I am an undergraduate Computer Science student from B.M.S. College Of Engineering. My areas of interest include Cyber Security, Network Analysis and Data Science. I also enjoy competitive programming.
PROJECT: Log Analysis System
The main idea behind my project is to provide the administrator overseeing a server a smooth interface to view and analyze the traffic that the server is receiving and to present a concise overview of different details i.e. how many different clients access it, from which locations around the world and other statistics. The tool is capable of identifying a fairly good percentage of malicious attacks like SQL injection and cross-site scripting attempted by the clients and
reports them to the admin in a meaningful fashion.
Bio: I am a third year student from MIT Manipal. My primary interests lie in Cyber Security, within which I mostly associate myself with Network Analysis and Web Penetration Testing. But I am working towards expanding my purview to other sectors like Honeypots, Firewalls etc.
Currently, I am working on a Threat Intelligence System which will help us monitor attackers along with the ability to analyse their modus operandi.
The project has two primary objectives
· Near real time centralized monitoring of both insider and external cyber-attacks on cyber assets of critical infrastructure.
· Design and Development of a centralized security information and event management application
I am responsible for the conversion of the collected data into a meaningful manner and present it to the layman such that he/she might recognize and be aware of threats oriented in cyber space.
I am a sophomore at R.V. College Of Engineering. My primary interest lies in Artificial Intelligence, and I am currently associated with Machine Learning and Data Science. But I am also working towards expanding my purview to other sectors of AI like Deep learning, Artificial Neural Networks and its mathematical part comprising Linear Algebra, Probability and Statistics. Currently, I am working on Security of Cyber-Physical Systems(CPSs) from both Cyber and Physical attacks by identifying attacks vs non-attacks using Machine Learning Algorithms.
The project has the following objectives:
Bio: I am doing M.Tech specialization in Cyber Security from Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow.I have done B.Tech. in CSE from GurukulKangriVishwavidyalaya, Haridwar , Uttarakhand in 2017.
Project Title: A hybrid approach to detect advanced malware at large scale.
Recently Symantec reported that “Implanted Malware Attacks the Software Supply Chain”.
According to WatchGuard report “Approximately 30 percent of malwares are new and it was not caught by legacy anti-virus so there must be needed some advanced techniques”. & “Old threats become new again attackers have been evolving old attack technique with new obfuscation methods”.
This inspire me to analyse the new malwares. In this work we will use hybrid approach this includes static approach and dynamic approach to detect known and unknown malwares.
Bio:I am currently pursuing M.Tech in CSE with specialization in Cyber Security at Centre For Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow. I have completed my B.Tech in CSE from Greater Noida Institute of Technology, Greater Noida, U.P. in 2017.
Project Title : A Runtime analysis to detect malware.
Abstract:On the daily basis, Quick Heal detected around 2017722 malware, 31790 ransomware 189505 exploits and 58874 PUA and adware. Looking at this report that we have we can automate the proces to detect the malware. It is very necessary to understand the run time behaviour of malware as some of the features are displayed on run time. On execution of malware, you can monitor its behaviour such as what folders it tries to access, etc. My major goal is to detect the malware using Dynamic Analysis of malware. We can automate using Neural Network and Deep Learning approaches so one of the approach is using the LSTM (Long Short Term Memory) network in which we can provide the earlier reports and develop the network whether it is malware or not.
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