Though cloud computing has become an attractive technology due to its openness and services, it brings several security hazards towards cloud storage. Since the distributed nature of clouds is ...achieved through internetworking technologies, clouds suffer from all the vulnerabilities by which networking also suffers. In essence, data stored in clouds are vulnerable to attacks from intruders. But, no single technique can provide efficient intrusion detection. In this paper, we propose fuzzy self-classifying clustering based cloud intrusion detection system which is intelligent to gain knowledge of fuzzy sets and fuzzy rules from data to detect intrusions in a cloud environment. Its efficiency is explained by comparing with other three cloud intrusion detection systems. Using a standard benchmark data from a CIDD (Cloud Intrusion Detection Dataset), experiments are conducted and tested. The results are presented in terms of success rate accuracy.
Urban mobility attempts to combine payment systems asa service with mobility, which has been divided into several transportation segments, and offer door-to-door services to consumers. Demand ...forecasting in the transportation sector is usually done in pairs, based on origins and destinations. To be more precise, forecasts are made for the volume of container traffic, vehicle traffic, and passenger departure and arrival. The purpose of this work is to examine the literature on demand prediction forecasting in several transportation domains, including vehicle sharing, leased cars, bicycles, and public transportation. The novel assessment preferred research papers to applied machine learning, deep learning, neural networks and Quantum learning methods. The study justifies the difference between Quantitative and Qualitative demand prediction. This review examined in different levels such as forecasting methods, hybrid models and quantum machine learning methods. Each existing research works classified into algorithms, prediction and observed results in numerical. Finally, the survey effort to find the strengths and limitation of the prevailing past research approaches.
As the cloud infrastructure is simultaneously shared by millions of consumers, heinous use of cloud resources are also increasing. It makes ways to attackers to set up attacks by exploiting the ...vulnerabilities. And obviously, these attacks are leading to severe disasters as innocent consumers are unknowingly sharing cloud resources with harmful attackers. To prevent the occurrence of cloud attacks, attack graph based framework is proposed in this paper. Here, an attack path sketches an attack scenario by a streak of threats ranging in severity rating that shows how popular a particular cloud network service is in comparison. In a dynamic cloud environment, the proposed framework can disclose an optimal attack path thereby preventing cloud attacks. In cloud system the infrastructure is shared by potentially millions of users, which benefits the attackers to exploit vulnerabilities of the cloud. An instrument for analyzing multi-stage, multi-host assault scenarios in networks is the attack graph. It might not be possible for the administrator to patch every vulnerability n a large number of assault paths in an attack graph. The administrator might not be able to fix every vulnerability. To identify the most preferred or ideal assault path from a particular attack graph in a setting Ant Colony Optimization (ACO) algorithm is used.
The detailed analysis of security for the proposed scheme demonstrates that it is withstand against various attacks. 1.Introduction In the earlier stage of 90s, storage systems are maintained by the ...single server and controlled by various authentication and confidentiality schemes. Most of the cloud computing applications are designed with the combination of authentication and access control for user validation. Last few years, several research papers have been published by the scholars in the field of biometrics-based remote user authentication protocol using smart cards 11-24. ...they designed a new scheme that claims to overcome the security flaws of the two schemes and be secure to various attacks.
...the organizations are usually forced to use whatever packages are available with the provider, either at the expense of quality or at the expense of cost. The model is developed as an online ...scheduling algorithm for usage in multiple cloud environments. ...obtaining resources from the same cloud provider is not an optimal solution for this scenario. Equal and continually occurring time slots are grouped into separate sections.QoS pertaining to each of the time groups is identified, and continuous time-slots with similar QoS requirements are aggregated to form a single requirement unit.
Cloud computing started a new era in getting variety of information puddles through various internet connections by any connective devices. It provides pay and use method for grasping the services by ...the clients. Data center is a sophisticated high definition server, which runs applications virtually in cloud computing. It moves the application, services, and data to a large data center. Data center provides more service level, which covers maximum of users. In order to find the overall load efficiency, the utilization service in data center is a definite task. Hence, we propose a novel method to find the efficiency of the data center in cloud computing. The goal is to optimize date center utilization in terms of three big factors—Bandwidth, Memory, and Central Processing Unit (CPU) cycle. We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients. This approach indicates that the current cloud needs an order of magnitude in data center management to be used in next generation computing.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Face verification is by far the most popular biometrics technology used for authentication since it is noninvasive and does not require the assistance of the user. In contrast, fingerprint and iris ...identification technologies require the help of a user during the identification process. Now the technology behind facial recognition has been around for years but recently as its grown more sophisticated is applications have expanded greatly. These days a third-party service provider is often hired to perform facial recognition. The sensitivity of face data raises important privacy concerns about outsourcing servers. In order to protect the privacy of users, this paper discusses privacy-preserving face recognition frameworks applied to different networks. In this survey, we focused primarily on the accuracy of face recognition, computation time, and algorithmic approaches to face recognition on edge and cloud-based networks.
In this paper, a basic methodology with two essential strategies (basic analytical geometry and algebraic methods) is used to settle the organized vendor - buyer inventory issue. An incorporated ...vendor - buyer inventory framework with shortage determined logarithmically and mathematically. The proposed strategy approach yields the least of the coordinated absolute expense each year more effectively than the derivatives approach. What's more, for individuals without the foundation of derivatives, it is more valuable to decide the buyer's economic order.
Iot Based Power Monitoring System P M, Jaiganesh; B, Meenakshi Sundaram
International Journal of Computer Science and Engineering,
4/2021, Volume:
8, Issue:
4
Journal Article
Brain tumour (BT) detection involves the process of identifying the presence of a brain tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging techniques, such as ...MRI (Magnetic Resonance Imaging), CT (Computed Tomography), or PET (Positron Emission Tomography) scans. Early detection of BT is important and MRI is one of the primary imaging techniques used to diagnose and assess BT. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) have shown promising results in assisting with BT detection on MRI scans. This study designs an Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification (ELCAD-BTC) technique on MRIs. The presented system purposes to detect and classify various steps of BTs. The presented system contains a Gabor filtering (GF) approach to remove the noise and increase the quality of MRI images. Moreover, ensemble learning of three DL models namely EfficientNet, DenseNet, and MobileNet is utilized as feature extractors. Furthermore, the denoising autoencoder (DAE) approach can be exploited to detect the presence of BTs. Finally, a social spider optimization algorithm (SSOA) was carried out for the hyperparameter tuning of the DL models. For simulating the improved BT classification outcome, a brief set of simulations occur on BRATS 2015 database.
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