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One of the serious and widespread attacks in cyber security is Botnet. Using command and control infrastructure or peer-to-peer communication between bots, botmasters can perform a variety of attacks on internet system-users. To mitigate this, multiple techniques have been developed for botnet detection over the past two decades. In this paper we have discussed various botnet structures and the different techniques of botnet detection proposed in literature. We evaluated these techniques based on their distinctive features and presented their detailed comparative analysis. We also proposed a method for botnet detection using network traffic monitoring. Our approach is based on combining signature and anomaly detection systems that complement each other. Our proposed hybrid detection system may decrease false positive rate in anomaly detection by finding the well-known bots using signature detection and thereby may increase overall detection efficiency.

Keywords

Botnet, Malicious Activities, P2P, Anomaly Detection.

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Comparative Analysis of Medical Diagnostic Techniques Using ANN

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Authors

Farooq Ayoub Dar ¹, Yashwant Singh ¹

Affiliations
1 Department of Computer Science & Technology, Central University of Jammu, Jammu & Kashmir, IN

Source

Research Cell: An International Journal of Engineering Sciences, Vol 27, No 1 (2018), Pagination: 104-114

Abstract

An immense and immeasurable amount of data is available to medical experts, extending from points of interest of clinical manifestations to different sorts of biochemical information and yields of imaging gadgets. Each kind of information yields data that must be assessed and relegated to a specific pathology amid the diagnostic process. To rationalize the diagnosis in every day routine and maintain a strategic distance from misdiagnosis, methods of machine learning (particularly ANNs) may be utilized. The versatile learning algorithms of machine learning may deal with several kinds of restorative heterogeneous information and classify them into various class outputs. In this paper, we concisely survey and examine the logic, capacities, and performance of ANNs in medical diagnosis of various diseases by making comparative analysis and focusing more on the medical diagnosis of Diabetes. The use of PID dataset for diagnosis is also demonstrated.

Keywords

Multi-Layer Perceptron Neural Networks (MLPNN), PID (Pima Indian Diabetes Dataset), MLFFN (Multilayer Feedforward Network), BPN (Backpropagation Network), General regression neural network (GRNN), Radial basis function (RBF).

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A Fairness Framework for Resource Allocation in Cloud Computing

Abstract Views :215 | PDF Views:0

Authors

Imtiaz Ahmed ¹, Yashwant Singh ¹

Affiliations
1 Department of Computer Science & Information Technology, Central University of Jammu, Jammu and Kashmir, IN

Source

Research Cell: An International Journal of Engineering Sciences, Vol 27, No 1 (2018), Pagination: 115-121

Abstract

Cloud computing is a model for empowering request to arrange access to a common pool of configurable registering resource. In distributed computing frameworks the resource pool is built from an extensive number of heterogeneous servers. The multi-resource distribution component, called DRFH is a Predominant Resource Reasonableness (DRF) from a solitary server to various heterogeneous servers. The DRFH has various profoundly attractive properties. With DRFH, no client lean towards the allotment of another client; nobody can enhance its portion without diminishing that of the others; and all the more imperatively, no client has an impetus to lie about its resource request. As a direct application, we outline a straightforward heuristic that actualizes DRFH in true frameworks. Expansive scale reenactments driven by Google group follows demonstrate that DRFH altogether outflanks the conventional opening based scheduler, prompting substantially higher asset/resource use with considerably shorter occupation finish times.

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