Divya Bansal
PEC University of Technology
95 Papers
522 Citations
Divya Bansal is an academic researcher from PEC University of Technology. The author has contributed to research in topics: Computer science & Malware. The author has an hindex of 16, co-authored 76 publications. Previous affiliations of Divya Bansal include Indian Institutes of Technology.
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Papers
Malware Analysis and Classification: A Survey
TL;DR: This survey paper provides an overview of techniques for analyzing and classifying the malwares and finds that behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknownmalwares into their known families using machine learning techniques.
Smart patrolling
TL;DR: A smartphone based sensing and crowdsourcing technique to detect the road surface conditions using DTW2 technique which has not been researched on data based on motion sensors and shows better accuracy and efficiency when compared with the existing techniques.
141
MAC scheduling and SAR policies for Bluetooth: a master driven TDD pico-cellular wireless system
M. Kalia,Divya Bansal,Rajeev Shorey +2 more
- 15 Nov 1999
TL;DR: This work proposes two new scheduling policies that utilize information about the size of the head-of-the-line (HOL) packet at the master and slave queues to schedule the TDD slots effectively and proposes a modified scheduling algorithm that gives good performance.
120
Data scheduling and SAR for Bluetooth MAC
M. Kalia,Divya Bansal,Rajeev Shorey +2 more
- 15 May 2000
TL;DR: The priority scheme achieves high throughput as compared to the packet-by-packet generalized processor sharing (PGPS) based policies while guaranteeing a minimal service to each active slave while the K-fairness policy is characterized by a tight fairness bound as well as high system throughput.
114
A smartphone based technique to monitor driving behavior using DTW and crowdsensing
TL;DR: The motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone using DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent.
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