Browsing by Author "McFarland, Nichole"

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    Investigation of Malicious Portable Executable File Detection on the Network using Supervised Learning Techniques
    (IEEE, 2017-05) Vyas, Rushabh; Luo, Xiao; McFarland, Nichole; Justice, Connie; Computer Information and Graphics Technology, School of Engineering and Technology
    Malware continues to be a critical concern for everyone from home users to enterprises. Today, most devices are connected through networks to the Internet. Therefore, malicious code can easily and rapidly spread. The objective of this paper is to examine how malicious portable executable (PE) files can be detected on the network by utilizing machine learning algorithms. The efficiency and effectiveness of the network detection rely on the number of features and the learning algorithms. In this work, we examined 28 features extracted from metadata, packing, imported DLLs and functions of four different types of PE files for malware detection. The returned results showed that the proposed system can achieve 98.7% detection rates, 1.8% false positive rate, and with an average scanning speed of 0.5 seconds per file in our testing environment.
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    Secure cloud computing infrastructure for K-12 education
    (2017) Justice, Connie; McFarland, Nichole; Computer Information and Graphics Technology, School of Engineering and Technology
    With cloud computing becoming more and more popular among businesses, there has become a higher demand for security in the cloud. K-12 school systems have a lack of IT resources and support to securely store and share data, thus making cloud services an attractive option. Additionally, there is increasing pressure on school systems to provide information for students and parents that require access to the information stored on school networks. Therefore, cloud services are a viable option for K-12 school systems to alleviate the administrative overhead and to provide access to necessary information for students and parents. This applied research project is an experimental design for addressing the issues that the K-12 school systems face. The secure cloud project consisted of four databases and three nodes. The databases were Keystone, Glance, Nova, and Neutron. First, the Keystone database handled the identity service. The second database was the image client, Glance. Images were launched through this database following a correct authentication token. The third database was Nova. Nova handled all the compute services for the controller and compute node. Fourth was the Neutron database service, which handled all the networking agents that traveled through all three nodes. There were three nodes; a compute node; a controller node; and a networking node to run the cloud. The controller node is the first to be used by verifying identity of the user. It then travels through the management network to the compute node that operates the virtualized network. Traffic between will be monitored by the network node to assign DHCP to each session. Future work to the secure cloud include: a security node to filter through the traffic to alert when an issue arises; and another server to allow for more space to be allocated for virtual machines. These improvements will enhance performance by segmenting information on a different secure network. © American Society for Engineering Education, 2017.