A data miner currently focusing on Cybersecurity domain and interested in detecting malwares via novel Deep Learning approaches, and designing end-to-end systems with near-real time predictive performance.
Education
2019-2022 (Expected)
2007 - 2011
Skills
Languages
- Python 2.x-3.x
- Java 8.x
- J2EE (JSP)
- Javascript
- Apache Spark
- Bash Scripting
- REST API
- HTML
- CSS
Parallelization
- multiprocessing
- Dask (pandas)
- MPI with C++
Containers & Cloud
- Docker
- AWS
Relational Databases
- Postgres
- Oracle
- DB2
- MySQL
Non-Relational Databases
- HBase
Operating Systems
- Linux
- Windows
Libraries & Frameworks
- Tensorflow
- Keras
- PyTorch
- LogStash
- Kafka
- (more . . .)
IDE
- PyCharm
- Jupyter Notebook
- Google Colab
- Eclipse
- IntelliJ
- Visual Studio Code
- Toad for Oracle
- DBVisualizer for IBM DB2
- Postman
- Putty
- MobaXterm
AI & ML Platforms
- GCP
- AWS
- Microsoft Azure
- Infosys NIA
- IBM Watson
RPA Platforms
- BluePrism
- UIPath
- Automation Anywhere
- Edgeverve AssistEdge
Source Control & Management
- GIT
- TFS
- SVN
- JIRA
- Confluence
Publications
Low Carbon Footprint Training for 1D-CNNs with Temporal Max-Pooling [Oct '24]
Highlights:
Reducing GPU space, training time and carbon footprint of training 1D-CNNs over extremely large malware sequences ($2^{28}$ or $\approx$ 268 million timesteps).
LockBoost: Detecting Malware Binaries by Locking False Alarms. [Jul '22]
Highlights:
Improving the performance of CNN by locking FPR and boosting TPR.
A survey on cross-architectural IoT malware threat hunting [Jun '21]
Highlights:
Cross-Architectural IoT Malware Detection and Classification.
Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms [Jan '21]
Highlights:
Improving malware detection for stringent false positive requirements.
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities. [Aug '22]
Highlights:
Survey on machine learning for Android malware
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT [Aug '19]
Highlights:
This paper was developed as part of my Machine Learning course at SFU by Prof. Greg Mori
Research Experience
Research Assistant
Data Mining Lab, SFU
- I am working with Prof. Ke Wang and my current research focus in on the domain of Data Mining and Cybersecurity.
- Worked on a two-tiered architecture using TensorFlow (Keras) library to improve the malware detection performance of existing state-of-the-art convolutional neural network (CNN) models.
- Automated the extraction and conversion of hidden layer information from CNNs into meaningful representation to be used for reducing misclassifications.
- Devised and implemented three different variants of the two-tiered architecture in Python, and evaluated their results on two different datasets and achieved significant performance improvements.
- Completed a directed study over Lateral Movement based stealthy attacks that poses critical threat in compromising network wide systems.
Jan 2019 - Present
Researcher, Intern
Data Privacy and Protection Technology Lab, Huawei Canada- Built and delivered a top performing neural network model which achieved 1st place among the team members. The delivered model was capable of detecting highest rate of malware for lowest rate of misclassifications all with affordable memory footprint.
- Developed a research paper on advanced assembly learning for cross-architectural IoT malware threat hunting. (Submitted to IEEE Symposium on Security & Privacy)
- Developed a survey paper on existing approaches for cross-architectural IoT malware threat hunting. (Published in IEEE Xplore). [Link to Paper]
- Dynamically informed static analysis to improve Opcode-based malware detection.
- Model compression using Knowledge Distillation techniques.
- Explored novel learning methods via 3D-CNNs and Grouped convolution for learning different feature types.
Jan 2021 - Dec 2021
Professional Experience
Technology Lead
Artificial Intelligence and Automation Services, Infosys Limited- Played the role of Feature Team Lead (Onsite + Offshore) driving the project development in a data provisioning platform for order visibility.
- Managed a development team of 14 resources – for UI, API and Spark modules.
- Planned Agile sprints including resource allocation and execution.
- Developed Spark Streaming based modules for processing real-time event data.
- Postgres database management activities for dev and test regions.
Jan 2018 - Dec 2018
Technology Analyst
NIA Expert Services and Solutions, Infosys Limited- Developed solutions for a petroleum client to automate gas quality and weather monitoring activities using Infosys’ Nia and RPA platforms along with open source technologies.
- Understood and gathered knowledge over existing organizational data from people, processes and legacy software systems to discover the potential areas of automation.
- Analyzed and Designed solutions in accordance to the client landscape, and estimated manual effort and cost for an RPA automation solution. Implemented Proof of Concepts for the proposed automation solutions.
- Engaged clients as well as account teams driving the requirements discussion, and managed a team of 3.
- Provided web-based inventory management of various product assortments for a confectionary brand.
- Played major role in designing and implementation of application code changes and enhancements in the form of change requests. These efforts fetched me “AWARD OF EXCELLENCE” recognition twice as well as appreciations from higher management and client side.
- Performed root cause analysis as well as estimation of efforts for defect/bug fixes.
- Framed test cases and performed unit testing, integration testing and regression testing.
- Involved in knowledge management activities at account level.
- Performed database management activities and performance tuning activities to improve UI responsiveness.
Nov 2013 - Dec 2017
Senior Systems Engineer
Retail, Commerce and Logistics, Infosys Limited- Performed application development, maintenance and 24x7 support for an e-commerce B2B solution, and participated in solution engineering for placing different types of orders like standard, seasonal and returns.
- Played the role of configuration controller at project level.
- Developed SQL scripts and procedures for generating reports in PPMS.
- Carried out tuning activities for increasing PPMS application performance.
- Contributed for sending weekly and bi-weekly knowledge management mailers at account level.
- Provided B2b and B2C sales maintenance and support for Webshop application’s SAP backend.
Jul 2012 - Oct 2013
Systems Engineer
Telecommunications and Networking, Infosys Limited- Infovista - a network performance management tool for providing a centralized and comprehensive view of performance data (Voice, Internet) across a network infrastructure.
- Played major role in upgrading of Infovista VFK kit in all production servers, and provided support for centralized monitoring of Voice/Internet data performance.
- Prepared SQL scripts for module level testing, rollout and functionality testing support, and carried out the Configuration Management activities for VFK Upgrade.
Sep 2011 - Jun 2012
Achievements
- Won the "AWARD OF EXCELLENCE" - twice in 2015 and 2016 awarded by Infosys Limited at unit level, for adding business values to my projects with my commitment, hard work and competency.
- Garnered "GOLD MEDAL" in my undergraduate studies from Anna University at state level in 2011.
- Played the role of "STUDENT CHAIRMAN" of Computer Science department in Sona College of Technology during 2010 - 2011.
- Had undergone "YOUNG STUDENT SCIENTIST PROGRAM", an initiative directly under the control of, by then, President of India, Dr. A.P.J Abdul Kalam, during my schooling, for three years in a row.
- Certified in ROBOTIC PROCESS AUTOMATION TOOLS: Automation Anywhere Advanced RPA Professional.
- Certified in "IBM RATIONAL APPLICATION DEVELOPER" for Websphere 6.0 during 2011.
- Completed training in “IBM Watson V3 application development” and in “Perform cloud data science with Azure machine learning” during 2017.
Certifications
Deep Learning Specialization
Issuing Organization: Coursera
Validity: May 2019 View Credential
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications. Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow. Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data. Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.
Machine Learning with Tensorflow on Google Cloud
Issuing Organization: Coursera
Validity: July 2019 View Credential
Frame a business use case as a machine learning problem. Gain a broad perspective of machine learning and where it can be used. Convert a candidate use case to be driven by machine learning. Recognize biases that machine learning can amplify.
