Challenges exist in higher education networks with host inventory and identification. Any student, staff, faculty, or dedicated IT administrator can be the primary responsible personnel for devices on the network. Confounding the problem is that there is also a large mix of personally-owned devices. These network environments are a hybrid of corporate enterprise, federated network, and Internet service provider. This management model has survived for decades based on the ability to identify responsible personnel when a host, system, or user account is suspected to have been compromised or is disrupting network availability for others. Mobile devices, roaming wireless access, and users accessing services from multiple devices has made the task of identification onerous. With increasing numbers of hosts on networks of higher education institutions, strategies such as dynamic addressing and address translation become necessary. The proliferation of the Internet of Things (IoT) makes this identification task even more difficult. Loss of intellectual property, extortion, theft, and reputational damage are all significant risks to research institution networks. Quickly responding to and remediating incidents reduces exposure and risk. This research evaluates what universities are doing for host inventory and creates a working prototype of a system for associating relevant log events to one or more responsible people. The prototype reduces the need for human-driven updates while enriching the dynamic host inventory with additional information. It also shows the value of associating application and service authentications to hosts. The prototype uses live network data which is de-identified to protect privacy. / Master of Science / Keeping track of computers or hosts on a network has become increasingly difficult. In the past, most of the hosts were owned by the institution, but now more hosts are owned by the end users. The management of institution networks has become a mix of corporate enterprise, federated network, and Internet service provider. This model has survived for decades based on the ability to identify someone responsible when a host or system is suspected to be infected with malware or is disrupting network availability for others. Mobile devices, roaming wireless access, and users accessing services from multiple devices has made the task of identification more difficult. With increasing numbers of hosts on networks of higher education institutions, strategies such as dynamic addressing and address translation become necessary. The proliferation of the Internet of Things (IoT) makes identification even more difficult. Loss of intellectual property, theft, and reputational damage are all significant risks to institution networks. Quickly responding to and remediating cybersecurity incidents reduces exposure and risk. This research considers what universities are doing for host inventory and creates a working prototype of a system for associating relevant log events to one or more responsible people. The prototype reduces the need for human-driven updates while incorporating additional information for the dynamic host inventory. It also shows the value of associating application and service authentications to hosts. The prototype uses real network data which is de-identified to protect privacy.

Every day system administrators perform many tasks that share one common requirement: information about the system.This information has to be up-to-date and reliable.To collect this information on demand and manually is time consuming and labour intensive.In the Network and Systems Administration (NSA) lab environment at the University of Skövde, there are many computers which need to be administrated.Today this is done by using a shared spreadsheet, a costly method with drawbacks.This project will search for answers to the question ``How can an automated client inventory improve the work of administrators at the NSA lab?''.To answer this question, a prototype will be created and its performance compared with the manual collection of information.In order to create a realistic and useful prototype, a list of requirements for inventories has to be assembled.The prototype has additionally to take the peculiarities of the NSA lab environment into account.The information has to be gathered without any permanent installation of a client, as there are no internal hard drives available.This detail makes it impossible for already existing applications to be deployed.The NSA administrators will then compare the performance of the prototype with the manual collection of data. They will conduct a trial during which they will be observed and afterwards answer some questions about the process. The results of the analysis of this data yielded a number of hypotheses.Common concepts like the time saving by automation get supported by this projects findings, but also less obvious observations are made. With the help of an automated network inventory administrators can become proactive and fix potential issues before they become problems.The extraction of information from a network inventory requires less knowledge about system administration, as the information is presented in an user interface.The user does not need to gather data as this is done by the network inventory.Only the extraction of the right information from the user interface is required.Information useful to the continued development of the prototype was collected as well. Improvements to the user interface as well as more automation should be added to the prototype in order to further improve the efficiency compared to the manual collection of information.

Master´s thesis is solved in business enterprise focused on end user, proceeding demand prognosis. With concentration on solution and optimizing transport costs in options (production enterprise inventory, cross-docking), inventory management and logistic network in enterprise.