Seminars & Events


Short introduction to C3I Center's Research

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:

  • Design and evaluate novel defense mechanism for CPSs
  • Test mathematical models
  • Evaluate the performance of formal models of CPSs

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.

Bio: My name is Anam Fatima and I am pursuing M.Tech(specialisation in Cyber Security) from Centre For Advanced Studies, AKTU, Lucknow. I did my B.Tech from Institute of Engineering & Technology, Lucknow in 2013 and have been working as full-stack JAVA developer at a MNC since 2013. My area of interest is Secure App development and would like to explore other areas of security such as Blockchain.

Abstract: The massive popularity and nearly 88%global market share of Android devices has made apps more vulnerable to targeted security attacks. Quick Heal Security Labs detected over 1 million Android malware in 2017. The proliferation of fake apps has been one of the biggest mobile security concerns in 2017. In addition, downloading apps from third-party app stores makes it easier for mobile malware writers to attack the Android devices. Most malicious apps can compromise the personal and sensitive information of users. Android security is built on a permission-based mechanism and hence it is very important for users to carefully observe the permissions an App asks. In this internship, I will be working on developing an Android app which will help in characterisation and detection of malicious Apps on the devicebased on permissions.

Bio: I am pursuing M.Tech in specialization Cyber Security from CENTER FOR ADVANCED STUDIES ,AKTU,LUCKNOW. And I have done my degree in Computer Science Engineering 2016 from Seth Sriniwas Agrawal Institude of Engineering & Technology, Kanpur.

Area of interst

  • Cyber Security
  • Malware Analysis(Static Analysis)
  • Security of Cloud Computing based on Trusted ComputingS

Detection of Malware in Advance Android Apps By using the Static Analysis

Abstract: Since the time Android made its entry into the Smartphone arena, it ruffled quite a few players; even the big ones. At present time, Google Android grips a tight 51.6% of the US market share. The open source nature of Android has made it the most popular mobile platform in the world. And many android malware affected to the android devices so we have detect the malware by detection techniques. Static analysis is preferred the any other techniques because it is safe to affection of the system and Portable Executable (PE) used to feature extraction of malware in the malware analysis. Hence in the project I will  detect the malware by malware features by use of PE files advanced android malware in huge of the data.