Formal Methods Group

Formal Analysis and Semantics of Programming Language (FASP)   GROUP WELCOMES YOU!

The goal of FASP is to develop mathematical techniques and methods and high-performance  algorithms using appropriate programming languages for the design of trustworthy software- and data-intensive control systems.
Process algebras are semantically clear, mathematically rigorous languages that allow specifying and confirming the characteristics of concurrent communicative systems. They can be thought of as agents that continuously act and interact with one another and their shared environment, acting as models of processes. The agents could be artifacts, potentially represented in computer hardware or software systems, or they could be actual objects in the real world (including people). For describing the meaning of processes, numerous alternative techniques (operational, denotational, and algebraic) are used.

FASP Group’s research covers a wide range of topics, including those related to formal methods and verification of large systems, the semantics of programming languages, process algebra and process calculi, semantic models and logics, verification techniques based on types and type systems, formulation of model checking algorithms, techniques for efficient and scalable analysis, and formal verification in machine learning. A fundamental aspect of our research is the application of formal methods to the modeling, verification, and synthesis of complex software systems, including autonomous systems such as UAVs, forgery detection techniques [Video/Passive video], recommendation systems [movies], predicting smart building occupancy, APK detection, formal verification in machine learning, forgery detection in videos [parallel CNN], and safety-critical systems.

ACTIVITIES

• Conference -"4th Doctoral Symposium on Computational Intelligence (DOSCI-2023)" on March 3 [Online Mode]

• Workshops on Formal method Verification in different fields

• Upcoming :Course work on Formal Method PCS-01 by Prof. Manish Gaur.

PROJECTS

• Current: Synthetic Data Generation (SynD) using Machine Learning Algorithm in Cyber Physical Systems; Department of Science and Technology, Government of India through IHUB,  C3iHub FOUNDATION, IIT Kanpur.

 In House Funding Amount: INR 21,36,980.00; PI-Dr Parul Yadav; CO-PI- Prof. Manish Gaur.

• Completed: Google Code Lab in which we trained the  faculty/students in the areas of Machine Learning, Data Analytics, App Developments, Cyber and Data Security.  Funding Amount:  USD 100,000. PI- Prof. Manish Gaur 

• Completed : Setting up the Technology Business Incubation and Innovation Cell (TBIU).

            Total funding amount: INR 2,500,000 - Prof.Manish Gaur

• Completed: To develop software for computer based typing tests:Contract; Uttar Pradesh Subordinate Service Commission, Govt of Uttar Pradesh (LUCKNOW, UTTAR PRADESH, IN). Total funding amount:INR380,000  - Prof. Manish Gaur

• Completed: Analysis and Prediction of Influent and Effluent Quality Parameters for a UASB-Based Wastewater Treatment Plant in Asia Funding Source: In house Funding Amount: INR 2,80,000.00 Status: Completed (2020-23)- Dr Parul Yadav

• Completed: Project Title: Security and Surveillance Robot Funding Source: TEQIP-3. Funding Amount: INR 81860.40 Status: Completed (2019-2020)- Dr. Parul Yadav 

PUBLICATION

• Manish Gaur and Govind Kumar Jha and Preetish Ranjan and Hardeo K Thakur, A survey on trustworthy model of recommender system, International Journal of System Assurance Engineering and Management, Springer, Springer Journals Publication , , , 2021. DOI : https://doi.org/10.1007/s13198-021-01085-z 2020 

• Manish Gaur and Annie Irfan and S P Tripathi, A Study of Process Calculus for Formal Verification and Analysis of Security Protocol, 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), IEEE, , , 2020. DOI : 10.1109/iCATccT44854.2018.9001950 

• Manish Gaur and Nitesh Kumar and Vinay Kumar, Banking trojans APK detection using formal methods, 2019 4th International Conference on Information Systems and Computer Networks ISCON, IEEE , , , 2020. DOI : 10.1109/ISCON47742.2019.9036319 

• Manish Gaur, Vinay Kumar, Abhishek Singh, Vineet Kansal , A Comprehensive Survey on Passive Video Forgery Detection Techniques, Recent Studies on Computational Intelligence part of Studies in Computational Intelligence, Springer, , pp. vol 921 (39-57), 2020. DOI : https://doi.org/10.1007/978-981-15-8469-5_4

• Parul Yadav, Manish Gaur, A Process Calculi for Intrusion Detection System in Mobile Ad-hoc Networks, Journal of Communications, vol. 13, no.11, pp. 635-647, 2018 DOI: 10.12720/jcm.13.11.635-647 

• Parul Yadav, Manish Gaur, A Behavioural Theory for Intrusion Detection System in Mobile Ad-hoc Networks, Proc. of the International Conference on High Performance Compilation, Computing and Communications (HP3C-2018), Hong Kong, China, pp. 51-60, 15th-17th March, 2018 

• Parul Yadav, Manish Gaur, A Survey on Formal Modelling for Secure Routing in Mobile Ad-hoc Networks, Proc.of the International Conference on Distributed Computing and Internet Technology (ICDCIT-2015), Bhubaneshwar, Odisha, India, pp. 18-23, 5th8th February, 2015 

• Nehan Mumtaz, Parul Yadav, Manish Gaur, Distance Based Angular Multicast Routing Protocol for Mobile Ad-hoc Networks (DAMRP), Proc.of the 5th IEEE International conference on Communication Systems and Network Technologies (CSNT 2015), Gwalior, India, pp. 253 – 257, 4th-6th April 2015 DOI: 10.1109/CSNT.2015.231.

• Parul Yadav, Brijesh Singh Yadav, Joydeep Chandra, Statistical Analysis Based Efficient Decentralized Intrusion Detection Scheme for Mobile Ad-hoc Networks, Proc.of the 16th IEEE International Conference on Networks (ICON 2008), New Delhi, India, 12th-14th December, pp. 1-6, 2008 DOI: 10.1109/ICON.2008.4772601.

• Kumar, V., Gaur, M. and Kansal, V., 2022. Deep feature based forgery detection in video using parallel convolutional neural network: VFID-Net. Multimedia Tools and Applications, 81(29), pp.42223-42240.

• Kumar, V. and Gaur, M., 2022. Multiple forgery detection in video using inter-frame correlation distance with dual-threshold. Multimedia Tools and Applications, pp.1-20.

• Kumar, V., Singh, A., Kansal, V. and Gaur, M., 2021. A comprehensive survey on passive video forgery detection techniques. In Recent Studies on Computational Intelligence: Doctoral Symposium on Computational Intelligence (DoSCI 2020) (pp. 39-57). Springer Singapore.

Group Head

Prof. Manish Gaur

MEMBERS

Dr. Manik Chandra
Dr. Parul Yadav
Dr. Ramakant Baghel
Dr. Vinay Kumar
Dr. Govind Jha
Er. Priyanka Gupta [Phd Thesis Submitted]
Er. Abhishek Singh [Co-ordinator]
Er.Anil Kumar Singh [Phd Scholar] [Co-ordinator]
Er. Nishat Fatima [Phd Scholar] [Co-ordinator]
Er. Annie Irfan

CONTACT US

Institute of Engineering & Technology,

Sitapur Road,Lucknow

Uttar Pradesh

India

Pin Code       :   226021

Web: https://www.ietlucknow.ac.in