Efficient authentication and signing of multicast streams over lossy channels
- A. Perrig, R. Canetti, J. Tygar, D. Song
- Computer ScienceProceeding IEEE Symposium on Security and…
- 14 May 2000
This work proposes two efficient schemes, TESLA and EMSS, for secure lossy multicast streams, and offers sender authentication, strong loss robustness, high scalability and minimal overhead at the cost of loose initial time synchronization and slightly delayed authentication.
Why phishing works
- Rachna Dhamija, J. Tygar, Marti A. Hearst
- Computer ScienceInternational Conference on Human Factors in…
- 22 April 2006
This paper provides the first empirical evidence about which malicious strategies are successful at deceiving general users by analyzing a large set of captured phishing attacks and developing a set of hypotheses about why these strategies might work.
The TESLA Broadcast Authentication Protocol
- A. Perrig, R. Canetti, J. Tygar, D. Song
- Computer Science
- 2002
The TESLA (Timed Efficient Stream Loss-tolerant Authentication) broadcast authentication protocol is presented, an efficient protocol with low communication and computation overhead, which scales to large numbers of receivers, and tolerates packet loss.
Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0
- A. Whitten, J. Tygar
- Computer ScienceUSENIX Security Symposium
- 23 August 1999
It is concluded that PGP 5.0 is not usable enough to provide effective security for most computer users, despite its attractive graphical user interface, supporting the hypothesis that user interface design for effective security remains an open problem.
SPINS: Security Protocols for Sensor Networks
- A. Perrig, R. Szewczyk, J. Tygar, Victor Wen, D. Culler
- Computer ScienceACM/IEEE International Conference on Mobile…
- 16 July 2001
A suite of security protocols optimized for sensor networks: SPINS, which includes SNEP and μTESLA and shows that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of the network.
Adversarial machine learning
- J. Tygar
- Computer ScienceSecurity and Artificial Intelligence
- 1 September 2011
A taxonomy for classifying attacks against online machine learning algorithms and the limits of an adversary's knowledge about the algorithm, feature space, training, and input data are given.
Efficient and Secure Source Authentication for Multicast
- A. Perrig, R. Canetti, D. Song, J. Tygar
- Computer ScienceNetwork and Distributed System Security Symposium
- 2001
This paper proposes several substantial modifications and improvements to TESLA, which allows receivers to authenticate most packets as soon as they arrive, and improves the scalability of the scheme, reduce the space overhead for multiple instances, increase its resistance to denial-of-service attacks, and more.
Can machine learning be secure?
- M. Barreno, Blaine Nelson, R. Sears, A. Joseph, J. Tygar
- Computer ScienceACM Asia Conference on Computer and…
- 21 March 2006
A taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, and an analytical model giving a lower bound on attacker's work function are provided.
The security of machine learning
- M. Barreno, Blaine Nelson, A. Joseph, J. Tygar
- Computer ScienceMachine-mediated learning
- 1 November 2010
A taxonomy identifying and analyzing attacks against machine learning systems is presented, showing how these classes influence the costs for the attacker and defender, and a formal structure defining their interaction is given.
ELK, a new protocol for efficient large-group key distribution
ELK, a novel key distribution protocol, is designed and implemented to address security challenges of secure media broadcast over the Internet with perfectly reliable, super-efficient member joins and smaller key update messages than previous protocols.
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