Analysis of Security Based Accessibility Control Models for Cloud Computing

Access control has become the most necessary requirement to limit unauthorized and privileged access to information systems in cloud computing. Access control models counter the additional security challenges like rules, domain names, job allocation, multi hosting and separation of tasks. This paper classifies the conventional and modern access control models which has been utilized to restrain these access flaws by employing a variety of practices and methodologies. It examine the frequent security threats to information confidentiality, integrity, data accessibility and their approach used for cloud solutions. This paper proposed a priority based task scheduling access control (PbTAC) model to secure and scheduled access of resources & services rendered to cloud user. PbTAC model will ensure the job allocation, tasks scheduling and security of information through its rule policies during transmission between parties. It also help in reducing system overhead by minimize the computation and less storage cost.

Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios.

Building a Cross-Border E-Commerce Ecosystem Model Based on Block Chain + Internet of Things

With the development of Internet technology, cross-border e-commerce in the form of “Internet + foreign trade” has come into being. However, in recent years, scholars have continuously raised the problem that the cross-border e-commerce ecosystem with cross-border e-commerce platform as the core and the new Internet technology has not been developed sympathetically. Therefore, by establishing a model of cross-border e-commerce ecosystem and new Internet technology (block chain + Internet of Things), this paper analyzes the stable evolution strategy of both for convergence development and proposes countermeasures for the convergence development of new Internet technology and cross-border e-commerce platform ecosystem of RFID technology logistics tracking + block chain authentic product traceability, taking vertical class cross-border e-commerce platform as an example. The simulation experiment results show that the solution proposed in this paper is feasible compared with traditional and has obvious advantages in data theft prevention, multiparty authentication, and saving system overhead to solve the calculation method of e-commerce sharing problem. Reference is provided for solving the e-commerce cyberculture problem. The process takes advantage of block chain decentralization and auditability from the relevant data analysis from the theoretical concept of big data, the current situation of cross-border e-commerce, and how big data can be applied and developed for cross-border e-commerce, in three major dimensions.

Genetic Hybrid Optimization of a Real Bike Sharing System

In recent years there has been a growing interest in resource sharing systems as one of the possible ways to support sustainability. The use of resource pools, where people can drop a resource to be used by others in a local context, is highly dependent on the distribution of those resources on a map or graph. The optimization of these systems is an NP-Hard problem given its combinatorial nature and the inherent computational load required to simulate the use of a system. Furthermore, it is difficult to determine system overhead or unused resources without building the real system and test it in real conditions. Nevertheless, algorithms based on a candidate solution allow measuring hypothetical situations without the inconvenience of a physical implementation. In particular, this work focuses on obtaining the past usage of bike loan network infrastructures to optimize the station’s capacity distribution. Bike sharing systems are a good model for resource sharing systems since they contain common characteristics, such as capacity, distance, and temporary restrictions, which are present in most geographically distributed resources systems. To achieve this target, we propose a new approach based on evolutionary algorithms whose evaluation function will consider the cost of non-used bike places as well as the additional kilometers users would have to travel in the new distribution. To estimate its value, we will consider the geographical proximity and the trend in the areas to infer the behavior of users. This approach, which improves user satisfaction considering the past usage of the former infrastructure, as far as we know, has not been applied to this type of problem and can be generalized to other resource sharing problems with usage data.

An Adaptive IP Hopping Approach for Moving Target Defense Using a Light-Weight CNN Detector

Scanning attack is normally the first step of many other network attacks such as DDoS and propagation worm. Because of easy implementation and high returns, scanning attack especially cooperative scanning attack is widely used by hackers, which has become a serious threat to network security. In order to defend against scanning attack, this paper proposes an adaptive IP hopping in software defined network for moving target defense (MTD). In order to accurately respond to attacker’s behavior in real time, a light-weight convolutional neural network (CNN) detector composed of three convolutional modules and a judgment module is proposed to sense scanning attack. Input data of the detector is generated via designed packets sampling and data preprocess. The detection result of the detector is used to trigger IP hopping. In order to provide some fault tolerance for the CNN detector, IP hopping can also be triggered by a preset timer. The CNN driving adaptability is applied to a three-level hopping strategy to make the MTD system optimize its behavior according to real time attack. Experiments show that compared with existing technologies, our proposed method can significantly improve the defense effect to mitigate scanning attack and its subsequent attacks which are based on hit list. Hopping frequency of the proposed method is also lower than that of other methods, so the proposed method shows lower system overhead.

Blockchain Network Propagation Mechanism Based on P4P Architecture

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

LTE network performance evaluation based on effects of various parameters on the cell range and MAPL

These days long term evaluation (LTE) is considered the common mobile technology around the world and there is a need to maximize the network performance to satisfy the increased demand in terms of the cell capacity and coverage. These are many parameters in the network configuration and in the surrounded environment, which have great effects on the network performance. Examples of parameters are the system overhead rations, the required capacity of the network, neighbor cell load, and link budget parameters. The determination of the optimum configuration parameters, which achieve the best network performance, is a main step in the planning process in addition to it is continuous step in network optimization phase. In this study, the effects of some parameters will be investigating to get the best parameters that achieve the best network performance in terms of capacity of the cells and coverage area. The study will start by discussing introduction about LTE network components and protocols, and then the main parameters of the protocols will be revising. The study will display the results of changing many parameters related to LTE protocols and surrounding environment parameters on the LTE network performance.

HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.

Throughput Analysis for Cooperative Cognitive Radio Networks using Cyclostationary Detection

Detection of primary users (PUs) in the presence of interference and noise and improvement of spectrum utilization is one of the aims of cognitive radio (CR). In this paper, a fully distributed cooperative spectrum sensing scheme based on cyclostationary features techniques is proposed. The primary signal detection is realized by an orthogonal frequency division multiplexing (OFDM) sensing algorithm; each secondary user (SU) makes decisions about the PU by exchanging their own measurements with the local neighbors. The distributed scheme is analyzed on a network layer, where the throughput performance is analyzed in terms of sensing accuracy, frame duration, and system overhead. An analytical expression for the SU throughput is derived, in addition to investigating the issue of trade offs between time and overhead. Simulation results showed the relationship between the throughput and the sensing time, the effect of increasing the number of SUs on the throughput, and the outcome of increasing traffic intensity on the system performance