The Colorado Research Institute for Security and Privacy (CRISP) is an active research group in information security at the University of Denver.
CRISP is dedicated to creating security software that is directly useful for a broad audience, going beyond the usual construction of academic proof-of-concept prototypes.

The director of the institute is Dr. Thurimella, who focuses on algorithmic problems and mathematical analysis of security problems.

The current research interests of the group are:

Secure routing in P2P networks

Secure routing in peer-to-peer networks is challenging primarily because adversaries can easily participate in critical positions in the network. Assuming that the network is fully decentralized and thus no participant can be trusted, detecting and pinpointing malicious behavior is hard. We are interested in scalable protocols that preserve user privacy and provide performance guarantees (such as data integrity, limited bandwidth consumption and latency).

Consistency models for P2P systems

This research involves the creation of new consistency models and protocols that can be used efficiently given the constraints of a best-effort network and malicious peers.
As part of our work, we are developing secure and cheat-proof protocols for peer-to-peer games. Games are particularly challenging because players have an incentive to cheat while protocols must meet the real-time message passing requirements dictated by the type of game itself. We are currently using traffic traces and simulations to validate our protocols.

Reputation systems for content discovery

Building a reputation system for a decentralized network without a trusted third party is a challenging problem. A trusted third party adds additional cost and security risk.
Without one, collusion is a problem because reputation systems are based on feedback from peers, e.g. malicious peers can collude to give each other good ratings while giving negative ratings to other peers. This project deals with building a practical reputation system under some reasonable assumptions, e.g. the malicious nodes are in a minority.

Detecting Intrusion in Large Intrusion Detection Environments

Managing the high volume of alarms generated by large intrusion detection environments can be very challenging. A major problem faced by those who deploy current intrusion detection technology is the large number of false alarms generated by Intrusion Detection Sensors (IDSs), which can be well over 90 percent. In this project, techniques from data mining and web search (such as PageRank) are applied to separate the signal from noise.

At the University of Denver, professors associated with CRISP teach courses in Computer Security (COMP 3704) System Security (COMP 4704), Networking (COMP 3621) and Systems Programming (COMP 2400).

We believe in full disclosure and are happy to educate students in all aspects of security, including attacking the security of systems.

CRISP is always looking for enterprising students interested in computer security who enjoy systems programming. If you are not already a student at DU, please follow the Computer Science application process and mention your interest in computer security in the application.

This project is funded in part by the National Science Foundation under Grant No. DUE--0416969. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation.