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Development of an Automated Service Level Agreement Negotiation Framework for SaaS Cloud E-Marketplace

Development of an Automated Service Level Agreement Negotiat...

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Nnaji, Uche Azubuike, Ezenw Oke Adetiba, Emmanuel Akinola, Damola Department of Computer & Information Sciences Ota Nigeria Covenant University Department of Computer & Information Sciences Ota Nigeria Department of Electrical & Information Engineering Ota Nigeria

ISBN: (纸本)9798350358155

With the increasing number of Software-as-a-Service (SaaS) users, catering to their diverse needs while ensuring Quality of Service (QoS) has become imperative. Although SaaS providers regularly introduce new applications with varying offerings and QoS parameters, the current Cloud e-marketplace may overlook the individual QoS requirements of each user. To tackle this challenge, a Service Level Agreement (SLA) negotiation framework is employed for cloud service selection, ensuring SaaS user satisfaction, often facilitated by brokers. These brokers secure optimal offers from providers, taking into account the user's preferences. To initiate an agreement, brokers rank cloud providers, proceeding to negotiations only if the user rejects the top-rated provider's offer. Given the multitude of users and providers in today's cloud e-marketplace, selecting the most suitable provider and negotiating QoS parameters can be a complex task for brokers. This paper introduces a negotiation framework designed to enhance the customer satisfaction value for SaaS users through a service broker. Our proposed framework employs multi-agent systems (MAS) as the methodology, incorporating the level of competition, current negotiation time, and opportunities to formulate an improved negotiation model for SaaS users in the cloud marketplace. By adopting this proposed negotiation framework, SaaS users can acquire services from providers that precisely meet their requirements. Experimental results from simulations demonstrate that the proposed framework outperforms previous studies in terms of satisfaction level, response time, and negotiation success rate. © 2024 IEEE.

关键词： Marketplaces

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer systems Science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Enhancing Automated Android Application Security through Hybrid Static and Dynamic Analysis Techniques 6

Enhancing Automated Android Application Security through Hyb...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Manukulasooriya, G.E. Munasingha, R.S.I. Fernando, Harinda Uthpali, H.K.I. Yeshani, A.V.S.H. Siriwardana, Deemantha Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The rapid growth of mobile applications has led to serious security challenges, resulting in vulnerabilities. Automation in security testing methods is becoming popular, with the Automated Vulnerability Detection methodology being one such method. This work introduces a hybrid approach for detecting vulnerabilities in Android applications, combining static and dynamic analysis. The approach is accessible to both general users and expert developers, allowing for the identification of ingrained vulnerabilities in the source code. The hybrid model's effectiveness is assessed in various applications with different security flaws, providing a solid solution for automated vulnerability detection in Android applications, enhancing security and resilience in the mobile ecosystem. © 2024 IEEE.

关键词： Automation

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Cyber Induced Harmonic Instability of Inverter-Based Renewable Generators 24

Cyber Induced Harmonic Instability of Inverter-Based Renewab...

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24th EEEIC International Conference on Environment and electrical engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Zadsar, Masoud Vavdareh, Masoud Babaei Ghafouri, Mohsen Ameli, Amir Concordia University Department of Information and Systems Engineering Montreal Canada Lakehead University Department of Electrical Engineering Thunder Bay Canada

ISBN: (纸本)9798350355185

Extensive integration of inverter-based resources (IBRs) has introduced stability challenges to power grids due to their low-inertia nature and grid-following control configuration. To address these challenges, recent IEEE standards, e.g., IEEE 1547.1-2020, recommended grid-connected IBRs to provide grid-supporting functions, necessitating communication with utility control centers through various protocols. However, deployment of communication protocols exposes grid-connected IBRs to various threats originated from cyber-layer, endangering overall system stability. To make IBRs resilient to cyber-threats, knowledge about possible attacks and relevant consequences is required. On this basis, this paper emphasizes on vulnerability of grid-connected IBRs to a new family of resonance cyber-attacks. In this attack, the adversary induces harmonic instability by launching a resonance false data injection attack (FDIA) on the IBR's communication layer. To this aim, the attacker can take advantage of impedance-based models or frequency scanning approaches to determine the resonance frequency of the IBR with the grid. Through injecting sinusoidal noise within the resonance frequency range into the signals communicating between sensors or grid-supporting functions and IBRs, the adversary excites the resonance modes of the IBR with the grid, leading to instability of the inverter. The simulation results on a LCL-type grid-connected inverter demonstrated that this resonance FDIA can cause extensive current and voltage harmonic distortion at the inverter's point of common coupling (PCC), which can triggers protection devices and lead to inverter trip. © 2024 IEEE.

关键词： Smart power grids

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High-Throughput Key Switching Accelerator for Homomorphic Encryption

High-Throughput Key Switching Accelerator for Homomorphic En...

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2023 International Conference on IC Design and Technology, ICICDT 2023

作者： Wang, Zeyu Ikeda, Makoto The University of Tokyo Department of Electrical Engineering and Information Systems Japan

ISBN: (纸本)9798350319316

We propose an ASIC architecture for key switching in RNS-CKKS Homomorphic Encryption scheme. With the pipeline design, the frequent RAM access is avoided and high throughput is achieved. This key switching design is a general architecture for several operations in RNS-CKKS, such as reliearization, rotation and conjugation. Furthermore, we show the flexibility of the design in changing the degree of parallelism. Increasing parallelism leads to an almost quadratic growth in throughput, which further accelerates the calculation. Compared to previous design, the throughput is increased to 1.7 times for key switching operation. © 2023 IEEE.

关键词： Application specific integrated circuits

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Educational Data Analysis using Generative AI

Educational Data Analysis using Generative AI

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Berr, Abdul Leelaluk, Sukrit Tang, Cheng Chen, Li Okubo, Fumiya Shimada, Atsushi Department of Electrical Engineering and Computer Science Kyushu University Japan Graduate School of Information Science and Electrical Engineering Kyushu University Japan

With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades. © 2024 CEUR-WS. All rights reserved.

关键词： Students

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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