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Modelling of intrusion detection using sea horse optimization with machine learning model on cloud environment

International Journal of Information technology (Singapore) 2024年第3期16卷 1981-1988页

作者： Jansi Sophia Mary, C. Mahalakshmi, K. Department of Computer Science and Engineering Idhaya Engineering College for Women Tamil Nadu Kallakurichi India Department of Computer Science and Engineering KIT-Kalaignarkarunanidhi Institute of Technology Tamil Nadu Coimbatore India

The growing reliance on cloud services necessitates a heightened focus on security measures to protect the integrity and privacy of crucial business data. Privacy preservation techniques, incorporating cryptographic and optimization methods, are instrumental in securely storing data in the cloud. The development of Intrusion Detection Systems (IDS) is crucial for pinpointing anomalies in data, playing a pivotal role in fortifying the reliability, confidentiality, and availability of cloud-based systems. The current surge in interest from research communities towards leveraging machine learning (ML) methods for IDS reflects a strategic shift in addressing anomaly detection within network traffic to enhance overall cloud security. Thus, this study introduces a sea horse optimization with deep echo state network-based intrusion detection (SHO-DESNID) method on the cloud environment. The goal of the SHO-DESNID technique is to accomplish security in the cloud environment via an intrusion detection process. To accomplish this, the SHO-DESNID technique undergoes a min–max normalization approach as a pre-processing step. Moreover, the SHO-DESNID approach uses the DESN model for the identification and classification of intrusions into multiple classes. To enhance the intrusion detection rate of the DESN method, the SHO algorithm is utilized for optimal hyperparameter selection. The simulation outcome of the SHO-DESNID system is tested on a benchmark IDS database and the experimental values state the supremacy of the SHO-DESNID technique compared with other approaches. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cloud computing Deep learning Intrusion detection system Machine learning Privacy Security

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Text recognition using improved dual attention based on textual double embedding network with aquila optimization algorithm

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International Journal of Information technology (Singapore) 2024年 1-15页

作者： Singhdev, Harsiddhi Gupta, Shruti Srivastava, Vivek Saxena, Apoorva Department of Computer Science and Engineering ABES Engineering College Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering Axis Institute of Technology & amp Management Uttar Pradesh Kanpur India

Text recognition is the process that changes an image of text into a system readable text format. Different approaches were suggested related to text detection but the existing methods accuracy is low and error rate is high. Therefore, a text recognition using Improved Dual-attention based on Textual Double Embedding (IDTDE) with aquila optimization algorithm is proposed in this manuscript for effective text recognition. In this method the input image is taken from several datasets. Usually, the images have some kind of noise in it, and to eliminate that, the Structural interval gradient filtering preprocessing technique is used. Then, the ternary pattern and discrete wavelet technique is use to extract best features. Next, residual-based temporal attention convolutional neural network is utilized for the text classification and character identification are effectively attained by and IDTDE network. The Aquila optimization algorithm is used to optimize the network for best extracted text as output. Experimental outcomes demonstrate that the introduced approach accomplishes the high accuracy rates of 98.14%, 98.89%, and 90.47% on the IIIT5K, ICDAR 2013, and ICDAR 2015 datasets, respectively, surpassing the performance of existing frameworks. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Aquila optimizer Character recognition Gradient filtering Improved self-attention Text recognition

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Robust Deep Transfer Learning Based Object Detection and Tracking Approach

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Intelligent Automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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A Hybrid Approach to Header Size and Forwarding Table Optimization in Segment Routing

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 275-278页

作者： Roy, Anushree Sarkar, Tania Singh, Pranav Kumar Maity, Ranjan Central Institute of Technology Kokrajhar Department of Computer Science and Engineering Kokrajhar783370 India

Segment Routing is a source routing paradigm that simplifies packet forwarding and management across networks. Two major challenges of segment routing are the limitation of header stack size and the size of the Forwarding Table specific to each node. It has been observed that there is a trade-off between these two factors. In this letter, we have addressed this trade-off and presented a mechanism to minimize the stack size as well as the forwarding table. Instead of individual Segment IDs used in traditional segment routing, unique Path IDs are considered. This reduces the size of the header stack to one irrespective of the number of segments in the path. To reduce the size of the forwarding table, a forwarding technique based on matrix operations is used. Our approach also works on the reliability issues in forwarding and guarantees 100% successful packet forwarding. © 2019 IEEE.

关键词： Eigenvalues and eigenfunctions

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Anonymous and robust biometric authentication scheme for secure social IoT healthcare applications

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Journal of engineering and Applied science 2024年第1期71卷 8页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

In the era of rapid technological advancement, the Internet of Things (IoT) has revolutionised healthcare through systems like the Telecare Medicine Information System (TMIS), designed to streamline patient-doctor interactions and enhance medical treatment. However, the transmission of sensitive patient data over inherently insecure Internet channels exposes it to a spectrum of security risks. Protecting patient medical privacy and ensuring system reliability necessitate mutual authentication between both patients and medical servers. TMIS relies on robust authentication mechanisms, and combining passwords and smart cards has been a recognised approach for mutual authentication. This research introduces an innovative three-factor authentication technique with perfect forward secrecy by leveraging the power of Elliptic Curve Cryptography (ECC) in tandem with smart cards. Additionally, we have incorporated biometric authentication with a Fuzzy Extractor technology to enhance the security and reliability of the system, setting a new standard for user authentication within the realm of Social IoT healthcare. The use of ECC in the method is justified due to its compact key size and robust security measures, making the solution both efficient and secure. The proposed method safeguards user privacy by permitting registered users to change their passwords without divulging their identity to the server. The Burrows–Abadi–Needham logic (also known as the BAN logic) serves as a proof-of-concept for the proposed scheme’s security. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computation cost and communication cost of up to 71.03% compared to the other four relevant techniques, making it more useful in real-world scenarios. © 2023, The Author(s).

关键词： Smart cards

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Large-Scale Traffic-Sign Detection and Recognition Using Deep Learning 10

Large-Scale Traffic-Sign Detection and Recognition Using Dee...

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

作者： Eeswara Manikanta Reddy, P. Muramalla, Pranav Sri Vidhya, S.R. Cruz Antony, J. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Automated traffic sign detection and identification plays an important position in site visitors sign stock control. The site visitors sign affords an accurate and timely way to manage inventory with minimum human attempt. In the computer imaginative and prescient network, site visitors sign popularity and identity is a nicely-researched trouble. Most of the prevailing strategies paintings well on visitor's indicators required for self-sustaining and advanced motive force help structures. However, this represents a extraordinarily small number of all site visitors signs (approximately 50 kinds out of numerous hundreds) and the performance of the last site visitors symptoms is essential to cast off guide paintings in coping with the listing of visitors symptoms. There is an open query., in this text we talk the trouble to identifying and also identifying a big variety of traffic sign types suitable for automating site visitors signal stock management. We undertake a Convolutional Neural Network (CNN) technique, Mask R-CNN, to deal with the entire detection and identity method through give up-to cease gadget studying. We advise some of upgrades that are evaluated in detecting site visitor's signs and improve the over all performance. This approach is used to discover 200 sorts of visitors symptoms represented in our new dataset. Results are reported on extra tough site visitors signal sorts that have no longer been taken into consideration in previous paintings. We gift an in depth analysis of a deep studying technique to locate site visitors signs and symptoms with big variations in look inside a category and display error quotes below 3% with the proposed technique, which has capability for realistic applications in site visitors signal inventory management. © 2024 IEEE.

关键词： Inventory control

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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Role of Digital Watermarking in Wireless Sensor Network

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Recent Advances in computer science and Communications 2022年第2期15卷 215-228页

作者： Kumar, Sanjay Singh, Binod K. Akshita Pundir, Sonika Joshi, Rashi Batra, Simran Department of Computer Science and Engineering National Institute of Technology Jamshedpur India Department of Computer Science and Engineering Women Institute of Technology Deharadun India

WSN has been exhilarated in many application areas such as military, medical, envi-ronment, etc. Due to the rapid increase in applications, it causes proportionality to security threats because of its wireless communication. Since nodes used are supposed to be independent of human reach and dependent on their limited resources, the major challenges can be framed as energy consumption and resource reliability. Ensuring security, integrity, and confidentiality of the transmitted data is a major concern for WSN. Due to the limitation of resources in the sensor nodes, the traditionally intensive security mechanism is not feasible for WSNs. This limitation brought the concept of digital watermarking in existence. Watermarking is an effective way to provide security, integrity, data aggregation and robustness in WSN. In this paper, several issues and challenges, as well as the various threats of WMSN, is briefly discussed. Also, we have discussed the digital watermarking techniques and its role in WMSN. © 2022 Bentham science Publishers.

关键词： Energy utilization

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Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

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Review and Explore the Transformative Impact of Artificial Intelligence (AI) in Smart Healthcare Systems

Review and Explore the Transformative Impact of Artificial I...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Nagar, Sneha Patidar, Mukesh Sheikh, Rashid Singh, Sudhanshu Kashiv, Ashutosh Jain, Ankit Namdeo, Shravan Kumar Paranjpe, Pranav Mandloi, Devendra Singh Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Vadodara India Acropolis Institute of Technology and Research Department of Computer Science & Engineering Indore India Indian Institute of Technology Indore Department of Electronics and Communication Engineering Indore India

ISBN: (纸本)9798350388800

Because of its high level of complexity and data collection capabilities, the healthcare industry employs artificial intelligence (AI) more than other sectors. Artificial intelligence is becoming an increasingly important part of our daily routines. AI's features include extremely low error rates, constant accessibility, quick analysis, and real-time data dissemination. This paper examines the crucial role of artificial intelligence in intelligent healthcare systems, with a particular emphasis on enhancing healthcare delivery. Artificial intelligence in healthcare is rapidly evolving, with the potential to significantly enhance patient care, diagnosis, treatment, and cost distribution. It also presents issues of transparency, honesty, and availability. This study will look into the benefits, drawbacks, and other aspects of artificial intelligence in intelligent healthcare systems. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Ethical Considerations Explainable AI (XAI) Healthcare Resource Allocation Healthcare Systems Smart Treatment Planning

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