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|