A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks

An Optical Burst Switching (OBS) network is vulnerable to Burst Header Packet (BHP) flooding attack. In flooding attacks, edge nodes send BHPs at a high rate to reserve bandwidth for unrealized data bursts, which leads to a waste of bandwidth, a decrease in network performance, and massive data loss. Machine learning techniques are utilized to detect this attack in the OBS network. In this paper, we propose a particle swarm optimization–support vector machine (PSO-SVM) model for detecting BHP flooding attacks, in which the PSO is used to optimize the parameters of the SVM. We use the dataset provided by the UCI warehouse to train and test the model. The experimental results show that the detection accuracy of the PSO-SVM model reaches 95.0%, which is 9.4%, 9.6%, 20.7%, 8% higher than naïve Bayes, SVM, k-nearest neighbor, and decision tree. Although DCNN outperforms our model, it requires more processing and training time. Collectively, our approach is effective and high-efficiency in detecting flooding attacks in optical burst switching networks and maintaining network stability and security.

The optical properties of different materials used for thermo-optical switches

With growing demands of internet protocol services for transmission capacity and speed, the solution for future high speed optical networks is optical burst switching that is a technology for transmitting large amounts of data bursts through a transparent optical switching network the optical switches in optical burst switching networks play important role in the resource reservation and are very important to ensure reliability and flexibility of the network. This paper is focused on the very important components of Optical Burst Switching networks, ieo ptical switches, specifically thermo-optical switches. In this paper are presented the simulation analysis of performance evaluation of thermo-optical switches executed in the model of Optical Burst Switching network and simulation study of investigation of influence of roughness and layer thickness on the optical properties (spectral reflectance, transmittance) of selected materials (SiO2, Ta2O5, Al2O3) for thermooptical switches.

Fallacious Node Algorithm for Performance Enhancement in Optical-Burst-Switching Networks

In this paper, fallacious node algorithm is formulated for performance enhancement of an optical-burst-switching (OBS) network. With the procedural and observational analysis, we have demonstrated that the blocking probability is extremely unnoticeable, during a call establishment, while collectively discarding the faulty nodes from the selected paths. There may be distinguishing values of blocking probability because of random value of congestion on each path. The blocking probability is restrained so as not to be more than 10 % on several values on traffic and congestion. The blocking probability diminishes and becomes imperceptible with the incorporation of fallacious node algorithm and subsequently the performance of optical network is highly aggrandized. Thus the fallacious node algorithm manifests incredible prospects for optical networks as the key features such as the accessibility; sustainability and reliability of the network are highly appreciated and upgraded.