We describe a high-level system for granular data-parallel programming called GLU in which parallel applications are described as succinct implicitly parallel intensional compositions using sequential imperative functions. We show how different architecture-independent parallel programs can be generated from the high-level application description. We also show how GLU enables functional debugging of parallel applications without requiring their parallel execution. To illustrate the efficiency of parallel programs generated in GLU, we consider the results of a performance study of three real parallel GLU applications, executed on two different parallel computers. Finally, we compare GLU to other very high-level systems for granular data-parallel programming. © 1997 by John Wiley & Sons, Ltd.

Describes a real-time intrusion-detection expert system (IDES), that observes user behavior on a monitored computer system and adaptively learns what is normal for individual users, groups, remote hosts, and the overall system behavior. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the expert-system rule base.>

The design and implementation of a prototype intrusion-detection expert system (IDES) are described. IDES is based on the concept that an intrusion manifests itself as a departure from expected behavior for a user. The prototype monitors users on a remote system, using audit records that characterize their activities. It adaptively learns the normal behavior of each user and detects and reports anomalous user behavior in real time.>