Performance evaluation of an open distributed platform for realistic traffic generation

Network researchers have dedicated a notable part of their efforts to the area of modeling traffic and to the implementation of efficient traffic generators. We feel that there is a strong demand for traffic generators capable to reproduce realistic traffic patterns according to theoretical models and at the same time with high performance. This work presents an open distributed platform for traffic generation that we called distributed internet traffic generator (D-ITG), capable of producing traffic (network, transport and application layer) at packet level and of accurately replicating appropriate stochastic processes for both inter departure time (IDT) and packet size (PS) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. In this work a complete performance comparison among the centralized version and the two distributed versions of D-ITG is presented

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Modelling and Analysis of TCP Performance in Wireless Multihop Networks

Researchers have used extensive simulation and experimental studies to understand TCP performance in wireless multihop networks. In contrast, the objective of this paper is to theoretically analyze TCP performance in this environment. By examining the case of running one TCP session over a string topology, a system model for analyzing TCP performance in multihop wireless networks is proposed, which considers packet buffering, contention of nodes for access to the wireless channel, and spatial reuse of the wireless channel. Markov chain modelling is applied to analyze this system model. Analytical results show that when the number of hops that the TCP session crosses is fixed, the TCP throughput is independent of the TCP congestion window size. When the number of hops increases from one, the TCP throughput decreases first, and then stabilizes when the number of hops becomes large. The analysis is validated by comparing the numerical and simulation result

Web User-session Inference by Means of Clustering Techniques

This paper focuses on the definition and identification of “Web user-sessions”, aggregations of several TCP connections generated by the same source host. The identification of a user-session is non trivial. Traditional approaches rely on threshold based mechanisms. However, these techniques are very sensitive to the value chosen for the threshold, which may be difficult to set correctly. By applying clustering techniques, we define a novel methodology to identify Web user-sessions without requiring an a priori definition of threshold values. We define a clustering based approach, we discuss pros and cons of this approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially generated traces to evaluate its benefits against traditional threshold based approaches. We also analyze the characteristics of user-sessions extracted by the clustering methodology from real traces and study their statistical properties. Web user-sessions tend to be Poisson, but correlation may arise during periods of network/hosts anomalous behavior