Ansal University

Gurgaon, India

Ansal University

Gurgaon, India

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Borkar M.,Ansal University | Nitin,Jaypee Institute of Information Technology
Journal of Supercomputing | Year: 2015

This paper proposes a new variant of Gamma Interconnection Network (GIN), which uses an alternate source at the initial stage. The alternate source helped in realizing the Bit Reversal permutation completely in one pass. The paper also proposes a modified permutation realization algorithm, which is being used to realize the frequently used permutations on GIN family of networks. This algorithm also ensures that the alternate source approach can be used with all the GIN family networks with sizes $$\ge $$≥8 to realize the frequently used permutations. The paper also discusses the performance of the modified algorithm in terms of hop count required to realize the permutations as well as the effect on hardware cost due to alternate source. © 2015, Springer Science+Business Media New York.


Aggarwal P.,Ansal University | Sharma S.K.,Ansal University
Advances in Intelligent Systems and Computing | Year: 2015

Intrusion Detection System (IDS) can be called efficient when maximum intrusion attacks are detected with minimum false alarm rate but due to imbalanced data, these two metrics are not comparable on the same scale. In this paper, a new NPR metric is suggested in view of the imbalanced data set to rank the classification algorithms for IDS which can help analyze and identify the best possible combination of high detection rate and low false alarm rate with maximum accuracy and F-score. The new NPR metric is used for comparison and ordering of ten classifiers simulated on KDD data set. © Springer International Publishing Switzerland 2015.


Bahl S.,KIIT University | Sharma S.K.,Ansal University
Advances in Intelligent Systems and Computing | Year: 2015

Intrusion detection system (IDS) research field has grown tremendously over the past decade. Most of the IDSs employ almost all data features for detection of intrusions. It has been observed that some of the features might not be relevant or did not improve the performance of the system significantly. Objective of this proposed work is to select a minimal subset of most relevant features for designing IDS. A minimal subset of features is chosen from the features commonly selected by correlation based feature selection with six search methods. Further, the performance comparison among seven selected subsets and complete set of features is analyzed. The simulation results show better performance using the proposed subset having only 12 features in comparison to others. © Springer International Publishing Switzerland 2015.


Bahl S.,Ansal University | Sharma S.K.,Ansal University
International Conference on Computing, Communication and Automation, ICCCA 2015 | Year: 2015

Intrusion detection system (IDS) research field has grown tremendously in the past decade. Improving the detection rate of user to root (U2R) attack classes is an open research problem. Current IDS uses all data features to detect intrusions. Some of the features may be redundant to the detection process. The purpose of this empirical study is to identify important features to improve the detection rate of U2R attack class. The investigated correlation feature selection improved the overall accuracy, detection rate of U2R attack. The empirical results have given a noticeable improvement in detection rate of U2R. © 2015 IEEE.


Sharma S.,Ansal University
Worldwide Hospitality and Tourism Themes | Year: 2015

Purpose – The purpose of this paper is to explore fairs and festivals organized in the city; reasons to celebrate; and their economic impact on local people, vendors and visitors. Design/methodology/approach – Secondary data were collected through local library, Web sites, books and other publications. Local residents, visitors and vendors were interviewed through semi-structured questionnaire and personal interviews. Findings – The events organized are not only an expression of the religious, social and cultural urges of the local population but also help to preserve traditions and folk culture of the region. It brings suppliers and vendors from nearby villages and cities together, resulting in significant economic well-being of the community and self. Research limitations/implications – Traveling distance to the destination, understanding of the questionnaire by the audience and getting data to analyze the economic impact of such events at a higher level are some of the limitations. Further research is required on the economic impact of regional events on state revenue, and potential areas of study may include traditional sustainable practices and the economic impact and development of an economic framework, keeping regional fairs and festivals as the center of the study. Practical implications – The research highlights the challenges for the organizers, scope of improvement and ways to popularize regional culture and cuisine. Vendors and visitors find it difficult to reach the event but are optimistic about the development. It also acts as a promotional tool to popularize Pithoragarh as a tourist destination. Originality/value – The paper helps to project Pithoragarh as a potential tourist destination known for its fairs and festivals. It focuses on the economic impact of the stakeholders and it helps visitors to acknowledge traditions and cuisine. © Emerald Group Publishing Limited


Thakral B.,Ansal University | Bakshi G.,Ansal University | Kushwaha A.K.,Ansal University
ICROIT 2014 - Proceedings of the 2014 International Conference on Reliability, Optimization and Information Technology | Year: 2014

Classical Scaling is no longer possible to follow Moore's law. Planar Fully depleted SOI is an advanced technology designed to operate at low power. This paper deals with further scaling of SOI devices, its advantages and reduction of kink effect in SOI MOSFET using SELBOX Structure. Silvaco TCAD tools have been used, basic mechanism which lead to kink generation are studied in comparison with bulk MOSFET and advantages of SELBOX structures are described for various gap widths. © 2014 IEEE.


Aggarwal P.,Ansal University | Sharma S.K.,Ansal University
International Conference on Advanced Computing and Communication Technologies, ACCT | Year: 2015

The massive data exchange on the web has deeply increased the risk of malicious activities thereby propelling the research in the area of Intrusion Detection System (IDS). This paper aims to first select ten classification algorithms based on their efficiency in terms of speed, capability to handle large dataset and dependency on parameter tuning and then simulates the ten selected existing classifiers on a data mining tool Weka for KDD'99 dataset. The simulation results are evaluated and benchmarked based on the generic evaluation metrics for IDS like F-score and accuracy. © 2015 IEEE.


Bahl S.,KIIT University | Sharma S.K.,Ansal University
International Conference on Advanced Computing and Communication Technologies, ACCT | Year: 2015

Intrusion detection system (IDS) research field has grown tremendously in the past decade. Improving the detection rate of user to root (U2R) attack class is an open research problem. Current IDS uses all data features to detect intrusions. Some of the features may be redundant to the detection process. The purpose of this empirical study is to identify the important features to improve the detection rate and reduce the false detection rate. The investigated feature subset selection techniques improve the overall accuracy, detection rate of U2R attack class and also reduce the computational cost. The empirical results have shown a noticeable improvement in detection rate of U2R attack class with feature subset selection techniques. © 2015 IEEE.


Aggarwal P.,Ansal University | Sharma S.K.,Ansal University
Advances in Intelligent Systems and Computing | Year: 2016

Imbalance in data is quite obvious while studying intrusion detection system (IDS). Classification algorithms are used to identify the attacks in IDS, which has many parameters for its performance evaluation. Due to imbalance in data, the classification results need to be revisited given that IDS generally evaluates detection rate and false alarm rate which belongs to two different classes. This paper validates a new metric NPR used for ranking the classifiers for IDS. The metric is made functional on KDD data set and then the classifiers are ranked and compared with results on another data set. © Springer India 2016.


Aggarwal P.,Ansal University | Sharma S.K.,Ansal University
Procedia Computer Science | Year: 2015

The KDD data set is a well known benchmark in the research of Intrusion Detection techniques. A lot of work is going on for the improvement of intrusion detection strategies while the research on the data used for training and testing the detection model is equally of prime concern because better data quality can improve offline intrusion detection. This paper presents the analysis of KDD data set with respect to four classes which are Basic, Content, Traffic and Host in which all data attributes can be categorized. The analysis is done with respect to two prominent evaluation metrics, Detection Rate (DR) and False Alarm Rate (FAR) for an Intrusion Detection System (IDS). As a result of this empirical analysis on the data set, the contribution of each of four classes of attributes on DR and FAR is shown which can help enhance the suitability of data set to achieve maximum DR with minimum FAR. © 2015 Published by Elsevier Ltd.

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