In an open network-computing environment, a workstation cannot be trusted to identify its users correctly to network services. Authentication protocols provide an approach for the receiver of a message to ascertain its origin and to verify the identity of the sender in a distributed environment. However, most of the protocols have suffered from several kinds of attacks. Therefore, it is necessary to verify authentication protocols deliberately with such attacks as a basis. This work presents a new cryptographic protocol for an open network-computing environment. It describes the weaknesses and limitations in Kerberos protocol and shows how the new protocol overcomes these weaknesses and limitations. We also demonstrate how the new protocol provides an additional service, privacy, beside the authentication service in less number of messages than the previous authentication protocols.

A problem in the sequential detection of weak signals in additive noise is solved under the assumption that the unknown noise density function is a member of some known class of symmetric densities. Two general approaches to the design of receivers which are asymptotically most robust in a minimax sense are established. A comparison between the two methods is provided for the special case when the noise density class is defined byF= \{ f: \int_{-a}^{a} f( x) dx = p, fsymmetric and contiauous at\pma \}. Furthermore the savings in the expected sample size over some nonsequential receivers are calculated.

The problem of detection of known signals in additive noise is solved under the assumption that the unknown noise density is a member of some known family of symmetric densities. A general approach to the design of receivers that are asymptotically most robust is established. As an example, a detector is derived by applying the procedure to the special case obtained when the noise density family is defined byF=\left\{f \left| \int^{a}_{-a} f(x) dx = p, f \mbox{symmetric} \right}.Simulation results showing the detector's performance for small sample sizes are provided.