Computer Science & Engineering ->Center of Excellence

Network Forensic

Network forensics is a subfield of digital forensics where evidence is captured from networks and interpretation is substantially based on knowledge of cyber attacks. It aims to locate the attackers and reconstruct their attack actions through analysis of intrusion evidence. Major technical challenges facing network forensics analysis: Current sources of intrusion evidence such as IDS alerts are not well adapted for forensics investigation. Cyber attacks are increasingly sophisticated. Short response times. Major objectives of network forensics analysis can be summarized into two fundamental problems: attack group identification and attack scenario reconstruction. Attack scenario reconstruction is the process of inferring step-wise actions taken by the attacker to achieve his malicious objective. Attack group identification is the task of discovering the group of hosts involved in the attack and determining the roles of each host in the group.

The main objectives of our proposed project are

  • Detection of TCP SYN flooding
  • Detection of UDP flooding
  • Estimation of Throughput of the network
  • Filtering of Packets
  • Detection of Routing Protocol
  • Finding Round Trip Time
  • Getting other application protocol details for analysis like DNS-protocol This project concentrates on taking care of Deformed Packets with appropriate flags, Aggregation and reassembly of packets. It is not dependent on NIC and it is a Hardware engine. Thus there Tuning intrusion prevention and detection solutions

    Data Science

    Data Science is a subject of Computer Science aimed at building machines and computers that can enhance logical operations. AI systems have the ability to execute tasks naturally associated with human intelligence, like speech recognition, decision-making, visual perception, and translating languages.

    Machine Learning is one of the widely used algorithms of AI. The learning process involves the enhancement of new declarative knowledge, the advancement of cognitive and motor skills through practice or instruction. Since the beginning of the computer era, researchers and scientists have been trying to implant such abilities in computers. Solving this issue has been, and remains, most fascinating and challenging long-term goal in AI.

    Some of the applications of Data Science are as follows:

    Natural Language Processing

    NLP is a method in which computers are made to understand, execute and manipulate human language. To reach this goal, a computer should be able to “understand” a large amount of data – from grammar syntax and rules to various accents and colloquialisms. Whereas a speech recognition system, for example, manual speech becomes audio data, which then turns into text data, a complex process itself. This text data can be implemented in an “intelligent” system for different applications such as controlling devices or translators.

    Computer Vision

    Computer vision is the science of manipulating or understanding videos and images. It has many applications, comprising of augmented reality, autonomous driving and industrial inspection. The implementation of deep learning for computer vision can be differentiated into many categories: detection, generation, segmentation and classification, both in videos and images.

    Financial Services

    Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. The technology is also used to identify opportunities for investments and trade. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud.

    Marketing and Sales

    Companies are using machine learning technology to analyze the purchase history of their customers and make personalized product recommendations for their next purchase. This ability to capture, analyze, and use customer data to provide a personalized shopping experience is the future of sales and marketing.


    Government agencies like utilities and public safety have a specific need FOR Ml, as they have multiple data sources, which can be mined for identifying useful patterns and insights. For example sensor data can be analyzed to identify ways to minimize costs and increase efficiency. Furthermore, ML can also be used to minimize identity thefts and detect fraud.


    With the advent of wearable sensors and devices that use data to access health of a patient in real time, ML is becoming a fast-growing trend in healthcare. Sensors in wearable provide real-time patient information, such as overall health condition, heartbeat, blood pressure and other vital parameters. Doctors and medical experts can use this information to analyze the health condition of an individual, draw a pattern from the patient history, and predict the occurrence of any ailments in the future. The technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment.


    Based on the travel history and pattern of traveling across various routes, machine learning can help transportation companies predict potential problems that could arise on certain routes, and accordingly advise their customers to opt for a different route. Transportation firms and delivery organizations are increasingly using machine learning technology to carry out data analysis and data modeling to make informed decisions and help their customers make smart decisions when they travel.

    Oil and Gas

    This is perhaps the industry that needs the application of machine learning the most. Right from analyzing underground minerals and finding new energy sources to streaming oil distribution, ML applications for this industry are vast and are still expanding.

    Proposed Data Science will be set up at the institute level and it can be used by students or faculty members of any engineering department. AI and ML have varied applications across different engineering streams.

    Internet of Things

    Center of Excellence on Internet of Things (IoT) is established for carrying out projects and research work by Undergraduate/Post Graduate students and Research students/faculties at Department of Computer Science and Engineering, PES College of Engineering, Mandya. The above said laboratory has the overall goal of identifying and providing solutions for the real life problems and also research problems. As an outcome of the established laboratory, a number of projects on Internet of Things were developed by a number of talented UG/PG students. Some team members are working in collaboration with different start- up companies/ practitioners to address the real life problems and to give solutions for the societal issues.

    Various components used for developing a number of projects on different types of problems are given below. 1. Gold pack of Zolertia Device ( 10 no’s ) 2. Zolertia gateway 3. Arduino UNO R3 board with DIP ATmega328P 4. Arduino Yun 5. Arduino Ultrasonic Range Finder Module Sensor Distance Measuring Transducer 6. Microphone sound sensor module 7. MQ-7 Carbon Monoxide Detection Sensor 8. Ethernet W5100 Shield Network Expansion Board w/ Micro SD Card Slot 9. Electronic Components {Bread boards, Jumper, cables, LED's, Resistors, Capacitors, Transistors} 10. Finger-clip Heart Rate Sensor with shell 11. Serial Camera Kit 12. PIR Motion Sensor - Large Lens version 13. GSR sensor 14. GPS Module 15. EMG Detector 16. Wind Speed/ Direction Sensor 17. Fingerprint Sensor 18. Water Flow Sensor 19. Adult reusable NIBP cuff 20. Raspberry Pi 2 Model B 1GB 21. Soil moisture sensor 22. Water level switch 23. Flame Sensor

    Methodology 1. Internet of Things (IoT) Laboratory has been established with the required facilities for implementing the projects on various problems chosen from the societal issues. 2. Interaction with various experts from different start-up companies/ practitioners to address the real life problems. 3. Best possible solutions are developed by using the equipments in the Internet of Things (IoT) Laboratory. 4. Challenges in developing solutions for some more realistic problems are the focus of forthcoming days. Outcome: A number of projects on Internet of Things are developed by a number of talented UG and PG students.