• Publications
  • Influence
Efficient authentication and signing of multicast streams over lossy channels
TLDR
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. Expand
Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0
TLDR
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. Expand
The TESLA Broadcast Authentication Protocol
TLDR
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. Expand
Why phishing works
TLDR
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. Expand
Efficient and Secure Source Authentication for Multicast
TLDR
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. Expand
SPINS: security protocols for sensor networks
TLDR
A suite of security building blocks optimized for resource-constrained environments and wireless communication, and shows that they are practical even on minimal hardware: the performance of the protocol suite easily matches the data rate of the network. Expand
Can machine learning be secure?
TLDR
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. Expand
The security of machine learning
TLDR
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. Expand
Adversarial Machine Learning
  • J. Tygar
  • Computer Science
  • IEEE Internet Comput.
  • 1 September 2011
The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. HeExpand
SPINS: Security Protocols for Sensor Networks
TLDR
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. Expand
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