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KeywordCatalyst network switching architecture
Nexus network switching architecture
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AbstractThis project is targeted to compare two major switching architectures provided by CISCO. CISCO is a network device manufacturer who has contributed to networking world by inventing many networking protocols which are used to improve the network performance and network health. In this document the switching architectures CATALYST and NEXUS are compared. All the available features in each architectures are listed and working of the supported protocols is explained in detail. The document also considers three network scenarios and explains which architecture is best suited and explains why in detail.
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5G Networks Security: Attack Detection Using the J48 and the Random Forest Tree ClassifiersKholidy, Hisham A.; Steele II, Bruce; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)5G is the next generation of cellular networks succeeding and improving upon the last generation of 4G Long Term Evolution (LTE) networks. With the introduction of 5G comes significant improvements over the previous generation with the ability to support new and emerging technologies in addition to the growth in the number of devices. The purpose of this report is to give a broad overview of what 5G encompasses including the architecture, underlying technology, advanced features, use cases/applications, and security, and to evaluate the security of this new networks using existing machine learning classification techniques such as The J48 Tree Classifier and the Random Forest tree classifier. The evaluation is based on the UNSW-NB15 dataset that was created at the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) at the University of New South Wales. Since 5G datasets have yet to have been created, there is no publicly available dataset for the 5G systems. However, While the UNSW-NB15 dataset is built using a standard wireless computer network, we will use it to simulate the device-to-device (D2D) connections that 5G will support. In the case with the UNSW dataset, the J48 tree classifier fits more accurately than the Random Forest classifier. The J48 tree classifier achieved an 86.422% of correctly classified instances. On the other hand, the Random Forest tree classifier achieved 85.8451% of correctly classified instances.
Creating a mesh sensor network using Raspberry Pi and XBee radio modulesForcella, Michael (2017-05)A mesh network is a type of network topology in which one or more nodes are capable of relaying data within the network. The data is relayed by the router nodes, which send the messages via one or more 'hops' until it reaches its intended destination. Mesh networks can be applied in situations where the structure or shape of the network does not permit every node to be within range of its final destination. One such application is that of environmental sensing. When creating a large network of sensors, however, we are often limited by the cost of such sensors. This thesis presents a low-cost mesh network framework, to which any number of different sensors can be attached. The hardware configuration is detailed in such a way that anyone with a modest understanding of technology will be able to reproduce it. The software setup required by the user has also been minimized and clearly documented. Details specific to the user's setup can be entered into a configuration file and the majority of software scripts are scheduled to run automatically via Linux Cron jobs. I conclude by outlining several potential modifications to the framework, including further automation of the software setup, inclusion of additional hardware, and alternate methods for downloading data from the network.
A Technology Case Study on Integrating Open Stack with SDN for Internet Connectivity using BGPGonuguntla, Raja Bhushan Rao; Hash, Larry; Advisor (2016-12)There were many developments in Internet usage, which resulted in significant increase in Internet routing. With existing networking infrastructure, it is difficult to meet these requirements and causing more cost and less performance. Since network devices are hardware modules, processing them requires more power and more memory. However, if network protocols are developed using software modules, flexibility can be achieved in various programming applications and reduces dependency on hardware. The concept of using networking protocols as a software module can be explained using “Software Defined Networking (SDN).” With SDN, existing infrastructure can be integrated with various applications and centralized control protocols can be developed. One of the key components of SDN is integrating with Cloud Computing, where many applications can be built, which can be used for on-demand services. Integrating cloud computing with SDN will create dynamic networks and reduces infrastructure costs. In this paper, a case was considered for providing better internet connectivity by building public & private networks using Open source cloud technology (OpenStack) and existing distribution environments. For connectivity, BGP was used as routing protocol as it is known to be well- suited for such environments. Both public and private networks were integrated with SDN for centralized control. OpenStack was used to build various network topologies using different plugins through SDN controller. This method allowed to develop SDN controller with global view of OpenStack networks. The same controller was connected to distributed layers using Open Flow protocol. Since, both OpenStack and distributed networks were attached to SDN controller, centralized control of network protocols could be achieved. This model of centralized networks could be very useful in reducing costs and improving network efficiency, especially in large scale deployments.