In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. To the best of our knowledge, no research to date has been conducted to assist network forensics investigators and cloud service providers in finding an optimal method for investigation of network vulnerabilities found in cloud networks. To this end and in this paper, the state-of-the-art C-NFMs are classified and analyzed based on the cloud network perspective using SWOT analysis. It implies that C-NFMs have a suitable impact on cloud network, which further requires for reformation to ensure its applicability in cloud networks.
Abstract
This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per
... Show MoreAn Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to
... Show Moreتم تقييم المعرفة والوعي تجاه داء الكريبتوسبوريديوس في هذه الدراسة خلال الفترة من أبريل 2009 حتى يونيو 2011 ، من بين 188 شخصًا من كلا الجنسين تم تقسيمهم إلى مجموعات ومجموعات فرعية مختلفة على النحو التالي المجموعة 1: تشمل 48 طبيبًا من مختلف المستشفيات والرعاية الصحية الأولية مراكز في بغداد تم تقسيم هذه المجموعة إلى 30 طبيبًا متخصصًا و 18 طبيبًا ممارسًا عامًا. المجموعة الثانية: تضم 45 عضو هيئة تدريس من قسم الأحياء ، قس
... Show MoreOnline learning is not a new concept in education, but it has been used extensively since the Covid-19 pandemic and is still in use now. Every student in the world has gone through this learning process from the primary to the college levels, with both teachers and students conducting instruction online (at home). The goal of the current study is to investigate college students’ attitudes towards online learning. To accomplish the goal of the current study, a questionnaire is developed and adjusted before being administered to a sample of 155 students. Additionally, validity and reliability are attained. Some conclusions, recommendations, and suggestions are offered in the end.
Knowledge and awareness towards cryptosporidiosis were assessed in this study during the period from April 2009 till June 2011, among 188 person of both gender which were divided in different groups and sub groups as following Group 1: include 48 physician from different hospitals and primary health care centers in Baghdad this group were sub divided into 30 Specialist doctors and 18 General practitioners doctors. Group 2 : include 45 teaching staff member from Biology department , Biotechnology department in University of Baghdad and AL-Nahrain University as well as teaching staff member from the college of medicine – University of Baghdad and University of Mustansiryah, this group were sub divided into 9 Ph.D and 36 M.Sc. + B.Sc. me
... Show MoreThis paper deals with the nations of British American Struggle in Caribbean. It
explains British Navy attitude of American expansion in Caribbean. Then continuation of
American expansion in the same place and Britain failure to limit it. This paper high lights the
beginning of acceptance between Britain's and United state, especially after British
submission to United States in Caribbean. Then we study the Anglo – American harmony and
the affection on the Anglo – American relation.
This paper concludes that American influences in Americans which is a truth.
This paper presents a minimum delay congestion control in differentiated Service communication networks. The premium and ordinary passage services based fluid flow theory is used to build the suggested structure in high efficient manage. The established system is capable to adeptly manage both the physical network resource limitations and indefinite time delay related to networking system structure.
The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA
... Show MoreMelanoma, a highly malignant form of skin cancer, affects individuals of all genders and is associated with high mortality rates, especially in advanced stages. The use of tele-dermatology has emerged as a proficient diagnostic approach for skin lesions and is particularly beneficial in rural areas with limited access to dermatologists. However, accurately, and efficiently segmenting melanoma remains a challenging task due to the significant diversity observed in the morphology, pigmentation, and dimensions of cutaneous nevi. To address this challenge, we propose a novel approach called DenseUNet-169 with a dilated convolution encoder-decoder for automatic segmentation of RGB dermascopic images. By incorporating dilated convolution,
... Show MoreThis research is devoted to investigating the thermal buckling analysis behaviour of laminated composite plates subjected to uniform and non-uniform temperature fields by applying an analytical model based on a refined plate theory (RPT) with five unknown independent variables. The theory accounts for the parabolic distribution of the transverse shear strains through the plate thickness and satisfies the zero-traction boundary condition on the surface without using shear correction factors; hence a shear correction factor is not required. The governing differential equations and associated boundary conditions are derived by using the virtual work principle and solved via Navier-type analytical procedure to obtain critica
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