Athul Jayaram is a former cyber risk consultant at a big four consulting organization. He is currently a full time bug bounty hunter ranked top 100 in Bugcrowd and Hackerone. He has experience in securing critical assets of corporate clients across sectors like banking, finance, automobiles and technology. His area of expertise involves vulnerability assessment and penetration testing of web applications, thick client applications, thin client applications, android applications, iOS applications, networks, incident response & recovery and digital forensics. He also holds a vast understanding of the IT Industry.
CVE-2019-2706 was issued by Oracle for his critical vulnerability discovery in the Middleware used by Corporate Applications.
Featured in Hall of Fames and Acknowledged by Google, Microsoft, Sony, Intel, Nokia, Lenovo, Oracle, SAP, DarkMatter, Upwork, Ford, Genymotion, Trend Micro, DJI, United Nations, UN Women, Indian Angel Network, Zomato, De Nederlandsche Bank, Cambridge University, Visma, Auditwolf, 1Password, Indian Angel Network, Chalk, Inflectra, Wellthy and many others.
His key interests are web application penetration testing, mobile application penetration testing, server penetration testing and network security assessment.
Research Paper Published on International Journal of Innovative Research in Science, Engineering and Technology IJIRSET Vol. 5, Issue 9, September 2016
An Internet of Things Framework for Automation and Remote Control of Home Appliances View Paper
Lean Six Sigma Approach for Global Supply Chain Management using Industry 4.0 and IIoT, published on IEEE View Paper
An Enterprise Resource Management Model for Business Intelligence, Data Mining and Predictive Analytics, published on IEEE View Paper
Smart Retail 4.0 IoT Consumer Retailer Model for Retail Intelligence and Strategic Marketing of In-store Products
A Data Mining System for Sentiment Classification of Indian Currency Demonetisation using Naive Bayes Classifier Algorithm
An IIoT quality global enterprise inventory management model for automation and demand forecasting based on cloud, published on IEEE View Paper