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GEURE-2021 Registrations invited from students

GEURE-2021 Registrations invited from students

“Graphic Era Deemed to be University Research Expo (GEURE-2021) Theme – UN 17 Goals” will be held on 10th April 2021 at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India. This expo is organized by GEU IEEE STUDENT BRANCH, Department of Computer Science and Engineering & Institution’s Innovation Council (IICs), Ministry of Education (MoE), Govt. of India. This event is intended for the students to showcase their research and projects.

Event name: Graphic Era Deemed to be University Research Expo (GEURE-2021) Theme – UN 17 Goals

Venue: CS&E Block 

Timings: 10:00 am – 1:00 pm, 

Date: 10/04/2021

Registration link: https://forms.gle/gcZj5BgdCq8GPg2G7

Last date of registration 31 March 2021.
 The certificates will be distributed to all the participants. For more information about themes please visit 

https://www.un.org/development/desa/disabilities/envision2030.html

 

Notice: Regarding Mini Project of 6th semester

Notice: Regarding Mini Project of 6th semester

All the students of 6th semester have already received the topics for Mini project. Apart from being a compulsory component of the evaluation scheme miniprojects are essentially queried during placement interviews in the next semester.

Keeping in view of the importance all 6th semester students are directed to inform their mentors about the project they have chosen by Monday, 1st March, 2021. Any technical queries should be discussed with the faculty resource person before finalizing the topic. The information of the projects chosen by the students will be directly taken from the mentors. Also, a mini project progress evaluation will be held during end of March 2021. No clarifications will be provided later to students who fails to inform their mentors regarding their projects.

Students can also contact the following for any clarification regarding mini projects between 4PM to 5PM by Monday, 1st March, 2021

Dr. Devesh Pratap Singh, Prof and Head

D. Bordoloi, Associate Prof

Graphic Era Deemed to be University Research Expo (GEURE-2021)

Greetings of the day! I am glad to inform you that an event “Graphic Era Deemed to be University Research Expo (GEURE-2021) Theme – UN 17 Goals” to be held on 10th April 2021 at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India. This expo will be organized by GEU IEEE STUDENT BRANCH, Department of Computer Science and Engineering & Institution’s Innovation Council (IICs), Ministry of Education (MoE), Govt. of India. This event is intended for the students to showcase their research and projects.

Event name: Graphic Era Deemed to be University Research Expo (GEURE-2021) Theme – UN 17 Goals

Venue: CS&E Block 

Timings: 10:00 am – 1:00 pm, 

Date: 10/04/2021

Registration link: https://forms.gle/gcZj5BgdCq8GPg2G7

Last date of registration 31 March 2021.

The certificates will be distributed to all the participants.

For more information about themes please visit 

https://www.un.org/development/desa/disabilities/envision2030.html

Urgent Notice: List of 8th Sem students not completing Sales Force Trailmix program risking Failed grade in Seminar

A number of students currently in 8th semester opted for Sales Force Developer Trailmix program. However only a few of those students have completed the modules. The following students have not completed the program.

It is reiterated that the Seminar Marks (100 marks) of 8th semester for these students are completely based on this. If they do not complete the modules, they will be summarily failed in the Seminar component which will result in back in 8th semester and subsequent delay of completing the degree.

The following students are directed to complete the program within 48 hours without fail.

S.No. Enrollment # Student Name Branch
1 GE-172012468 Abhishek Pandey CS(GEN)
2 GE-172012479 Akash Mittal CS(GEN)
3 GE-172012882 Akash Negi IT
4 GE-172012484 Amartya Sen CS(GEN)
5 GE-172012486 Amit Bisht CS(GEN)
6 GE-172012487 Anant Jakhmola CS(GEN)
7 GE-172012491 Ananya Rana CS(GEN)
8 GE-172012503 Anshika Chandel CS(GEN)
9 GE-172012512 Apoorv Agarwal CS(GEN)
10 GE-172012747 Arti Bhatt CS(CC)
11 GE-172012704 Aryan Bhatt CS(BDA)
12 GE-172012521 Avikal Thapliyal CS(GEN)
13 GE-172012523 Aviral Negi CS(GEN)
14 GE-172012705 Ayush Gairola CS(GEN)
15 GE-172012888 Bhumika Joshi IT
16 GE-172012706 Chandrima Kundu CS(BDA)
17 GE-172012540 Divyansh Lakhera CS(GEN)
18 GE-172012542 Diwas Hemdani CS(GEN)
19 GE-172012559 Isha Negi CS(GEN)
20 GE-172012709 Kartik Bahuguna CS(BDA)
21 GE-172012767 Komal Singhal CS(CC)
22 GE-172012894 Mansi Bhandari IT
23 GE-172012577 Mohd Aasif CS(GEN)
24 GE-172012587 Neha Gupta CS(GEN)
25 GE-172012592 Nirbhay Maitra CS(GEN)
26 GE-172012593 Nishant Verma CS(GEN)
27 GE-172012599 Parth Bhutani CS(GEN)
28 GE-172012607 Pushkar Shukla CS(GEN)
29 GE-172012780 Rakshit Belwal CS(CC)
30 GE-172012895 Ritik Agarwal IT
31 GE-172012783 Samarth Gupta CS(GEN)
32 GE-172012629 Sanjana Verma CS(GEN)
33 GE-172012634 Shivam Chauhan CS(GEN)
34 GE-172012785 Shivanshu Gupta CS(CC)
35 GE-172012641 Shreya Wason CS(GEN)
36 GE-172012899 Shubham Kumar Singh IT
37 GE-172012718 Sohom Ghosh CS(BDA)
38 GE-172012645 Sparsh Kishore Kumar CS(GEN)
39 GE-172012569 Swaleha CS(GEN)
40 GE-172012719 Tushar Bharadwaj CS(BDA)
41 GE-172012657 Ujjwal Shrivastav CS(GEN)
42 GE-172012659 Vaibhav Agarwal CS(GEN)
43 GE-172012666 Vidhi Arora CS(GEN)

Mini Project Notice for 4th Semester Students

 

Mini Project Notice for 4th Semester Students

The following lists the mini-project topics for the 4th semester students of BTech-CSE/CSE-DS/CSE-ML/CSE-CC/CSE-IS/CE/CST/CE-SE/IT students who have NOT OPTED for the GCCF. Along with the project list, name(s) of faculty member is(are) given who would act as the resource person for the topic. Note that you are required to understand, study and implement the topic on your own. The Resource person may only be contacted if you need advice on things like dataset, suitable tools etc. Resource persons will NOT advice you in any way in the coding part, they will also NOT evaluate your projects.

Mini Projects will be done individually. No group work is allowed. Mini project is a compulsory part of your scheme, Absentees and Failed students have to resubmit as Back.

Expected Evaluation Date: 1st week of May. Exact dates will be announced later.

Marks: 100 marks.

Note: Students who have opted and selected for Mini Projects through GCCF modules will be evaluated through that mode only. This notice is for the Non GCCF students.

Serial No 1 Resource person: Hemant Singh Pokhariya
1.     Investigating the Land Use Land Cover change Detection by using GIS and RS Techniques:

Change detection is widely used to estimate deforestation, LULC, urban growth, shifting cultivation, etc. If the change detection process on earth surface is carried out accurately and timely, then the interaction and relationship between humans and natural phenomena can be better investigated leading to a better utilization of the natural resources.

Serial No 2-4 Resource person: Deepak Uniyal
2.     Misinformation: Detecting and combating misinformation on Social Media
3.     Detecting Offensive Content on Social Media:

Read the following articles to get an idea about the topic –

i) https://www.aclweb.org/anthology/2020.lrec-1.531.pdf

ii) https://paperswithcode.com/task/abuse-detection

iii) http://sersc.org/journals/index.php/IJAST/article/view/14949

4.     Smart Dustbin – Garbage Management:

Please go through following articles and discuss the problem statement with me (Deepak Uniyal) before finalizing the statement. The links given below are for reference purpose and you should not exactly copy these ideas.

i) https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042027/pdf

ii) https://www.instructables.com/Smart-Garbage-Monitoring-System-Using-Internet-of-/

iii) https://www.electronicshub.org/smart-dustbin-using-arduino/

Serial No 5-6 Resource person: Dr. Sachin Sharma
5.     IoT & Smart City Technology:

Smart cities use Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data. The cities then use this data to improve infrastructure, public utilities and services, and more.

6.     AI & ML in Smart Urban Areas:

Machine learning generally takes the data generated by several apps such as Health MD applications, internet-enabled cars, etc. and leverages it to identify patterns and learn how to optimize the given set of services. Its tools are able to personalize the smart city experience by aggregating information about the most used roads in a city and then apply it to a transportation system.

On the other hand, machine learning and AI can be helpful in waste collection and its proper management and disposal which is a vital municipal activity in a city. Thus, the technology for smart recycling and waste management provides a sustainable waste management system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives.

Serial No 7-8 Resource person: Dr. Mohd. Wazid
7.     AI enabled authentication scheme in IoT:

Students can design AI enabled authentication schemes for various computing domains like smart homes, smart transportation system, smart healthcare systems.

8.     Access control schemes in Industry 4.0:

Students can design access control schemes for Industry 4.0 communication.

Serial No 9 Resource person: Ankit Tomar
9.     Customer Segmentation in retail sectors:

Segment the customers based on their gender, age, interest. It is useful in customized marketing. Customer segmentation is an important practice of dividing customers based on individual groups that are similar.

Serial No 10 Resource person: Garima Sharma
10.  Performance Analysis of Big Data and Data Science Tools:

Contrast different available tools and techniques in big data stack and analyze performance of each tool , handson on different programming techniques using real world use cases.

Serial No 11 Resource person: Kireet Joshi
11.  Machine learning in Healthcare:

Machine learning has virtually endless applications in the healthcare industry. Today, machine learning is helping to streamline administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.

Serial No 12 Resource person: Saurabh Mishra
12.  Voice based Email service for visually challenged people:

The main benefit of this system is that the use of keyboard is completely eliminated, the user will have to respond through voice and mouse click only.

Serial No 13-14 Resource person: Ashwini Kumar
13.  Visual Question Answering using Deep Learning:

Visual Question Answering is a research area about building an AI system to answer questions presented in a natural language about an image.

A system that solves this task demonstrates a more general understanding of images: it must be able to answer completely different questions about an image, oftentimes even addressing different sections of the image.

14.  Social Media Sentiment Analysis using Twitter Dataset:

Twitter boasts 330 million monthly active users, which allows businesses to reach a broad audience and connect with customers without intermediaries. On the downside, there is so much information that it’s hard for brands to quickly detect negative social mentions that could harm their business.

That’s why sentiment analysis, which involves monitoring emotions in conversations on social media platforms, has become a key strategy in social media marketing.

Listening to how customers feel on Twitter allows companies to understand their audience, keep on top of what’s being said about their brand, and their competitors, and discover new trends in the industry.

Serial No 15-16 Resource person: Dr. Manoj Diwakar
15.  Multimodality medical image fusion:

Tools: MATLAB/Python, Description: An image fusion based on multimodal medical images renders a considerable enhancement in the quality of fused images. An effective image fusion technique produces output images by preserving all the viable and prominent information gathered from the source images without any introduction of flaws or unnecessary distortions.

16.  Medical Image denoising:

Tool: MATLAB/Python, Description: medical imaging is frequently justified in the follow up of a disease which is already diagnosed and treated. Medical images like any other form of imaging techniques are susceptible to noise and artifacts. Noise can be random or white noise with an even frequency distribution or frequency dependent noise introduced by a device’s mechanism or signal processing algorithms. The presence of noise makes the images unclear and may perplex the identification and analysis of diseases which may result heavy losses including deaths. Hence, denoising of medical images is a mandatory and essential pre-processing technique for further medical image processing stages.

Serial No 17 Resource person: Dr. Priya Matta
17.  IoT Based Home Automation:

This project will allow you to control the devices and appliances present at your home via an app or even via voice commands..

Serial No 18-25 Resource person: Dr. Preeti Mishra
18.  Cloud based Android Malware Feature Extraction:

Write a script that performs the following function s(features extracted by static analysis)

a. For the apks present in dataset, diassemble the apk

b. Extract the permission features from the manifest files.

c. The features extracted should be converted into a csv file. Thus for each apk there should be a row in the csv. The columns of the csv will be the different permission. The value for each apk will be 0 or 1 based on permission present or absent.

* Use a cloud platform for performing project. Dataset- Use any dataset available at this link https://github.com/traceflight/Android-Malware-Datasets

19.  Android Dynamic Feature Extraction (API extraction):

Write a script that performs the following functions (features extracted by dynamic analysis)

a. For the apks present in dataset, Runs the apk in a secure environment (in a VM)

b. Using the tools extract the API calls (AndroidTamer->MobSF can be used here) that the apk makes during its execution.

c. Converts the obtained API calls details into a csv file. Thus for each apk there should be a row in the csv. The columns of the csv will be the different API calls. The value for each apk will be 0 or 1 based on API call present or absent

Android Tamer is a VM consisting of several Android analysis tools ranging from analysis to pentesting. 8GB RAM is must otherwise the speed of the application slows down w.r.t. responsiveness.

20.  Android APP penetration testing:

Perform penetration testing of the web based mobile apps (use the BurpSuite for this). Exploit the XSS vulnerability by using browser on the emulator. You will need to set up proxy setting on the emulator. Setting will also be made in the Burpsuite to intercept the requests from app and then these requests can be tampered. Create a demo app(web based) and include a search text box in this. When you query using this search box, you can intercept this search string using Burp and then also tamper it.

21.  Andoid APP penetration testing and security breach:

Try to exploit the vulnerable components of an app (benign app) from another app (malicious app). Do the following

a. Create a demo app with a login screen

b. Once valid credentials are provided it should take you to the next screen/page.

c. Try to open the second screen/page directly from the malicious app.

d. Then also provide a fix for this vulnerability.

e. Once done, now again try to access the second screen/page from the malicious app by encoding permissions in the malicious app.

This scenario demonstrates how a malicious app tries to access secure area of a genuine app with code vulnerabilities.

22.  Performance analysis of Web Browser Forensic Tools:

Demonstrate and compare the performance of any three web browser forensics tools.  Analysis the activity logs of any one web browser.

23.  IoT Penetration Testing:

Find the vulnerabilities in any IoT device using penetration testing tools. Parse the logs and generate some report as an output.

24.  Cloud based Web application development:

Develop and host some innovative web application using cloud services such as AWS EC2, AWS S3 and RDS services etc.

25.  Generate and Parse Cloud User Activity Logs:

Extract the logs of services associated with your account. Parse them and convert into some meaningful format using python APIs. You can use CloudTrail, CloudWatch  etc. to monitor the activity logs. Write a script to extract and parse such logs.

Serial No 26-27 Resource person: Sarishma
26.  Comparative analysis of different forensic tools:

Purpose:  To compare and evaluate the performance of different tools by using quantifiable data. To find out the best tool after running test cases on 4-5 different forensic tools.

Questions to be answered:

i)The tool’s features: How does it process, index, search, etc.?

ii)The tool’s ability to obtain artifacts:  How many artifacts?,  Location of those artifacts, How many relevant artifacts? , How quickly were artifacts obtained? (time)

iii)The tools searching speed: What differences are present between the tool’s searches?, Test under different scenarios

iv)The tool’s ability to recover deleted files: What data can be recovered?, Can it be used to create a time series analysis with support of artifacts?

v)The tool’s level of difficulty:  Is it easy for beginners?, Designing test cases is easy?, Evaluate the analysis provided by the tool.

 

Testbed setup:

Hardware used: Use virtual machines for testing. Do not use your personal system as there are high chances of damage. Use a virtual machine via VMware or VMWorkstation. Run the test case scenario to setup the scene. Create a copy of the VM by using the tool FTK Imager.

Software used:

i)FTK Imager

ii)FTK 6.1 (Forensic Toolkit)

iii)SANS Investigative Forensic Toolkit Workstation

iv)EnCase Forensic v8

v)Magnet IEF

vi)VMware vSphere

27.  Image Steganalysis for forensic investigation of hidden artefacts:

JPEG (other formats) – analysis of image formats in order to find out hidden information which might be stored. It can be extended by doing analysis of various other multimedia files including audio, video, network packets. A detailed study of formats along with their threat vectors has to be done.

 

Mini Project Notice for 6th Semester Students (CSE-Core and IT)

Mini Project Notice for 6th Semester Students (CSE-Core and IT)

The following lists the mini-project topics for the students of BTech-CSE(Core)-6th semester and BTech(IT), 6thsemester. Along with the project list, name(s) of faculty member is(are) given who would act as the resource person for the topic. Note that you are required to understand, study and implement the topic on your own. The Resource person may only be contacted if you need advice on things like dataset, suitable tools etc. Resource persons will NOT advice you in any way in the coding part, they will also NOT evaluate your projects.

For IBM specializations students, mini projects will be handled by IBM directly.

Mini Projects will be done individually. No group work is allowed.

Expected Evaluation Date: 1st week of May. Exact dates will be announced later.

Marks: 100 marks.

Serial No 1 Resource person: Hemant Singh Pokhariya
1.     Investigating the Land Use Land Cover change Detection by using GIS and RS Techniques:

Change detection is widely used to estimate deforestation, LULC, urban growth, shifting cultivation, etc. If the change detection process on earth surface is carried out accurately and timely, then the interaction and relationship between humans and natural phenomena can be better investigated leading to a better utilization of the natural resources.

Serial No 2-3 Resource person: Dr. Sachin Sharma
2.     IoT & Smart City Technology:

Smart cities use Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data. The cities then use this data to improve infrastructure, public utilities and services, and more.

3.     AI & ML in Smart Urban Areas:

Machine learning generally takes the data generated by several apps such as Health MD applications, internet-enabled cars, etc. and leverages it to identify patterns and learn how to optimize the given set of services. Its tools are able to personalize the smart city experience by aggregating information about the most used roads in a city and then apply it to a transportation system.

On the other hand, machine learning and AI can be helpful in waste collection and its proper management and disposal which is a vital municipal activity in a city. Thus, the technology for smart recycling and waste management provides a sustainable waste management system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives.

Serial No 4-5 Resource person: Dr. Mohd. Wazid
4.     ML security schemes :

Students can design some scheme for secure machine learning models.

5.     AI enabled blockchain based security techniques in IoT:

Students can design AI enabled blockchain based security techniques for various computing domains like smart homes, smart transportation system, smart healthcare systems.

Serial No 6 Resource person: Ankit Tomar
6.     Investigation of people Emotion Recognition in Videos:

Audio- and video-based emotion recognition in the wild is still a challenging task. In the wild (as opposed to controlled) refers to real-life and uncontrolled conditions of data acquisition, including changing and challenging lighting conditions, indoor and outdoor scenarios, sensor and environmental noise and resolution issues, motion blur, occlusions and pose changes, as well as uncontrolled scenarios from which the emotional expressions are produced.

Serial No 7 Resource person: Garima Sharma
7.     Text Mining, Structure Mining, Link Analysis:

Incorporate Data Mining in Big Data World Real Time applications, Work on Structure and Graph Mining, Analysis on relationship between different attributes using graph mining.

Serial No 8-9 Resource person: Ashwini Kumar
8.     Sentiment Analysis in Healthcare using Machine Learning:

In today’s competitive healthcare industry, hospitals, physician practices, and health systems are becoming more focused on improving the patient experience. Healthcare facilities offering convenience, functionality, and access that modern consumers expect are emerging leaders in their spaces and improving their bottom-line.

Today’s consumers have a ton of options when it comes to choosing healthcare services. Therefore, healthcare facilities need to be proactive in attracting and retaining patients. One of the ways they can do this is by using patient sentiment analysis.

9.     Offensive Language detection using Deep Learning:

Hate Speech Detection is the automated task of detecting if a piece of text contains hate speech.

Serial No 10-11 Resource person: Dr. Manoj Diwakar
10.  Medical Image Fusion and denoising using deep learning:

Tool: Matlab/ Python, Description:  In this project, two different modality medical images are fused and denoised using deep learning concept to get single more informative output medical fused image.

11.  Brain tumor detection:

Tools: Matlab/Python; Description: The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the various anatomical structure of the human organ. Detection of human brain abnormal structures by basic imaging techniques is challenging.

Serial No 12 Resource person: Dr. Priya Matta
12.  IOT based Traffic Signal Monitoring & Controller System:

This system will automate complete traffic signaling system and will monitor traffic signal densities. This system will provide an option to the controllers to override any signal in case of emergency.

Serial No 13-15 Resource person: Ramesh Singh Rawat
13.  Malware Detection and Mitigation Cloud Environment:

Investigate the type of Malware, e.g. Botnets in the virtualized environment. Detect the malware infection, propagation and/or attack in the virtualize environment and then in cloud computing.

14.  Evasion of Machine Learning Botnet Detection Models:

Machine learning is a popular approach to malware detection because it can generalize to never-before-seen malware families and polymorphic strains.  Recent work in adversarial machine learning has shown that models are susceptible to gradient-based and other attacks. So, to study/investigate the various invasion techniques against the state-of-the-art malware detection techniques.

15.  Net-Flow Analysis for Malware Detection:

The various Machine Learning techniques have been applied to Malware Detection. So, to study the state-of-the-art machine learning techniques used for Malware detection and develop a model to detect the latest robust and stealthy malware in the wild.

Serial No 16-17 Resource person: Vishu Tyagi
16.  Sentiment Analysis on Twitter data:

Sentiment analysis is a kind of data mining where you measure the inclination of people’s opinions by using NLP, text analysis, and computational linguistics. We perform sentiment analysis mostly on public reviews, social media platforms, and similar sites.

17.  Recommendation System:

Recommendation System is a filtration program whose prime goal is to predict the rating or preference of a user towards a domain-specific item or item.

Serial No 18-28 Resource person: Dr. Preeti Mishra
18.  Use the following dataset for IoT network intrusion dataset for cloud based attack analysis. https://ieee-dataport.org/open-access/iot-network-intrusion-dataset:

Write a script to perform the following functionality.

a)         Using Convolution neural network, detect the IoT malware as dynamic analysis. (Requirements: VM, Raspberry pi 3B+ model, Cloud Environment).

b)         Analysis of cyber attack for Raspberry pi Malware.

(Requirement: AWS Cowrie services, Ubuntu Server, Mirai code (download from github), Raspberry Pi).

19.  DDoS attack on the Raspberry pi model integrated with Cloud:

Write a script to generate and analyze DDoS attack on the Raspberry pi model integrated with AWS IoT Core

20.  IoT Botbet Dataset analysis using Cloud Services:

Mirai Dataset analysis using Cloud based Machine Learning Platform.

21.  Cloud based adversary aware Android IDS based on static features:

The IDS system should be safe from the machine learning limitations which sometimes cause a system to prone to evasion and poisoning attacks.

Basically, you have to work on designing secure ML based algorithm for android malware analysis. The dataset info will be shared by the Resource person

22.  Web Browser Forensics:

Design and implement an efficient web browser forensics system. Consider any web browser such as Google Chrome or Firefox for analysis.

23.  Hypercall Attacks in Para Virtual Machines:

Demonstrate the attack scenario using HInjector (tool that has a series of hypercall attacks) that can be used for testing that can be used to exploit the hypervisor (Xen or KVM etc).Generate some logs.

24.  DoS Attack Analysis at Para Virtual Machine which exploit hypercall interface:

Perform the DoS attack to exploit the hypercall interface. The virtualization platform specifications are the XenServer (latest version) and the client (XenCenter). The both guest OSs can be Ubuntu or Windows. Do the following:

a.       Create a test set up with Xen (or any other hypervisor) base virtualized platform.

b.      Create two guest virtual machines, one can be a legitimate web server and other can play the role of the attackers.

25.  Performance analysis of Virtual Machine Introspection:

Demonstrate the impact of the virtual machine introspection tools on the performance of the virtual machines. Do the following:

a.       Install VMI tools (for example, LibVMI (library for Xen or KVM)) in Dom 0 or privileged domain.

b.      Check the performance of virtual machines while monitored.

26.  Hypercall Log Generation and Analysis:

Monitoring the hypercalls (from virtual machine to hypervisor) and collect the hypercall traces. For Xen hypervisor, a tool like Xentracer and XenAnalyzer can be used. Do the following:

a.       Hypercalls can be monitored in Dom 0 (privileged domain) using Xentrace plugin.

b.      Store them in hard disk, then XenAnalyze plugin is used to convert this data in the human readable format.

27.  Security Analysis of Cloud User Activity Logs:

Perform the security analysis of the cloud services to find if there is any vulnerability present. You can use CloudTrail, CloudWatch  and Sagemaker etc. to monitor and analyze the activity logs of various services associated with your account. Write a script to extract, parse and analyze such logs using machine learning.

28.  Cloud based data analytics for network attack dataset analysis:

Perform and analysis the cloud based data analytic tools and algorithms to analysis the network attack dataset. You have to write the code using the cloud based ML APIs. No GUI based platform is allowed.

Serial No 29-31 Resource person: Sarishma
29.  Task scheduling algorithm for running IoT applications on Cloud:

Genetic algorithm, ant colony optimization etc. can be used to design a model which uses an algorithm to schedule tasks – with focus on increasing performance, reducing energy consumption, reducing resource allocation time – geographically over cloud end points. Use of CloudSim simulator is encouraged as it helps to simulate the exact environment which is needed as defined by our constraints of IoT based scenario.

30.  Using bloom filter for securing cloud forensics logs:

Implement bloom filter on cloud’s agent based or log-based data. This increases the security and trust of users as well as investigators by taking out the scope of manipulation out of the hands of CSP. Two different implementations of bloom filter can be implemented, and the results compared. Use of CloudSim simulator along with Java is recommended.

31.  To work on an exhaustive coverage of ways to steal a Virtual Machine in cloud environment:

There are ways to get hold/ownership of a virtual machine in a cloud environment. In this project, we aim to demonstrate the exploitation of an unprotected virtual environment and acquire virtual machine data. All the ways through which this can be done will be exhaustively covered one by one by attacking the VM’s through different means. Any established penetration testing software tool will be used (can use multiple) along with VMware Virtual Infrastructure. By working on this, we will generate a comprehensive guide as to how a VM can be exploited and a comparative analysis of “security of traditional hardware” as compared to “virtual hardware”.

Serial No 32-35 Resource person: Vikas Tomer
32.  Why an Employee Leaves: Predicting using Data Mining Techniques
33.  Sentiment Classification of News Headlines
34.  Crop Production Analysis and Classification
35.  Sales Forecasting using Walmart Dataset

 

Notice: Selected Students List for Mini Projects through GCCF (4th Sem)

 Notice: Selected Students List for Mini Projects through GCCF (4th Sem)

In continuation to the Notice regarding Mini Projects through GCCF for 4th semester, the following students have been selected to complete their mini projects through the GCCFs modules. The details of the same was already provided in the previous notice.

List of Selected Students:    http://csitgeu.in/wp/gccflist.xlsx

Note1: The students have to complete all the modules latest by 31st March, 2021

 

Notice: AWS Trainings in February, 2021

Notice: AWS Trainings in February, 2021

GEU has an ongoing collaboration with AWS Academy. To extend your learning about AWS, students are offered a complimentary place at the Live virtual AWS Trainings happening in January, 2021. Duration of these virtual sessions varies between 90 minutes to 5 hours and will provide an introduction to the topics from AWS Trainers. Majority of the foundation topics have 2 sessions

To confirm your registration, please select from one or several of the times below (all India Standard Time) and follow the registration links. Please register using your institute email.

Primary registration page with course details available here.

Course Date Time Register Now
Cloud Essentials
AWS Cloud Practitioner Essentials 19 February 9:00am – 2:00pm Register now
22 February 9:00am – 2:00pm Register now
Blockchain
Introduction to Blockchain and AWS Managed Blockchain 15 February 9:30 AM – 11:00 AM Register now
Developer Readiness
Amazon DynamoDB for Serverless Architectures 26 February 10:00 AM – 12:00 PM Register now
Introduction to Containers 16 February 2:30 PM – 4:00 PM Register now
Amazon Elastic Kubernetes Service (EKS) Primer 16 February 10:00 AM – 11:30 AM Register now
Machine Learning & Data Analytics
Big Data Fundamentals on AWS 12 February 10:00 AM – 11:30 AM Register now
Machine Learning Basics 15 February 9:00am – 10:30am Register now
Migrations
Strategies and Tools to Perform Large Scale Migrations

 

26 February 2:00pm – 3:30pm Register now
AWS Certification Readiness
Exam Readiness AWS Certified: Solutions Architect – Associate 15 February 10:00am – 2:00pm Register now
22 February 10:00am – 2:00pm Register now
Exam Readiness AWS Certified: Solutions Architect – Professional 22 February 10:00am – 2:00pm Register now
Exam Readiness AWS Certified: Cloud Practitioner 12 February 10:00am – 11:00am Register now
Exam Readiness AWS Certified: Developer – Associate 19 February 10:00am – 2:00pm Register now
Exam Readiness AWS Certified: Machine Learning – Specialty 26 February 10:00am – 2:00pm Register now

 Session Details:

AWS Cloud Practitioner Essentials (5 hours):
This fundamental-level course is intended for individuals who seek an overall understanding of the AWS Cloud, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS services, security, architecture, pricing, and support. This course also helps you prepare for the AWS Certified Cloud Practitioner exam.

Big Data Fundamentals on AWS (90 mins):
In this session, you will learn about Big Data and basic architecture, value, and potential use cases. The course introduces you to specifics of some key technologies, including Apache Hadoop, Amazon EMR, Apache Hive, and Apache Pig. Although the course focuses on industry-standard Big Data solutions, you will learn about the AWS Big Data ecosystem, a set of services and solutions provided by AWS to build and enhance Big Data solutions.

Amazon DynamoDB for Serverless Architectures (2 hours):
This session provides an in-depth and hands-on introduction to Amazon DynamoDB and how it is leveraged in building a serverless architecture. The course talks about core DynamoDB components and how-to setup and access them in creating a serverless application. You will also learn about several DynamoDB features, best practices and how this NoSQL service is beneficial in comparison to SQL solutions.

Machine Learning Basics (90 mins):
In this course, you get an overview of the concepts, terminology, and processes of the exciting field of machine learning.

Introduction to Blockchain and Amazon Managed Blockchain (90 mins):
This introductory course is designed for technical and nontechnical learners who are unfamiliar with blockchain and interested in how this technology can solve business problems.

Amazon Elastic Kubernetes Service (EKS) Primer (90 mins):
This course teaches you the basics of the Amazon Elastic Kubernetes Service (EKS). You will learn about the implementation of containers on AWS using EKS and complementary services. You will also learn about how communications and management are performed in EKS.

Introduction to Containers (90 mins):
This is an introductory course designed for participants with little-to-no previous knowledge of containers.

Strategies and Tools to Perform Large-Scale Migrations (90 mins):
Learn about AWS’s strategy and best practices for performing large-scale migrations.

 

Notice: Uploading of Information of Different Certifications obtained by 6thand 8th sem students

Notice: Uploading of Information of Different Certifications obtained by 6thand 8th sem students

All students of 6th Semester and 8th Semester who have obtained any certification in areas of Cloud Computing, Bigdata, Visualization, Cyber Security are required to provide the information on an urgent basis using the following link:

https://docs.google.com/forms/d/e/1FAIpQLSfAAvu-_F7IGs59HKbyVPD8t6hpURl53JxMFg3dVyLzK1siog/viewform?vc=0&c=0&w=1&flr=0&usp=mail_form_link