Prachi Jhanwar

N Queens with Obstacles

August 1, 2017 – September 1, 2017

The code is an implementation of traditional N-Queens problem with obstacles using 3 algorithms namely Depth First Search, Breadth First Search & Simulated Annealing. If an obstacle is present in a row of the board, the queen can be placed at either side of the obstacle without attacking each other. The code is a pure python implementation of basic search algorithms in Artificial Intelligence.

A new technique for detecting and blocking the installation of malicious software based on the reputation of the loadpoint n-grams

April 1, 2017 – June 1, 2017

Deploying a loadpoint entry is an integral part of installation for every malicious payload. It enables the payload to launch and execute every time the system boots. However, the loadpoint entries are not used as standalone detection entities. Instead, they are only cleaned up by anti-virus software, if the associated files are detected, either in a static scan or based on their behaviour. By identifying unique loadpoint trigrams from an internal telemetry collected over a predefined period and studying their associations with Ground Truth Good and Bad files, low confidence Good and Bad files and Unknown files, as well as honouring their prevalence and age, we were able to successfully validate the idea. Even in its most restricted form, based on the confidence for the disposition for the trigram, the technology is successfully used to either block an attack, prompt the user, or silently submit files and associated telemetry for backend validations. This research work is published in Virus Bulletin Conference 2017.

Implementation of Automated, Elastic resource provisioning for NoSQL clusters using Tiramola Framework

March 1, 2015 – Present

TIRAMOLA is a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. As a part of this project deployed private cloud at home network using eucalyptus 3.5 on CentOS 6.0. Installed multi-node Hadoop and HBase cluster over it. Also installed Ganglia for monitoring. TPCC benchmark was set for all the transactions. Neo load was used for load generation and distribution. Implemented Markov's Decision Process to decide at run time whether to add the VM, remove the VM or no change depending upon the load. Technologies used are JAVA, Cloud services, Hadoop, HBase, Machine learning algorithms.