Institute for Development and Research in Banking Technology

About: Institute for Development and Research in Banking Technology is a government organization based out in Hyderabad, India. It is known for research contribution in the topics: Cloud computing & Encryption. The organization has 148 authors who have published 270 publications receiving 2833 citations.

SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services

Abstract: With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection.

Network intrusion detection system using J48 Decision Tree

Abstract: As the number of cyber attacks have increased, detecting the intrusion in networks become a very tough job. For network intrusion detection system (NIDS), many data mining and machine learning techniques are used. However, for evaluation, most of the researchers used KDD Cup 99 data set, which has widely criticized for not showing current network situation. In this paper we used a new labelled network dataset, called Kyoto 2006+ dataset. In Kyoto 2006+ data set, every instant is labelled as normal (no attack), attack (known attack) and unknown attack. We use Decision Tree (J48) algorithm to classify the network packet that can be used for NIDS. For training and testing we used 134665 network instances. The generated rules works with 97.2% correctness for detecting the connection i.e., no attack, known attack or unknown attack.