• A Cryptographically Secure Power Generators (CSPRNG) is a power generator with properties that makes it suitable for use in cryptography. CSPRNGs are designed explicitly to resist determined mathematical reverse engineering. In this project, we are studying the designs of power generators based on number theory. We concentrate in fast generation of the following generators: Power generators such as BBS generators.

  • The project aims to design, develop and analyse an encryption algorithm.

  • The objective of the project is to design a secure and efficient authenticated encryption algorithm. This algorithm could be implemented on a variety of platforms including GPUs and mobile devices. This implementation will have an impact on the level of security as well as the speed, which however can be suitably tailored. From the perspective of national security, there is a need to give considerable importance for the research, development and deployment of indigenously built secure and efficient authenticated encryption algorithm in major communication systems.

  • Visual cryptography is a perfectly secure way to protect secrets and is characterized by its decryption method. This project is to develop new visual cryptographic scheme satisfying certain conditions. In this project, a secret image is split into a set of shares, so that some authorized shares can have access to the secret, where as other unauthorized shares cannot leak out any secret information.

  • The stability theory of stream ciphers suggests that good key stream sequences must not only have a large linear complexity, but also change of a few terms must not cause a significant drop of the Linear complexity. This unfavorable property leads to the concept of k-error linear complexity. In this project we propose to extend this to the case of multisequences.

  • The project proposes to develop a hardware based network intrusion detection system for high speed networks.

  • COTS Security Incident and Event Management (SIEM) Systems process log events based on built-in rules and identify actionable incidents. These primarily identify known attacks. Using Machine Learning techniques such as Naive Bayes and AdaBoost algorithms, we aim to predict new attacks probabilistically for wired and wireless networks. The Machine Learning-based prediction system in tandem with an SIEM system to predict an attack before it actually occurs. Evaluate the effectiveness of the ML system comparing with the SIEM system in network attack prediction

  • In this project we are attempting to address various VLSI design related issues with a special focus on low area and low power implementation of the finite filed arithmetic. We intend to select a few algorithms with the potential for application in the area. Their realization with FGPA will be studied, analyzed and compared. The focus will be on the identification of algorithms and parameter combinations which will deliver optimum performance.