feature matching. To extract the high-level features and low-level features different algorithm has been proposed. In this model, features are automatically selected. This proposed MCM- $$V_b$$ F model is also tested on Asamyukta hasta mudras of Bharatanatyam classical dance of South India (Tamil Nadu). This model obtain an accuracy 94.12%, 87.14% for Sattriya classical dance Single-Hand Gestures (SSHG) dataset and Bharatnatyam classical dance Single-Hand Gestures (BHSG) dataset respectively. This paper also provides the comparative study of the proposed model MCM- $$V_b$$ F with traditional bench-mark classifier model such as Naive Bayes, Decision Tree and Support Vector Classifier (SVM) etc.

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Efficient Private Circuits with Precomputation

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2023年第2期2023卷 286-309页

作者： Wang, Weijia Ji, Fanjie Zhang, Juelin Yu, Yu School of Cyber Science and Technology Shandong University Qingdao China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao China Quan Cheng Shandong Laboratory Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Qi Zhi Institute Shanghai China Shanghai Key Laboratory of Privacy-Preserving Computation Shanghai China

At CHES 2022, Wang et al. described a new paradigm for masked implementations using private circuits, where most intermediates can be precomputed before the input shares are accessed, significantly accelerating the online execution of masked functions. However, the masking scheme they proposed mainly featured (and was designed for) the cost amortization, leaving its (limited) suitability in the above precomputation-based paradigm just as a bonus. This paper aims to provide an efficient, reliable, easy-to-use, and precomputation-compatible masking scheme. We propose a new masked multiplication over the finite field Fq suitable for the precomputation, and prove its security in the composable notion called Probing-Isolating Non-Inference (PINI). Particularly, the operations (e.g., AND and XOR) in the binary field can be achieved by assigning q = 2, allowing the bitsliced implementation that has been shown to be quite efficient for the software implementations. The new masking scheme is applied to leverage the masking of AES and SKINNY block ciphers on ARM Cortex M architecture. The performance results show that the new scheme contributes to a significant speed-up compared with the state-of-the-art implementations. For SKINNY with block size 64, the speed and RAM requirement can be significantly improved (saving around 45% cycles in the online-computation and 60% RAM space for precomputed values) from AES-128, thanks to its smaller number of AND gates. Besides the security proof by hand, we provide formal verifications for the multiplication and T-test evaluations for the masked implementations of AES and SKINNY. Because of the structure of the new masked multiplication, our formal verification can be performed for security orders up to 16. © 2023, Ruhr-University of Bochum. All rights reserved.

关键词： Formal verification

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Enhancing Wireless Communication with Compact SIW Resonator-based UWB Fork Monopole Antenna 25

Enhancing Wireless Communication with Compact SIW Resonator-...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Samir, Ahmed Bendoukha, Samir Attar, Hani Ababneh, Jafar Al-Hihi, Mohammad Hafez, Mohamed Mti University Faculty of Engineering Department of Electrical Engineering Cairo Egypt Taibah University College of Engineering at Yanbu Department of Electrical Engineering Saudi Arabia Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Erlangen Germany Feqs INTI-IU-University Faculty of Engineering Nilai Malaysia

ISBN: (纸本)9798331540012

The rapid expansion of wireless communication across networks and devices, driven by advancements in the Internet of Things (IoT), highlights the need for compact, lightweight, cost-effective, and efficient antennas to meet market demands and support wireless systems. Patch and substrate-integrated waveguide (SIW) antennas have emerged to address these needs. In this study, a substrate-integrated waveguide resonator is used to construct an ultra-wideband (UWB) fork monopole antenna. The SIW resonator enhances antenna performance and bandwidth while reducing weight and dimensions. A prototype antenna was designed, manufactured, and evaluated through experimental tests. The impact of integrating array holes into the antenna on bandwidth characteristics is also examined. Significant performance improvements are demonstrated by the proposed SIW-based UWB fork monopole antenna. Simulations and experimental results show a bandwidth improvement of up to 10.1 GHz, an S11 value reduction to -47.5 dB with one array of holes, and a gain increase to 4.9642 dB. These metrics confirm the antenna's effectiveness in overcoming traditional bandwidth limitations and improving overall performance. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset 26

Improving Multilayer-Perceptron(MLP)-based Network Anomaly D...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yin, Yuhua Jang-Jaccard, Julian Sabrina, Fariza Kwak, Jin Massey University Cybersecurity Lab School of Mathematical and Computational Sciences New Zealand Central Queensland University School of Engineering and Technology Australia Ajou University Department of Cyber Security Korea Republic of

ISBN: (纸本)9798350331684

The network intrusion threats are increasingly severe with the application of computer supported coorperative work. Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or K-means as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model. © 2023 IEEE.

关键词： Intrusion detection

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IoT-Enabled Respiratory Pattern Monitoring in Critical Care: A Real-Time Recurrent Neural Network Approach 10

IoT-Enabled Respiratory Pattern Monitoring in Critical Care:...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajarajan, S. Kalaivani, R. Kaliammal, N. Saravanan, S.T. Ilampiray, P. Srinivasan, C. Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mit College of Arts and Science For Women Musiri Tamil Nadu Trichy India Department of Electronics and Communication Engineering Namakkal Tamil Nadu Rasipuram India Sphoorthy Engineering College Department of CSE-Cyber Security Telangana Hyderabad India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350353068

Critical in intensive care units (ICUs), monitoring respiratory patterns is essential for diagnosing respiratory distress early and treating patients with severe illnesses properly. Conventional monitoring techniques may not be able to capture all aspects of respiratory dynamics in real-time. This research presents a real-time recurrent neural network (RNN) respiratory pattern monitoring system that can be integrated with the Internet of Things (IoT). Tidal volume, respiratory rate, and inspiratory and expiratory flow patterns are just a few of the respiratory characteristics that may be remotely monitored using our system IoT sensors that continuously gather data. An RNN model learns patterns in the time series of respiratory signals and gives instantaneous feedback on the patient's condition based on the processed data. It demonstrates that our approach successfully detects aberrant breathing patterns by evaluating its performance using data obtained from critical care patients. Compared to more conventional ways of monitoring, the proposed system has several benefits, such as the ability to identify respiratory problems early on, analyze data in real time, and provide continuous monitoring. Improving patient outcomes and quality of treatment in critical care settings may potentially provide immediate insights into respiratory dynamics. © 2024 IEEE.

关键词： Recurrent neural networks

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DataJudge: Cross-Chain Data Consistency Verification Based on Extended Merkle Hash Tree 24th

DataJudge: Cross-Chain Data Consistency Verification Based o...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Zeng, Junyu Cao, Jie Dai, Shengjiang Xiang, Qiao Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

Consortium blockchains, with their decentralized characteristics, have found widespread applications across various industries. However, this also forms isolated data islands. These data islands have become obstacles to data sharing between industries, and the issue of cross-chain data consistency verification remains unresolved. Current mainstream cross-chain technologies solve asset transfer issues through complex transaction steps, thus failing to meet the need for efficient data transmission. Existing research has proposed solutions for cross-chain data consistency verification, but two major issues remain. Firstly, these schemes do not consider data changes or support data dynamic operations. Secondly, these schemes involve a significant amount of complex computations, leading to slow efficiency. To address the above issues, we propose DataJudge, a cross-chain data consistency verification scheme based on an extended Merkle hash tree. This scheme not only verifies cross-chain data consistency but also effectively fills the gap in existing schemes regarding dynamic operations. Additionally, we modify the paillier homomorphic encryption. To improve efficiency, we do not introduce many complex computations. Furthermore, the extended Merkle hash tree structure enhances efficiency. Finally, we implement a prototype system and evaluate it. The results demonstrate that DataJudge can effectively verify cross-chain data consistency and outperform existing schemes such as DCIV and BeDCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data consistency

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ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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