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Cybersecurity: Threats, Requirements, Deployments, Techniques, system Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

引用

Smart Devices (ICSD), International Conference on

作者： Ajay Yadav Vijay Karnatak Amit Kumar Mishra Shalini Verma Pallaw Singh Aswal Neha Tripathi University Institute of Computing Chandigarh University Mohali Punjab India Dept. of Computer Science & Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Doon Group Of Institution Rishikesh India Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Tehri India Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users’ data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided.

关键词： Authorization Smart cities systems architecture Cyberspace Loss measurement Fraud computer security Protection Smart devices Business

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Quantum transport simulation of a mid-wave infrared Ga-based type-II superlattice for curved focal-plane arrays 13

Quantum transport simulation of a mid-wave infrared Ga-based...

引用

Infrared Sensors, Devices, and Applications XIII 2023

作者： Glennon, John Bellotti, Enrico Div. of Material Science and Engineering Boston University BostonMA02215 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02215 United States

ISBN: (纸本)9781510665880

Type-II strained-layer superlattices have theoretically predicted advantages that make them an attractive material system for use in infrared technology. However, the influence of structural disorder has been demonstrated to negatively impact the performance of these materials. A recent development in infrared technology is the use of curved focal-plane arrays which promise improved uniformity of sensitivity and a reduction in the optical complexity of the device. Curving of the wafer introduces mechanical strain into the absorber layer which can also impact the vertical carrier mobility. This work reports on an analysis of the influence of external strain and disorder on the vertical hole mobility in curved focal-plane arrays utilizing a mid-wave InAs/GaSb absorber layers. A series of quantum transport calculations is performed over a range of strain configurations for two different structures: an ideal superlattice and a superlattice with graded interfaces and disorder. Finally, the impact these effects have on the quantum efficiency of a hypothetical curved-FPA is approximated, and a potential avenue for improved device design is suggested. © 2023 SPIE. All rights reserved.

关键词： Quantum efficiency

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

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2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Reddy, Arra Keerthan Vardhan Rapeti, Harsha Reddy, Doduguru Amarnath Srinivas, M. Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems © 2025 IEEE.

关键词： Deep neural networks

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Investigating the Effectiveness of Algorithms in Serverless Cloud Computing

Investigating the Effectiveness of Algorithms in Serverless ...

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2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Jindal, Mayank Mimani, Sushant Pillai, Saniaikanth E Vadakkethil Somanathan Jiwani, Nasmin Kiruthiga, T. San Jose CA United States GoDaddy WA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Server less cloud computing has won vast recognition in current years because of its capability to provide green and price-effective solutions for numerous computing needs. On this model, cloud carriers manage the scaling and provisioning of sources, permitting users to consciousness totally on their application code. As an end result, server less computing gives an appealing opportunity to standard server-primarily based computing, in which customers needed to control the whole infrastructure themselves. However, with the rise of server less computing, there was a growing challenge about the effectiveness of algorithms on these surroundings. Traditional algorithms are designed to run on a set of resources, which may not translate properly to the dynamic and elastic nature of server less structures. This has caused a want for studies in developing algorithms mainly tailor-made for server less computing. One fundamental assignment on this place is the optimization of resources and price inside the server less environment. Since customers simplest pay for the resources they use, it's far critical to layout algorithms that minimize useful resource consumption and value. Previous research has already indicated that conventional algorithms that rely upon pre-determined aid allocation won't carry out nicely within the server less model. Moreover, server less systems additionally introduce new challenges in phrases of useful resource availability, network latency, and processing delays due to the pay-in keeping with-use model. © 2024 IEEE.

关键词： Cloud computing

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Developing a Face Aging Model Using Face Synthesis Techniques

Developing a Face Aging Model Using Face Synthesis Technique...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Priyanka Kaushik Zakiya Manzoor Khan Muntaha Manzoor Khan Beenu Mago K. Jeevanadan Shivansh Dwivedi Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science Lovely Professional University Punjab India Dept. of Computer Science NIILM University Haryana India Dept. of Computer Science Skyline University College Sharjah UAE Dept. of Civil Engineering Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Regarding the realm of advancing technology, the subsequent development of face aging applications has garnered substantial interest. This research examines face synthesis techniques, with specific emphasis for the various practical usage of computational adversarial networks (GANs). The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age-progressed face images. The project assimilates Python programming, convolutional neural networks (CNNs), and several other machine learning and image processing proceeds, advancing in qualitative data from Kaggle. The goal of our project is to advancement of face-aging technology over the use of this multifaceted method, with the possible uses in amusement, forensic science, and technological process.

关键词： Ethics Technological innovation Forensics Aging Generative adversarial networks Generators Convolutional neural networks Faces Stress Python

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FasNet: An Advanced Deep Learning Model for Liver Tumor Segmentation

FasNet: An Advanced Deep Learning Model for Liver Tumor Segm...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Al Amin Md. Sanaul Haque Shakib Imtiaz Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

A critical focus area in medical imaging is liver tumors, which present significant challenges due to their high fatality rate, rapid progression, and severe impact on public health. Accurate detection and segmentation of liver tumors play an essential role in deciding on the proper treatment options and assessing the risk of liver resection. This study introduce a hybrid deep learning model named FasNet, designed to enhance the accuracy of liver tumor segmentation. FasNet combines the strengths of the ResNet-50 and VGG-16 architectures, incorporating 3D convolutional layers to process volumetric data from liver CT scans more effectively than traditional 2D methods. This 3D approach enables the model to capture complex spatial relationships and contextual information within liver images. FasNet leverages VGG-16's capabilities for fine-grained feature extraction and integrates the residual learning approach from ResNet-50 to address the vanishing gradient problem. Our extensive evaluation of FasNet using a large liver imaging dataset demonstrates its high accuracy and effectiveness, highlighting its potential for application in clinical settings where precise tumor segmentation is crucial for optimal patient care. FasNet represents a significant advancement in medical imaging with dice score of 0.854, precision as 85.1%, recall as 81% and Fl-score as 99.5%, offering new opportunities for improving diagnostic methods and ultimately enhancing patient outcomes in the fight against liver disease.

关键词： Deep learning Image segmentation computer vision Accuracy Three-dimensional displays Liver computer architecture Feature extraction Tumors Biomedical imaging

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Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

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A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： S Bhuvaneswari A Abirami R P Suvetha Sri R Srinithy R Swathi Faculty of dept. of Computer Science and Engineering Easwari Engineering College Chennai India Student of dept. of Computer Science and Engineering Easwari Engineering College Chennai India

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. Data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues.

关键词： Wireless communication Wireless sensor networks Temperature distribution Clustering algorithms Throughput Data models Delays

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Thermal Error Modelling using Features selected from Particle Swarm Optimization 5

Thermal Error Modelling using Features selected from Particl...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Ajith Kumar, B. Nayana, B.R. Shashank, P. M. S. Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Bengaluru India Ace Designers Ltd. Research and Technology Development Group Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528539

computer Numerical Control (CNC) machines are employed in various industrial manufacturing processes to produce precise components. The CNCs are prone to thermal deviations from the heat generated during machining. Axis motors, rotary bearings, and spindle motors are the main sources of heat. In this context, temperature sensitive areas (TSAs) apply to these locations. The accuracy of the machining process is directly affected by the heat-induced deviation between the tool and the workpiece. The objective is to minimize these deviations between the tool and the workpiece. The most challenging aspect of creating thermal error compensation models is figuring out which TSAs to use. This work proposes employing particle swarm optimization (PSO), a computational intelligence (CI) technique, to choose significant features. First, information about the machine's temperature and thermal deviation is collected. Second, the outliers are eliminated by cleaning the data. Following this, PSO optimizes the error band to identify which TSAs are significant. Advanced machine learning (ML) models like polynomial regression (PR) and random forest (RF) regression are trained utilizing these TSAs as features. The proposed approach is compared with matching models developed with features selected through the brute force (BF) approach. While PSO-RF outperformed BF-RF in providing 16.60% error reduction with different features, PSO-PR and BF-PR provided 47.12% error band reduction with similar features. Based on the findings, it can be concluded that PSO-PR can be used on the machine and that PSO outperforms BF in terms of identifying the ideal subset of influential features. © 2024 IEEE.

关键词： Error compensation

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Design and Development of an IoT Based Smart Vaccine Carrier and Real-Time Monitoring system

Design and Development of an IoT Based Smart Vaccine Carrier...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Raisa T. Hasanat Nafees Mansoor Nabeel Mohammed Mohammad S. Rahman Sifat Momen Mirza Rasheduzzaman EA Technology Capenhurst United Kingdom Dept. of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering United International University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Vaccination stands as the most reliable defense against a spectrum of life-threatening diseases. However, the eq-uitable distribution of vaccines in developing and least-developed nations face formidable challenges due to the absence of efficient and transparent cold chain management systems. This article introduces a real-time data-centric monitoring system designed to track the distribution and transportation of vaccines. Additionally, this research presents an energy-efficient vaccine carrier based on thermoelectric cooling, boasting minimal power consumption requirements. This innovative carrier ensures the safe transportation of vaccines even to the most remote regions. Moreover, the proposed system regulates the temperature and humidity of individual carriers by continuously monitoring sensor data. Furthermore, the system includes real-time tracking and reporting features. Anticipated outcomes include a significant increase in vaccination coverage in remote areas and enhanced transparency in the distribution process.

关键词： Temperature sensors Temperature distribution Power demand Cold chain Transportation Humidity Real-time systems Vaccines Reliability Monitoring

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