Networking based projects free download

This project is aimed at developing an Online Intranet College Management System CMS that is of importance to either an educational institution or a college. The system CMS is an Intranet based application that can be accessed throughout the institution or a specified department.

This system can be used as an attendance monitoring system for the college. Attendance and marks will be updated by staff. An Institutes managing different branches at several locations. They want to maintain the accountant salary and students personal and payment details centrally. Student can pay their fees Online by using this portal. Online classifieds are the need of the hour as a user can become both buyer and seller on the click of a button.

More over since it is published online, any person can see it sitting anywhere in the world. All it takes is basic computer knowledge to browse this site. In the existing system, interaction between a buyer and seller is limited and the buyer may only have a vague idea of the product they may be purchasing. The proposed system brings in more clarity to the present scenario, not only the consumer will get all the info of the product or service on one click, they can always analyze similar type of products and find out which is the better one.

Since there will be competition the price will come down and the quality of product will go up. This website is published by a member of the Our Team. If you like my site, then you can always drop in an appreciation to me. Click here. On the one hand, traditional privacy-preserving learning approaches rely on heavy cryptographic primitives on training data, in which the learning speed is dramatically slowed down due to computation overheads.

On the other hand, complicated architectures of distributed system prevent existing solutions from being deployed in practical scenarios. In this paper, we propose a novel efficient privacy-preserving machine learning scheme for hierarchical distributed systems.

With the study of different scenarios, the proposed scheme not only reduces the overhead for the learning process but also provides the comprehensive protection for the hierarchical distributed system. Extensive real-world experiments are implemented to evaluate the privacy, efficacy, and efficiency of our proposed schemes. As a safety-critical system, the rapid response from the health care system is extremely important.

To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds.

However, these fog devices generate huge amount of sensor data. Designing a specific framework for fog devices to ensure reliable data transmission and rapid data processing becomes a topic of utmost significance. Functionalities of REDPF include fault-tolerant data transmission, self-adaptive filtering and data-load-reduction processing.

Specifically, a reliable transmission mechanism, managed by a self-adaptive filter, will recollect lost or inaccurate data automatically. Then, a new scheme is designed to evaluate the health status of the elderly people. Through extensive simulations, we show that our proposed scheme improves network reliability, and provides a faster processing speed. As now a fundamental commodity in our current information age, such big data is a crucial key to competitiveness in modern commerce.

In this paper, we address the issue of privacy preservation for data auction in CPS by leveraging the concept of homomorphic cryptography and secured network protocol design.

Specifically, we propose a generic Privacy-Preserving Auction Scheme PPAS , in which the two independent entities of Auctioneer and Intermediate Platform comprise an untrusted third-party trading platform. Via the implementation of homomorphic encryption and one-time pad, a winner in the auction process can be determined and all bidding information is disguised.

Yet, to further improve the security of the privacy-preserving auction, we additionally propose an Enhanced Privacy-Preserving Auction Scheme EPPAS that leverages an additional signature verification mechanism.

The feasibilities of both schemes are validated through detailed theoretical analyses and extensive performance evaluations, including assessment of the resilience to attacks. In addition, we discuss some open issues and extensions relevant to our scheme.

Such phenomenon poses tremendous challenges to data centers with respect to enabling storage. In this paper, a hybrid-stream big data analytics model is proposed to perform multimedia big data analysis.

This model contains four procedures, i. Specifically, an innovative multi-dimensional Convolution Neural Network CNN is proposed to assess the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm.

In order to ensure video quality, minimal correlation and minimal redundancy MCMR are combined to optimize the decision-making algorithm.

Simulation results show that the amount of processed video is significantly reduced, and the quality of video is preserved due to the addition of MCMR. The simulation also proves that the proposed model performs steadily and is robust enough to scale up to accommodate the big data crush in data centers. By deploying a gateway anti-phishing in the networks, these current hardware-based approaches provide an additional layer of defense against phishing attacks.

However, such hardware devices are expensive and inefficient in operation due to the diversity of phishing attacks. With promising technologies of virtualization in fog networks, an anti-phishing gateway can be implemented as software at the edge of the network and embedded robust machine learning techniques for phishing detection.

In this paper, we use uniform resource locator URL features and web traffic features to detect phishing websites based on a designed neuro-fuzzy framework dubbed Fi-NFN. Based on the new approach, fog computing as encouraged by Cisco, we design an anti-phishing model to transparently monitor and protect fog users from phishing attacks.

The experiment results of our proposed approach, based on a large-scale dataset collected from real phishing cases, have shown that our system can effectively prevent phishing attacks and improve the security of the network.

It is always critical for participants to consume as little energy as possible for data uploading. However, simply pursuing energy efficiency may lead to extreme disclosure of private information, especially when the uploaded contents from participants are more informative than ever. In this paper, we propose a novel mechanism for data uploading in smart cyber-physical systems, which considers both energy conservation and privacy preservation.

The mechanism preserves privacy by concealing abnormal behaviors of participants, while still achieves an energy-efficient scheme for data uploading by introducing an acceptable number of extra contents. To derive an optimal uploading scheme is proved to be NP-hard. Accordingly, we propose a heuristic algorithm and analyze its effectiveness.

The evaluation results towards a real-world dataset demonstrate that the results obtained through our proposed algorithm is comparable with the optimal ones. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge.

We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge e.

There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations i. Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment.

Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i. Therefore, Sybil detection in social networks is a fundamental security research problem. Structure-based methods have been shown to be promising at detecting Sybils. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and they are not robust to noisy labels.

LBP-based methods are not scalable, and cannot guarantee convergence. SybilSCAR maintains the advantages of existing methods while overcoming their limitations. Under our framework, these methods can be viewed as iteratively applying a local rule to every user, which propagates label information among a social graph.

Breakthrough in this area has opened up a new dimension to the design of software defined method in wireless sensor networks WSNs. In this paper, we propose a flow splitting optimization FSO algorithm for solving the problem of traffic load minimization TLM in SDWSNs by considering the selection of optimal relay sensor node and the transmission of optimal splitting flow. To this end, we first establish the model of different packet types and describe the TLM problem.

We then formulate the TLM problem into an optimization problem which is constrained by the load of sensor nodes and the packet similarity between different sensor nodes. Afterwards, we present a Levenberg-Marquardt algorithm for solving the optimization problem of traffic load. We also provide the convergence analysis of the Levenberg-Marquardt algorithm.

However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in SDN networks.

NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision.

Simulation results demonstrated the effectiveness of NetworkAI. An outsourced infrastructure is a virtual infrastructure that mimics the physical infrastructure of the precloud era; it is therefore referred to as a tenant network TN in this paper. This practice draws upon the notion of TN abstraction, which specifies how TNs should be managed.

However, current virtual software-defined network SDN technology uses an SDN hypervisor to attain TNs, where the cloud administrator is given much-more-than-necessary privileges; thus, not only could violation of the security principle of least privilege occur, but the threat of a malicious or innocent-but-compromised administrator may be present.

Motivated by this need, we propose the specification of TN abstraction, including its functions and security requirements. Then, we present a platform-independent concretization of this abstraction called TNGuard, which is an SDN-based architecture that protects the TNs while removing unnecessary privileges from the cloud administrator.

Experimental results show that the resulting system is practical, incurring a small performance overhead. Load balancing techniques are essential for M2M networks to relieve the heavy loading caused by bursty traffic. Leveraging the capability of SDN to monitor and control the network, the proposed load balancing scheme can satisfy different quality of service requirements through traffic identification and rerouting. In particular, the current push toward fog computing, in which control, computation, and storage are moved to nodes close to the network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms.

Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks.

We consider optimization of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes.

Our results show more than 13 times greater energy usage for node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions.

New challenges, like the deployment of novel wireless services or the aim of operators to provide end-to-end monitoring and opimization, make it necessary to develop an innovative scheme for network management. Within these, the automation of RCA activities is one of the key elements to reduce operational expenditures related to network management.

In this article, an SH framework for next-generation networks using dimensionality reduction is proposed as the tool enabling the management of an increasingly complex network, taking advantage of both feature selection and feature extraction techniques. A proof of concept has been carried out in the context of automatic RCA in a live network.

Results show that the proposed framework can effectively manage a high-dimensional environment from different data sources, eventually automating the tasks usually performed by troubleshooting experts while optimizing the performance of the RCA tool.

Basically , It is the concept of understanding routing, switching, and wireless. Networking Project with various algorithm and protocol developed. In addition , they are essential ones which communicate with each other. Internet usage control using access control techniques. Brute Force attack detection using wireshark. WAN Optimization design for Enterprise.

DMZ Network design with Cisco routers. Small Business Network Design with secure e-commerce server. Employee Website Monitoring using Packet Analysis. DHCP Infrastructure security threats , mitigation and assessment. Network Security Policy Implementation for Campus. Comparative study of Web application and Network Layer firewalls. Small business network design with guest network. Network Intrusion detection based on pattern matching. Packet Loss Testing tool.

Network design proposal for bank. Hotspot design proposal for coffee shop. Network design proposal for LAN. Network design proposal for Internet cafe.