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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Prasad, Sanjana Sharma, Ishu Arun, S. Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

ISBN: (纸本)9798350370249

DNS attacks can be caused by various methods such as Rebinding attacks, Cryptojacking and Domain Generation Algorithm. Classifying DNS attacks in a timely manner and taking necessary action to mitigate the issue is important. This research study describes the hybrid model of Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) that is used to improve the detection or categorization of DNS attacks. A dataset called CIRA-CIC-DoHBrw-2020 is used to train the suggested hybrid mode. According to experimental data, the LSTM prototype outperformed CNN with respect to identifying or categorizing DNS attacks. The CNN-LSTM and CNN-GRU hybrid models have the ability to improve the model's accuracy. CNN was used for feature extraction and LSTM for temporal modeling in the CNN-LSTM hybrid model. The suggested CNN-GRU hybrid model, on the other hand, obtains the greatest accuracy of 95.16%, demonstrating the efficacy of integrating GRU with CNN to effectively recognize or categorize both temporal and geographical characteristics of DNS network data. The CNN-GRU model's enhanced performance demonstrates how well merging different neural network architectures—like CNNs and GRUs—may enhance convergence and lower error rates. © 2024 IEEE.

关键词： Convolutional Neural Networks DNS Security Gated Recurrent Units Infrastructure Security Network Security Network Traffic Classification

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An efficient hybrid multifactor mutual authentication and session key agreement scheme for patient monitoring system using IoMT

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Multimedia Tools and Applications 2024年第36期83卷 83805-83835页

作者： Rai, Shanvendra Paul, Rituparna Banerjee, Subhasish Meher, Preetisudha Department of Computer Science & amp Engineering National Institute of Technology Arunachal Pradesh Jote Itanagar Arunachal Pradesh Papumpare791113 India Department of Electronics & amp Communication Engineering National Institute of Technology Arunachal Pradesh Jote Itanagar Arunachal Pradesh Papumpare791113 India

The Internet of Medical Things (IoMT) provides such flexibility in our society where anyone can get medical treatment at any time, from anywhere. IoMT is a type of network where different resource-constraint physiological sensors are deployed in and/or on the human body that connects with the internet through the Gateway node, for monitoring purposes. However, due to the open nature of communication in the IoMT;the security, and privacy of patients' sensed data is very challenging, and that needs to be addressed, because any modification or alteration to it may lead to putting the life of a patient in danger. In this context, Chunka et al. proposed an authentication and key agreement (AKA) scheme for IoMT and claimed that the scheme has many security features and is easy to deploy. Unfortunately, it came to notice during this research that the scheme is vulnerable to multiple attacks, including replay, insider, smart card loss, eavesdropping, and server spoofing attacks, additionally failing to establish the session key agreement. So to overcome this issue, an efficient and improved multi-factor lightweight mutual AKA scheme is proposed through this article by incorporating a PUF-enabled sensor node and smart card for the users. To prove the superiority of the proposed schemes and to demonstrate the security features, the scheme is verified by formal security proof using the ROR model and informal proof using the AVISPA tool kit. In the end, a comprehensive analysis covering security, performance, and a comparative evaluation with existing similar approaches along with the Chunka et al. scheme demonstrates that the suggested approach not only achieves a higher level of protection against commonly recognized threats but also maintains an economically efficient mechanism concerning sensor nodes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sensor nodes

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Ultra Energy Efficient Reliable and Robust DLJLFET-Based-PUF for IoT Devices 3rd

Ultra Energy Efficient Reliable and Robust DLJLFET-Based-PUF...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Rajan, Chithraja Kumar, Rakesh Panchore, Meena Singh, Jawar School of Computer Science and Engineering Ramdeobaba University Maharashtra Nagpur440013 India Department of Electronics and Communication Engineering National Institute of Technology Patna Bihar800005 India Department of Electronics and Communication Engineering Indian Institute of Technology Patna Patna India

ISBN: (纸本)9789819743582

Physically Unclonable Function (PUF) emerged as a prominent hardware security solution for Internet of Things (IoTs) devices. PUFs are also used for generating secret keys for cryptography and for low-cost authentication in the high security of integrated circuits (ICs). Some natural variations occur during the manufacture of ICs. These variations are inevitable and uncertain. The PUF generates a different and unique random number by using this variation as the output key for the same input, even though this variation in the output is very small. In this article, a detailed comparative performance analysis of junctionless field effect transistor (JLFET)-based PUF and dopingless JLFET (DLJLFET)-based PUF is presented. A PUF circuit is designed using a five-stage ring oscillator (RO). It is observed that the delay in DLJLFET-based RO is 61% lower and consumes 47% less energy than JFLET-based RO at a supply voltage of 0.5 V because JLFET shows a higher leakage current due to high doping in the channel. Further, we did Monte Carlo simulation for 100 runs and observed that DLJLFET-PUF gives more unique PUF as mean HD is close to 50% and frequency of oscillation of DLJLFET is about 2 times higher than JLFET-PUF so DLJLFET-PUF will take less time during encryption and decryption for incoming and outgoing data, thus the output key generation will be faster. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware security

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Blockchain Cloud Computing: Comparative study on DDoS, MITM and SQL Injection Attack

Blockchain Cloud Computing: Comparative study on DDoS, MITM ...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Nagendra Singh, Harsh Pratap Mishra, Anuprita Khare, Anula Swarnkar, Mamta Almas, Shekh Kulsum Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar505001 India Shri Vaishnav Vidyapeeth Department of Computer Science and Engineering Vishwavidyalaya Indore India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Technocrats Institute of Technology Department of Electrical & Electronics Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India Sage University Department of ECE Indore India

ISBN: (纸本)9798350374100

This research paper explores the potential of using blockchain technology in cloud computing security to mitigate DDoS, MITM, and SQL injection attacks. The paper discusses the traditional security measures used in cloud computing and how attackers can bypass these measures at each layer of the OSI model. The proposed solution suggests utilizing blockchain features at each layer of the OSI model to enhance cloud security measures. The paper provides a detailed explanation of how the proposed blockchain-based security measures can be used to prevent DDoS, MITM, and SQL injection attacks. It is crucial to remember that the suggested remedy has not been put to the test in real life, and more investigation and testing are required to confirm its viability and efficacy. Overall, the integration of blockchain technology into cloud computing security shows promising potential to enhance the security of cloud environments. © 2024 IEEE.

关键词： Blockchain

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Cross-Layer Optimized HARQ Protocol for Underwater Acoustic Wireless Sensor Networks Using Stochastic Network Calculus 2

Cross-Layer Optimized HARQ Protocol for Underwater Acoustic ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Sivajayaprakash, A. Sukumaran, Rajeev Kumar, C. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore641032 India

ISBN: (纸本)9798331534950

UAWSNs face challenges such as long propagation delays, limited bandwidth, and varying channel conditions. To solve these problems, we developed a new protocol called Multi- Hop Cross-Layer Optimized Hybrid Automatic Repeat Request (CLO-HARQ) suitable for UAWSNs. This protocol automatically changes the retransmission strategy and error correction redundancy based on real-time feedback from the physical and network layers. A unique feature of this protocol is the inclusion of environmental factors such as water salinity and temperature in the decision-making process. Also, using Stochastic Network Calculus (SNC), stochastic traffic characteristics predict single-hop and multi-hop communication delay (delay), energy usage (energy usage), and throughput. In the performance evaluation of CLO-HARQ, it has been shown to provide significant improvement in efficiency, throughput, and delay over existing HARQ methods. It is well-suited for long-term underwater monitoring and is a sustainable solution for energy-constrained sensor networks. © 2024 IEEE.

关键词： Network layers

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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SAHARA: A Cloud - based Remote Health - Vitals Monitoring System 2

SAHARA: A Cloud - based Remote Health - Vitals Monitoring Sy...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Gupta, Arbind Kumar Nagaraja, J. Padma, Chennagiri Rajarao Mahesh Kumar, N. Roopa, M.S. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Medical Electronics Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Electronics and Communication and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367461

The healthcare landscape in India, like in many countries, faces disparities between urban and rural areas. While major cities boast advanced medical infrastructure, rural regions struggle with limited healthcare access, insufficient doctor-to-patient ratios, and poor road connectivity. The COVID-19 pandemic has emphasized the importance of robust healthcare systems. This paper proposes a cost-effective telehealth system to bridge this urban-rural healthcare gap. This paper discusses the historical development of telehealth technology, highlighting its early phases, rapid development, and current technological maturity. It then presents a novel system model "Sahara", focusing on the integration of temperature and pulse sensors to record patient health vitals. The system utilizes portable devices equipped with sensors to collect patient health data, which is wirelessly transmitted to the cloud. Doctors and caregivers can access this data through a secure web interface for remote diagnosis and treatment. The system ensures a simple user interface, is independent of electricity, and provides a low-cost solution for aggregating patient data. By leveraging readily available technology, this system has the potential to significantly improve healthcare access in rural India. The paper also reviews the evolution of telehealth technologies, highlighting the challenges and opportunities in the field. The system transfers these physiological data to the cloud using a WiFi module and stores them in Amazon Web Services (AWS). The data is accessible to authorized doctors and caretakers through a web interface. The paper also describes the implementation details of the system, both from the front-end and back-end perspectives, including user registration, appointment scheduling, and data integration. Finally, it discusses the results of the system and its potential impact on rural healthcare in India, emphasizing the role of telemedicine in improving healthcare accessibility, redu

关键词： Electronic health record

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Development of IoT based Smart Campus using STM32 15

Development of IoT based Smart Campus using STM32

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raju, Leo Thirunavukkarasu, K. SSN College of Engineering Department of Electrical and Electronics Engineering Chennai India Thanthai Periyar Govt Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331300579

Over the past few decades, energy has been used mindlessly leading to many problems like reduction in supply, high power rates, increased carbon footprint, etc. Smart campuses, home automation, smart grid, etc. make living comfortable and economical. It optimizes energy consumption and is quite simple to operate too. This project proposes Internet of Things (IoT) based automation for smart car parking inside the campus, thus converting existing campus into smart campus. In this work, we have developed a prototype for smart campus using STM32 microcontroller. This microcontroller has better features compared to other contemporary microcontrollers. This prototype includes a smart car parking system which reduces traffic congestion and facilitates car parking inside the campus. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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Cyber-Physical Attack Detection in Additive Manufacturing: An LSTM-Based Autoencoder Approach Using Reconstruction Error Analysis from Side-Channel Monitoring

Cyber-Physical Attack Detection in Additive Manufacturing: A...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Chithras, T. Brijesh, K.J. Paul, M. Mano Raja Karthick, S. Bhuvanesh, A. Keerthanadevi, R. Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Gregorian Institute of Technology Department of Automobile Engineering Kottayam India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to identify cyber-physical threats in additive manufacturing systems, this study suggests a sophisticated technique that uses data from side-channel monitoring. Strong attack detection capabilities are guaranteed by the method's integration of many important approaches for pre-processing, analyzing, and classifying time-series data. In order to make the continuous time-series data more understandable and efficient to analyze, a pre-processing method based on defined Window Sliding (FWS) is used to divide the data into manageable windows of defined size. After that, we use Discrete Wavelet Transform (DWT) to get all the features out of each window, collecting important data from all the different frequency bands. Using Particle Swarm Optimization (PSO) to sift through the DWT coefficients and extract the most useful features improves classification performance by making sure that only the most informative characteristics are used. In order to train a deep learning (DL) approach that can identify anomalies via reconstruction errors, the optimal feature set is used. This technique is an LSTM-A autoencoder. We show that the suggested approach can differentiate between normal and attack cases with a 99% accuracy rate by applying it to the problem of side-channel attack detection in additive manufacturing. To protect cyber-physical systems from advanced cyberattacks, this method offers a scalable and adaptable solution while simultaneously increasing detection accuracy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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