Computer Networks Security Models - A New Approach for Denial-of-Services Attacks Mitigation

Computer networks are a critical factor for the performance of a modern company. Managing networks is as important as managing any other aspect of the company’s performance and security. There are many tools and appliances for monitoring the traffic and analyzing the network flow security. They use different approaches and rely on a variety of characteristics of the network flows. Network researchers are still working on a common approach for security baselining that might enable early watch alerts. This research focuses on the network security models, particularly the Denial-of-Services (DoS) attacks mitigation, based on a network flow analysis using the flows measurements and the theory of Markov models. The content of the paper comprises the essentials of the author’s doctoral thesis.

Aplications of Neural Networks to Find the Impact of Water in Different Berry Components in Grapes

Grape juice composition during the different stages of berry growth was compared. The analytical data collected were used to investigate the relationships between some of the different components studied in these berries during the ripening period. Our goal is to study, with neural networks, the impact of water availability on Vitis vinifera L. cv. Tempranillo grape yields and juice composition over a three-year period.

Networks of Evolutionary Processors (NEP) as Decision Support Systems

This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.

Quality of Service System Approximation in IP Networks

This paper is sponsored by the Ministry of Education and Research of the Republic of Bulgaria in the framework of project No 105 “Multimedia Packet Switching Networks Planning with Quality of Service and Traffic Management”.

Applications of Radial Basis Neural Networks for Area Forest

This paper proposes a new method using radial basis neural networks in order to find the classification and the recognition of trees species for forest inventories. This method computes the wood volume using a set of data easily obtained. The results that are obtained improve the used classic and statistical models.

Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites.

A Linear Time Algorithm for Computing Longest Paths in Cactus Graphs

ACM Computing Classification System (1998): G.2.2.

Tutorial on Computing: Technological Advances, Social Implications, Ethical and Legal Issues

Computing and information technology have made significant advances. The use of computing and technology is a major aspect of our lives, and this use will only continue to increase in our lifetime. Electronic digital computers and high performance communication networks are central to contemporary information technology. The computing applications in a wide range of areas including business, communications, medical research, transportation, entertainments, and education are transforming local and global societies around the globe. The rapid changes in the fields of computing and information technology also make the study of ethics exciting and challenging, as nearly every day, the media report on a new invention, controversy, or court ruling. This tutorial will explore a broad overview on the scientific foundations, technological advances, social implications, and ethical and legal issues related to computing. It will provide the milestones in computing and in networking, social context of computing, professional and ethical responsibilities, philosophical frameworks, and social, ethical, historical, and political implications of computer and information technology. It will outline the impact of the tremendous growth of computer and information technology on people, ethics and law. Political and legal implications will become clear when we analyze how technology has outpaced the legal and political arenas.

An Adaptation of the Hoshen-Kopelman Cluster Counting Algorithm for Honeycomb Networks

We develop a simplified implementation of the Hoshen-Kopelman cluster counting algorithm adapted for honeycomb networks. In our implementation of the algorithm we assume that all nodes in the network are occupied and links between nodes can be intact or broken. The algorithm counts how many clusters there are in the network and determines which nodes belong to each cluster. The network information is stored into two sets of data. The first one is related to the connectivity of the nodes and the second one to the state of links. The algorithm finds all clusters in only one scan across the network and thereafter cluster relabeling operates on a vector whose size is much smaller than the size of the network. Counting the number of clusters of each size, the algorithm determines the cluster size probability distribution from which the mean cluster size parameter can be estimated. Although our implementation of the Hoshen-Kopelman algorithm works only for networks with a honeycomb (hexagonal) structure, it can be easily changed to be applied for networks with arbitrary connectivity between the nodes (triangular, square, etc.). The proposed adaptation of the Hoshen-Kopelman cluster counting algorithm is applied to studying the thermal degradation of a graphene-like honeycomb membrane by means of Molecular Dynamics simulation with a Langevin thermostat. ACM Computing Classification System (1998): F.2.2, I.5.3.