Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network data

Abstract: Ever since the internet became part of the everyday lives of humans providing network security has been considered of utmost importance. Over the years lot of time and energy has been devoted by people in the research community and industry to provide better, improved and secure mechanisms to ensure secure communications on the internet. Amongst the many fields of study, the most prominent and ever evolving one has been the study of network traffic for attack detection and mitigation. The advent of new technologies has led to an increase in the pace of network based attacks and therefore novel modified approaches are needed to be able to cope with these latest trends. Distributed machine learning with the development of new tools and frameworks like RDD structure in Apache Spark provides an immense scope of growth in this direction. Moreover, the dynamic nature of present day network traffic called concept drift has also necessitated studying solutions from a different angle. We, therefore, in this paper have worked on distributed machine learning based ensemble techniques to detect the presence of concept drift in network traffic and detect network based attacks. The work has been done in three parts. Firstly, two classifiers, namely, Random Forest and Logistic Regression have been used as level ‘0′ learners and Support Vector Machine has been used as level ‘1′ learner. Secondly, to handle the process of concept drift we have used a sliding window based K-means clustering. And thirdly ensemble based techniques for detection of attacks in the traffic. The experiments have been performed on three datasets, namely, the NSL-KDD dataset, the CIDDS-2017 dataset and generated Testbed dataset. These tests have been conducted on different machines by varying the number of executor cores to study time latency in a distributed environment. An accuracy of 93% on NSL-KDD, 98% on CIDDS-2017 and 97% on Testbed datasets for SVM based blending model have been achieved.

VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

Abstract: A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer’s dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources. So, to minimize energy consumption in the cloud data center, virtual machines should be scheduled in an energy-efficient way. In this paper, an Artificial Immune System based Virtual Machine Scheduling using Modified Clonal Selection Algorithm (VMS-MCSA) is proposed to schedule virtual machines energy efficiently. The classical Clonal Selection Algorithm(CSA) operators are modified such that they can be applied to the discrete optimization dynamic virtual machine scheduling problem. The randomized mutation operator is proposed, which reschedule VMs at each scheduling interval to handle the dynamicity of workload with minimum virtual machine migrations. Additionally, the VM-consolidation model was proposed for constraint-based virtual machine migration. The proposed VMS-MCSA algorithm is implemented on a cloudsim simulator, and the results show that the VM scheduling using VMS-MCSA is energy-efficient compared to other recent approaches.

Studies of a plasticized PEO + NH 4 PF 6 proton-conducting polymer electrolyte system and its application in a proton battery

Abstract: We have synthesized polyethylene oxide (PEO) + NH4PF6 polymer electrolyte films plasticized with ethylene carbonate (EC) and a mixture of ethylene carbonate and propylene carbonate (EC/PC) by using a solution cast technique. X-ray diffraction (XRD) results show a decrease in crystallinity from ∼67% in the unplasticized complex to 39% and 35% in the EC- and the EC/PC-plasticized complexes, respectively. Scanning electron microscopy (SEM) results show the formation of a fringed miscellar structure in the plasticized complexes. An increased coordination between dissociated ions and the ether oxygen of PEO on plasticization is established from the Fourier transform infrared (FTIR) studies. The ionic conductivity shows an enhancement of about two orders of magnitude on plasticization. The room-temperature ionic conductivities of the highest conducting compositions are obtained as ∼1.52 × 10−5 S cm−1 and ∼1.03 × 10−5 S cm−1 in the EC- and the EC/PC-plasticized complexes, respectively. The highest conducting compositions of plasticized polymer electrolyte films are used to fabricate proton batteries with the configuration Zn/ZnSO4.7H2O (anode) ‖Polymer electrolyte‖ PbO2/V2O5 (cathode), and different battery parameters are reported.