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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Analyzing Multi-Vector Ransomware Attack on Accellion File Transfer Appliance Server 7

Analyzing Multi-Vector Ransomware Attack on Accellion File T...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Kiesel, Karl Deep, Tom Flaherty, Austin Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (纸本)9789532901160

The aim of this paper is to analyze the Accellion File Transfer Attack. In December 2020, a group of malicious actors breached the Accellion FTA system. The FTA system is used to transfer mass amounts of data quickly and efficiently between multiple systems. Once Accellion's appliance was breached, malicious actors copied data and threatened to release the data onto the internet if not paid a ransom. In order to prevent this from taking place, Accellion, as well as multiple client companies, assisted breached establishments with defense solutions, customer support, and ransom advice. Our findings indicate that Accellion was breached through four separate exploits. The main hacking methodology used was an SQL injection. Once into the system, attackers could view which transferred data was sensitive due to it being flagged with a special 'sensitive' mark. Our findings are valuable because they express how companies should go about handling ransomware attacks. Our findings also indicate a solution for this type of attack and how a company should respond if they are faced with a ransomware attack. © 2022 University of Split, FESB.

关键词： Malware

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Expert System for Kubernetes Cluster Autoscaling and Resource Management 4

Expert System for Kubernetes Cluster Autoscaling and Resourc...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Samaratunge Arachchillage, Udara Srimath S. Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools' failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to configure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes. © 2022 IEEE.

关键词： Expert systems

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Stress detection system for social media users 7

Stress detection system for social media users

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Febriansyah, Mochamad Rizky Nicholas Yunanda, Rezki Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency - inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking 2

Autonomous Number Plate Detection System for Vehicle Identif...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nareddy, Neha Sri Polneni, Abhinaya Baithi, Rekha Kunchamwar, Pratyusha Suvarna, Sunil Kumar Battu, Durgabhavani Budda, Naveena Agarwal, Vikas Munimanda, Premchander AGVs Group Hyderabad India Malla Reddy Engineering College for Women Department of Information Technology Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta United States School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (纸本)9798350366570

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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A Case Study of Massive API Scrapping: Parler Data Breach After the Capitol Riot 7

A Case Study of Massive API Scrapping: Parler Data Breach Af...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Redding, David Ang, Jian Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (纸本)9789532901160

After the United States Capitol Hill Riots, there was a massive API scraping of Parler, an open social media platform, which resulted in 70 terabytes of user data being collected. The data breach, a serious confidential personal data leak, was not performed illegally. This paper analyzes the data breach and its impact in depth. The breach was a result of a hacktivist going with the alias @donk_enby, performing a massive API scraping of Parler's servers. The scraping took metadata from user's public, private, and previously deleted posts, uploaded to Parler's servers. Parler had failed to clear the metadata of these posts. The metadata contained names, dates, locations, and other data about the users who posted content to Parler's site. Over 70,000 GPS locations of Parler's users have been uncovered including users' private properties. These locations have also been used to tie citizens to the Capitol Riots if they uploaded any content about the riot from that day. Forms containing government identification of users were also leaked from Parler's servers that were used for account verification. This paper demonstrate background on the events leading up to, including, and following the Capitol Riots. The paper also examine the hacktivist's methodology for performing the API scraping and discuss possible defensive strategies such as API rate limiting, API request sanitation, and API call authorization. © 2022 University of Split, FESB.

关键词： Metadata

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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