Reham Arnous

Bio: Reham Arnous is an academic researcher from Mansoura University. The author has contributed to research in topics: Routing protocol & Computer science. The author has an hindex of 4, co-authored 7 publications receiving 42 citations.

Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment

Abstract: Trust management becomes an urgent requirement in the cloud environment and a trust relationship between service user and service provider is required. Trust is the estimation of the ability of cloud resources in completing a task based on some criteria such as availability, reliability, and resource processing power. In this paper, an enhanced QoS-based model for evaluating the trustworthiness of the cloud provider is introduced. The proposed model calculates the accumulative trust value which is updated dynamically at each transaction and reflects the current or latest transaction of the provider in the cloud. The trustworthiness of a cloud resource is evaluated based on its provider reputation history from user feedback ratings based on the covariance mathematical technique to evaluate the credibility of the user's feedback. The trustworthiness of a cloud resource is also evaluated by calculating the computing power of resources at run-time. Experimental results confirm the effect of user opinion and resources processing speed on trust value calculation, which in turn assesses the trustworthiness of the cloud provider. The simulation has been performed using the CloudSim with the platform Eclipse for developing the proposed model.

A Proposed Routing Protocol for Mobile Ad Hoc Networks

Abstract: Routing in wireless mobile ad hoc networks (MANETs) is a challenging task. Geographic routing protocols offer promising solutions for routing in MANETs. Their advantages are eliminating the need of topology storage and the associated costs. A disadvantage is that all nodes must be equipped with GPS receivers to be aware of their own positions which consume money and energy. Besides, GPS receivers may not work in areas that are mostly concentrated with computing devices. This work proposes a new routing algorithm that is suitable for network where some nodes may be aware of their position through GPS while others are not. In the proposed algorithm, routing decision is made by the combination of greedy forwarding mechanism and on-demand routing one. Packets are forwarded in greedy mode when position information is available and routed using a reactive on demand procedure when this information is missed. Simulation results show that the proposal achieves better performance compared to GPSR and the DSR protocols concerning end-to-end delay, throughput and packet delivery ratio

An optimized FP-growth algorithm for discovery of association rules

Abstract: Association rule mining (ARM) is a data mining technique to discover interesting associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM algorithm for compressing information in the tree structure. However, it tends to suffer from the performance gap when processing large databases because of its mining procedure. This study presents a modified FP-growth (MFP-growth) algorithm to enhance the efficiency of the FP-growth by obviating the need for recurrent creation of conditional subtrees. The proposed algorithm uses a header table configuration to reduce the complexity of the whole frequent pattern tree. Four experimental series are conducted using different benchmark datasets to analyze the operating efficiency of the proposed MFP-growth algorithm compared with state-of-the-art machine learning algorithms in terms of runtime, memory consumption, and the effectiveness of generated rules. The experimental results confirm the superiority of the MFP-growth algorithm, which focuses on its potential implementations in various contexts.

Improving the load balancing within the data network via modified AntNet algorithm

Abstract: AntNet is an agent based routing algorithm that is influenced from the unsophisticated and individual ant's emergent behavior. AntNet algorithm does not consider the effect of increasing the total numbers of routing packets (ants) moving inside the network which could contribute to congestion. This could eventually have a negative impact on the overall network performance. Moreover, AntNet addresses the problem of routing not load balancing as AntNet philosophy can lead to network congestion and create bottlenecks. The main objective of this work is modifying the AntNet algorithm to address its above mentioned drawbacks. This work measures the effect of increasing the number of ants on the average packet delay and network throughput; and modifying, accordingly, the ants' generation rate. Also we introduced a modified AntNet load- aware algorithm that improves AntNet in order to support load balancing. A complete network simulator is developed in C++ in order to simulate and evaluate the AntNet algorithm and the suggested modifications. Results showed that controlling and limiting the total number of routing packets (ants) moving inside the network decreased the average delay per packet and increased the network throughput. The proposed modified load aware algorithm achieves better results in terms of throughput and average queue length and average delay experienced per packet.

An Integrated Framework to Ensure Information Security Over the Internet

Abstract: Information security is not a new concept in the technological industry. Information security is one of the major study areas in computer information systems. Due to the increasing popularity and dependency over internet, the need for proper information security has increased. There are new security paradigms arising every day to increase the information security. Determining the quality and value of the information, to set the proper system qualities to implement proper security is of high importance. However, the requirement of a more effective solution to be implemented in order to prevent the increasing security threats against the information on internet. Information security requires the integration of different perspectives including mathematical evaluation, technical evaluation, economic perspective and social perspective. Hence, in this paper, we are proposing an integrated framework to ensure information security over the internet. In the proposed framework, we incorporate mathematical and logical analysis techniques by incorporating methodological strategies and procedures along with different analysis techniques which will have different factors causing risk as the input parameters and generates one output – risk, which is expressed as in terms of probability. Thus, the proposed framework for information security system stands above the existing systems in producing better results

Load Balancing of Nodes in Cloud Using Ant Colony Optimization

Abstract: In this paper, we proposed an algorithm for load distribution of workloads among nodes of a cloud by the use of Ant Colony Optimization (ACO). This is a modified approach of ant colony optimization that has been applied from the perspective of cloud or grid network systems with the main aim of load balancing of nodes. This modified algorithm has an edge over the original approach in which each ant build their own individual result set and it is later on built into a complete solution. However, in our approach the ants continuously update a single result set rather than updating their own result set. Further, as we know that a cloud is the collection of many nodes, which can support various types of application that is used by the clients on a basis of pay per use. Therefore, the system, which is incurring a cost for the user should function smoothly and should have algorithms that can continue the proper system functioning even at peak usage hours.

A binary Sine Cosine-Modified Whale Optimization Algorithm for Feature Selection

Abstract: The increase of many pillars within the dataset makes it needed to pick the best part of features. The feature selection approach directly influences the performance of the style in terms of integrity and computational information. The wrapper feature choice version deals with the function set to improve the category reliability. In this paper, a new wrapper feature selection binary formula is intended based upon the Sine Cosine Algorithm (SCA) and a modified Whale Optimization Algorithm (MWOA). This algorithm (Binary SC-MWOA) was associated with obtaining unassociated characteristics and selecting the optimum features. The proposed formula’s attractive outcomes reveal the algorithm’s performance for picking the best features. Ten different UCI Repository datasets are checked in the experiments.

Applications and Datasets for Superpixel Techniques A Survey

Abstract: The use of superpixels instead of pixels can significantly improve the speed of the current pixel-based algorithms, and can even produce better results in many applications such as robotics, remote sensing, industrial inspection, and medical diagnosis. Two main tasks related to vision could benefit from superpixels, named object class segmentation and medical image segmentation. In both cases, superpixels can increase performance significantly and also reduce the computational cost. In addition to superpixel applications, various datasets were employed for the evaluation of the superpixel algorithms. This work aims to survey the recent applications and the most common datasets that can be used based on superpixel techniques.

Integrating a High-Reliability Multicriteria Trust Evaluation Model with Task Role-Based Access Control for Cloud Services

Abstract: Cloud data storage is revolutionary because it eliminates the need for additional hardware, which is often costly, inconvenient, and requires additional space. Cloud data storage allows data owners to store large amounts of data in a flexible way and at low cost. The number of online cloud storage services and their consumers has therefore increased dramatically. However, ensuring the privacy and security of data on a digital platform is often a challenge. A cryptographic task-role-based access control (T-RBAC) approach can be used to protect data privacy. This approach ensures the accessibility of data for authorized consumers and keeps it safe from unauthorized consumers. However, this type of cryptographic approach does not address the issue of trust. In this paper, we propose a comprehensive trust model integrated with a cryptographic T-RBAC to enhance the privacy and security of data stored in cloud storage systems, and suggests that trust models involve inheritance and hierarchy in the roles and tasks of trustworthiness evaluation, where this study aims to identify the most feasible solution for the trust issue in T-RBAC approaches. Risk evaluations regarding other possible flaws of the design are also performed. The proposed design can decrease risk by providing high security for cloud storage systems and improve the quality of decisions of cloud operators and data owners.