Papers by Kaustav Choudhury
In this work, the continue from the last research work done [20], thus it is proposed a data mini... more In this work, the continue from the last research work done [20], thus it is proposed a data mining based anomaly detection system, aiming to detect volume anomalies, using Simple Network Management Protocol (SNMP) monitoring. The method is novel in terms of combining the use of Digital Signature of Network Segment (DSNS) with the evolutionary technique called Particle Swarm Optimization (PSO)[5] and neural network training, applied in a real data set. PSO is a high efficient heuristic technique with low computational complexity, developed in 1995 by Kennedy and Eberhart [1] inspired by social behavior of bird flocking. The DSNS is a baseline that consists of different normal behavior profiles to a specific network device or segment, generated by the GBA tool (Automatic Backbone Management), using data collected from SNMP objects. The proposed anomaly detection system uses the SVM in order to clusterize the traffic collected by SNMP agents and its respective DSNS. The PSO is combine...
In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was prop... more In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, fun...
In this paper, a resource management technique is proposed to handle the request of Virtual Machi... more In this paper, a resource management technique is proposed to handle the request of Virtual Machines (VM’s) as per the need of users, consisting of resources (mips, vm image size, network bandwidth, number of cpu’s) containing cloudlets which in turn are cloud-based application services (content delivery, social networks) commonly deployed in data centers. There are three priority mechanisms governing the user’s requests namely low, medium and high priority requests. Here, the priority is given to the amount of VM’s requested. To implement the above concept, the Hybrid Cloud model is used by which the benefits of both the private and public clouds can be reaped. This model has its advantages that it proves to be cost-effective as the resources are effectively utilized from private clouds and only when exhausted are taken from public clouds which is cheaper.
In this paper, a resource management technique is proposed to handle the request of Virtual Machi... more In this paper, a resource management technique is proposed to handle the request of Virtual Machines (VM's) as per the need of users, consisting of resources (mips, vm image size, network bandwidth, number of cpu's) containing cloudlets which in turn are cloud-based application services (content delivery, social networks) commonly deployed in data centers. There are three priority mechanisms governing the user's requests namely low, medium and high priority requests. Here, the priority is given to the amount of VM's requested. To implement the above concept, the Hybrid Cloud model is used by which the benefits of both the private and public clouds can be reaped. This model has its advantages that it proves to be cost-effective as the resources are effectively utilized from private clouds and only when exhausted are taken from public clouds which is cheaper.
Eucalyptus is an open-source software platform used to create a virtual IT infrastructure with al... more Eucalyptus is an open-source software platform used to create a virtual IT infrastructure with all facilities like storage, network and security as available in real scenarios in a computer system so that researchers and cloud system developers can test their software with least amount of expenditure. Xen is the underlying Virtual Machine Monitor better known as Hypervisor used to simulate the various virtual machines (hardware or software) to work upon. The main aim is to generate a pool of resources or a ‘cloud’ using the above tools and manage them effectively. less
In this work, a data mining based anomaly detection system is proposed, aiming to detect volume a... more In this work, a data mining based anomaly detection system is proposed, aiming to detect volume anomalies, using Simple Network Management Protocol (SNMP) monitoring. The method is novel in terms of combining the use of Digital Signature of Network Segment (DSNS) with the evolutionary technique called Particle Swarm Optimization (PSO)[5] and neural network training, applied in a real data set. PSO is a high efficient heuristic technique with low computational complexity, developed in 1995 by Kennedy and Eberhart [1] inspired by social behavior of bird flocking. The DSNS is a baseline that consists of different normal behavior profiles to a specific network device or segment, generated by the GBA tool (Automatic Backbone Management), using data collected from SNMP objects. The proposed anomaly detection system uses the SVM in order to clusterize the traffic collected by SNMP agents and its respective DSNS. The PSO is combined with the SVM in order to improve performance and quality of the solution in the clusterization and calculation of clusters centroids. Tests were carried out using a real network environment in the Techno India University, Kolkata. Numerical results have been shown that the obtained detection and false alarm rates are promising. It is also implemented the deterministic method proposed in order to detect anomalies on the same dataset, so that both methods could be compared.
In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was prop... more In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset.Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed-forward networks are trained using back propagation learning algorithm.
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Papers by Kaustav Choudhury