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.

Recent advances in the diagnosis of COVID-19: a bird's eye view.

Abstract: The COVID-19 pandemic is still escalating and has shaped an extraordinary and pressing need for rapid diagnostics with high sensitivity and specificity. Prompt diagnosis is the key to mitigate this...

Novel voltage-mode cascadable all-pass sections employing grounded passive components

Abstract: This paper presents two new first-order voltage-mode (VM) cascadable all-pass (AP) sections, employing two differential voltage current conveyors (DVCCs) and three grounded passive components. Both...

A comparison of two hybrid ensemble techniques for network anomaly detection in spark distributed environment

Abstract: In this paper, the authors have compared ensemble methods in Spark supported distributed environment. With ever changing attack trends traditional machine learning algorithms fail to detect new types of network based attacks. Machine learning techniques therefore need to be improved. Secondly, there is need for faster and accurate detection algorithms and study of distributed frameworks like Apache Spark is much needed. Thirdly, dataset size reduction plays major role in machine learning algorithms and therefore effort is required to reduce data sizes without affecting the performance metrics. In this work KMeans Clustering and GMM based Clustering have been used to reduce the dataset size while maintaining the diversity of the traffic. The clustered data acts as input to Random Forest Classifier. The RF classification has also been done for class-wise detection of attacks. The outputs from KMeans based RF classification, GMM based classification and class-wise RF classifications were taken as input for base learners of ensemble methods. Two ensemble methods, namely, Weighted Voting based AdaBoostensemble and Stacking based ensemble have been studied and compared. Two dataset, namely, NSL-KDD and UNSW-NB15 have been used to carry out the study. An accuracy of 78.9% and 58.54% for KDDTest+ and KDDTest-21 with KM+RF was achieved. An accuracy of 79.98% and 63.19% were achieved with GMM+RF. Furthermore, an accuracy of 82% was achieved for UNSW-NB15 with KM+RF whereas an accuracy of 84% was achieved for the same with GMM+RF. With Weighted Voting based AdaBoost ensemble accuracies of 90.46% and 83.32% for KDDTest+ and KDDTest-21 were achieved respectively. Similarly an accuracy of 91.31% was achieved for UNSW-NB15 Test data with Weighted Voting based AdaBoost ensemble. With Stacking based ensemble accuracies of 85.24% and 78.20% were achieved for KDDTest+ and KDDTest-21 respectively. Lastly an accuracy of 89.57% was achieved with Stacking based ensemble for UNSW-NB15 Test dataset. Overall we were able to achieve better detection rates and accuracies with reduced false alarm rates by using ensemble methods. Tests were conducted on different machines by varying the number of executor cores to study time latency in distributed Spark environment.

Impact analysis of rank attack with spoofed IP on routing in 6LoWPAN network

Abstract: The routing protocol — RPL for low power and lossy networks is the fundamental routing convention of 6LoWPAN The introduction of rank idea in RPL serves numerous intentions, including path optimization, prevention of loops, and managing control overhead Since the RPL is currently used as the main routing protocol, for large scale low-power and lossy networks, the concept of “Rank” can lead to a vulnerable performance due to the internal threats in RPL based networks It is studied that an attack on the rank property with legitimate IP can degrade the network performance in the aspect of the delivery ratio and end-to-end delay And this can be further degraded if node's IP gets spoofed by the attacker node This work discuss the impact of rank attack when the attack is launched by the node having a spoofed IP address Performance is measured in terms of packet delivery ratio and end-to-end delay of packets The observations show that the Rank attack with spoofed IP has more impact on the degradation of delivery ratio