Fuzzy Clustering Based Approach to Network Traffic Classification and Anomaly Detection

We develop network traffic classification and anomaly detection methods based on traffic time series analysis using fuzzy clustering. We compare four fuzzy clustering techniques using different dimensionality reduction methods and validity indices to work out effective anomaly detection algorithm. Effectiveness of the proposed classification system is evaluated on traffic data with and without traffic attack components

Daudzfunkcionāla izsaukumu centra modelēšana un darbības kvalitātes novērtēšana

Ir aprakstītas un salīdzinātas daudzfunkcionāla izsaukumu centra trīs darbības shēmas balstītas uz universāliem un specializētiem kanāliem. Veicot simulēšanas eksperimentus JMT vidē ir pamatots, ka universālu un specializētu kanālu kombinēšana dod iespēju risināt ar centra darbības optimizēšanu un efektivitātes paaugstināšanu saistītas problēmas

Fuzzy Approach for QoS Aware Application Driven Traffic Control in GMPLS Networks

The ITU-T defined next generation network (NGN) architecture designates the resource admission control function to perform the application-driven Quality of Service (QoS) control across access and core networks. The Generalized Multiprotocol Label Switching (GMPLS) was introduced by the Internet Engineering Task Force (IETF) to cope with new traffic engineering challenges in fast optical networks and provide them with reliable end-to-end QoS mechanisms. However, an actual Connection Admission Control (CAC) implementation inside the resource reserva-tion protocol – traffic engineering extension (RSVP-TE) in GMPLS networks does not provide the ability of effective decision making, since the applied threshold CAC lacks of the capability to consider QoS policies on GMPLS network nodes. This prevents an effective end-to-end QoS control in a fully dynamic, application driven Label Switched Path (LSP) setup scenario. This work presents a specific imple-mentation of fuzzy-CAC operating over an RSVP-TE agent in GMPLS network domain. This fuzzy-CAC implementa-tion is applied to a testbed where a client application re-quests a real-time data transfer through a GMPLS network, which results in dynamic LSP setup and exclusion. The ad-mission control is performed upon service request based on QoS class requirements and network resource availability. The differentiated traffic treatment on per-flow basis is rea-lized through employment of IF-THEN rule based expert knowledge. Effective traffic differentiation is achieved in a multi-service network scenario and thus it validates fuzzy-CAC as candidate for RSVP-TE protocol enhancement for application driven QoS provisioning in GMPLS networks

Data Mining for Managing Intrinsic Quality of Service in MPLS

LSP set up admission control policy is one of the notable problems that have to be solved to fulfill the requirements for effective resource allocation and network utilization for appropriate QoS level. In this paper, we verify a possibility of a new LSP setup admission algorithm, which uses optimization procedure based on multi-objective model with Pareto ranking and Genetic Algorithm. Decision rules are generated with Data Mining approach by performing classification operation to the selected data. This algorithm functions in two phases – classification and operating, which are accomplished consecutive. Algorithm is described and depicted. Experimental data are depicted and future research subjects are pointed. Ill. 4, bibl. 1

A Fuzzy Approach for Network Bandwidth Management

The paper deals with the problem of resource allocation in a substrate network with DaVinci architecture. We present a methodology of decision making on network bandwidth allocation based on fuzzification and defuzzification principles and the expert knowledge database of fuzzy rules and describe a fuzzy logic based modification of the adaptive bandwidth allocation mechanism to optimize decision making under uncertain network conditions. We describe a simulation scheme used for the estimation of performance of this modification for two nodes network topology

A Fuzzy Logic Based Approach to Bandwidth Allocation in Network Virtualization

We present a fuzzy logic based methodology of decision making on bandwidth allocation in a substrate network with DaVinci architecture, according to which the physical substrate network is divided into virtual networks. This methodology describes a fuzzy modification of the adaptive bandwidth allocation mechanism introduced in order to optimize decision making under uncertain network conditions by using fuzzification and defuzzification principles and the expert knowledge database of fuzzy rules. The effectiveness of it is evaluated on the link level for two traffic types within simulation experiments realized by using Coloured Petri Nets Tools

Coloured Petri Nets Based Simulation Scheme for Adaptive Bandwidth Management

We consider the problem of resource allocation for multiple classes of traffic in a substrate network with DaVinci architecture. According to DaVinci (Dynamically Adaptive Virtual Networks for a Customized Internet) approach a single physical network can support multiple traffic classes with different performance objectives by means of multiple virtual networks constructed over the physical one. In this context the problem of bandwidth allocation is a conditional maximization problem for the aggregate performance of all virtual networks. We describe Coloured Petri Nets (CPN) based models and present CPN based schemes for simulation of dynamically adaptive bandwidth allocation mechanisms proposed to support multiple traffics in a substrate network. The simulation study focuses on two traffic types: delay sensitive and throughput sensitive. Obtained simulation results demonstrate the achievement of dynamically adaptive bandwidth management strategies

Simulation Based Analysis and Development of Decision Support System for Virtual Network Bandwidth Management

We develop fuzzy logic based methodology of decision making on bandwidth allocation in a substrate network with DaVinci architecture, according to which the physical substrate network is divided into virtual networks. We show that fuzzy game theory can be effectively used as a framework of decision making tools for bandwidth allocation mechanisms. The effectiveness of this methodology is evaluated and improved within simulation experiments realized by using Coloured Petri Nets Tools

Evaluation of AggSessAC Admission Control Solution to Improve Network Revenue

The evaluation of a new admission control scheme that provides selective and priority-based flow admission strategy is an essential part of network revenue management in order to provide their maximization. The existing CAC schemes largely based on the serialized processing strategy of new requests, while making an acceptance or rejection decision once a new request arrives. However, present traffic volumes and computing capabilities allows us to offer a new admission control. The proposed method focuses on a new operation paradigm for admission control scheme, where requests are aggregated and processed using mutually comparisons among them, thus facilitating selectivity and network revenue maximization. We publish method architecture and realization description in modular OMNeT++ simulation environment as well as the obtained results. Finally, simulation results of proposed scheme are compared with conventional ThresholdAC method, which uses available link bandwidth for decision-making process and serialized flow processing strategy. The results show that the proposed method greatly improves and extends decision-making process and allows increasing number of accepted higher priority traffic flows and, as a result, the network revenues