UUM Network Traffic Analysis and Users' Website Preferences

The current world is experiencing a revolution in Internet services and networking; a revolution that provided and continues to provide varying features and invaluable tools to computer networks. On the other hand, several problems are being faced by users and global organizations in networking including lack of bandwidth and packet loss during transmission which impacts Internet efficiency and the performance of network. These issues can be rectified through the measurement and analysis of the network’s performance. Moreover, for network performance enhancement, it is imperative to study users’ behaviour. Therefore, the main objectives of the present study are to identify UUM network performance through Internet traffic and to highlight users’ behaviour. A total of three methodological steps are carried out to meet the objectives of the study; the first one is the data collection phase whereby the source for data collection is taken from the presently used main distributed switch in an hour for each working day in a duration of one week; the second one is the data analysis phase where Wireshark is used to provide the statistics of traffic and finally; the third phase is the data presentation where Microsoft Excel is utilized to present data. Study findings presents valuable information of network bandwidth, data loss rates and Ethernet traffic distribution, in addition to the fact that is social websites are the most websites used in UUM. These findings leads to facilitate the enhancement of UUM network performance and Internet bandwidth strategies

Large Scale Enrichment and Statistical Cyber Characterization of Network Traffic

Modern network sensors continuously produce enormous quantities of raw data that are beyond the capacity of human analysts. Cross-correlation of network sensors increases this challenge by enriching every network event with additional metadata. These large volumes of enriched network data present opportunities to statistically characterize network traffic and quickly answer a key question: "What are the primary cyber characteristics of my network data?" The Python GraphBLAS and PyD4M analysis frameworks enable anonymized statistical analysis to be performed quickly and efficiently on very large network data sets. This approach is tested using billions of anonymized network data samples from the largest Internet observatory (CAIDA Telescope) and tens of millions of anonymized records from the largest commercially available background enrichment capability (GreyNoise). The analysis confirms that most of the enriched variables follow expected heavy-tail distributions and that a large fraction of the network traffic is due to a small number of cyber activities. This information can simplify the cyber analysts' task by enabling prioritization of cyber activities based on statistical prevalence.Comment: 8 pages, 8 figures, HPE

Mechanism of organization increase in complex systems

This paper proposes a variational approach to describe the evolution of organization of complex systems from first principles, as increased efficiency of physical action. Most simply stated, physical action is the product of the energy and time necessary for motion. When complex systems are modeled as flow networks, this efficiency is defined as a decrease of action for one element to cross between two nodes, or endpoints of motion - a principle of least unit action. We find a connection with another principle that of most total action, or a tendency for increase of the total action of a system. This increase provides more energy and time for minimization of the constraints to motion in order to decrease unit action, and therefore to increase organization. Also, with the decrease of unit action in a system, its capacity for total amount of action increases. We present a model of positive feedback between action efficiency and the total amount of action in a complex system, based on a system of ordinary differential equations, which leads to an exponential growth with time of each and a power law relation between the two. We present an agreement of our model with data for core processing units of computers. This approach can help to describe, measure, manage, design and predict future behavior of complex systems to achieve the highest rates of self-organization and robustness.Comment: 22 pages, 4 figures, 1 tabl