Linda is an approach to parallelism which relies on a virtual associative shared memory called tuple space. Tuple space is accessed through a small set of primitive operations and is conceptually easy to understand and manipulate. The physical implementation of a Linda tuple space may of course be completely different from the conceptual model. Rhodes has implemented versions of Linda on a ring of RS-232 joined PC's and on a cluster of T800 transputers with a single copy of tuple space on one transputer. Current research targets the implementation of a distributed Linda server on a network of heterogeneous processors. This work describes the design and implementation of a distributed Linda server. Emphasis is placed on aspects of the design which enhance portability and efficiency.

Many expert systems have been developed over the past decades. ProGrammar is a modest expert system shell that has been developed recently. It is built on top of the CProlog/UNIX* system running on a VAX† 11/750. ProGrammar is designed for processing and developing grammars. It can also be used as a knowledge base constructor for other fields besides grammars, a learning tool, a Prolog interpreter, and as a consulting system. ProGrammar is an interactive system meaning not only can the user query ProGrammar but ProGrammar also can question the user. The user is allowed to request an explanation from the ProGrammar on how the solution to the query was derived. / Science, Faculty of / Computer Science, Department of / Graduate

Master of Science

Data represent a key resource for all organizations we may think of. Thus, it is not surprising that data are the main target of a large variety of attacks. Security vulnerabilities and phishing attacks make it possible for malicious software to steal business or privacy sensitive data and to undermine data availability such as in recent ransomware attacks.Apart from external malicious parties, insider attacks also pose serious threats to organizations with sensitive information, e.g., hospitals with patients’ sensitive information. Access control mechanisms are not always able to prevent insiders from misusing or stealing data as they often have data access permissions. Therefore, comprehensive solutions for data protection require combining access control mechanisms and other security techniques,such as encryption, with techniques for detecting anomalies in data accesses. In this the-sis, we develop fine-grained anomaly detection techniques for ensuring in depth protection of data from malicious software, specifically, ransomware, and from malicious insiders.While anomaly detection techniques are very useful, in many cases the data that is used for anomaly detection are very sensitive, e.g., health data being shared with untrusted service providers for anomaly detection. The owners of such data would not share their sensitive data in plain text with an untrusted service provider and this predicament undoubtedly hinders the desire of these individuals/organizations to become more data-driven. In this thesis, we have also built a privacy-preserving framework for real-time anomaly detection.

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Computers connected to a local area network are often only fully utilized for short periods of time. In fact, most workstations are not used at all for a significant portion of the day. The combined "idle time" of the workstations on a network constitutes a significant computing resource, which is generally wasted. If harnessed properly, such a resource could constitute a cheap alternative to expensive high-performance computers. Adaptive parallelism refers to the parallel execution of a computation on a dynamically changing set of processors. This thesis investigates the viability of this approach as a vehicle to harness the "idle cycles" available on a heterogeneous cluster of networked computers. A system, called Remora, which implements adaptive parallelism via the Linda programming paradigm, is presented. Experiments, performed using Remora, show that adaptive parallelism provides an efficient vehicle for using idle processor cycles, without having an adverse effect on the tasks which constitute the normal workload of the computers being used.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / A real time distributed computer telemetry system based on network is described. It is a new generation of open telemetry system in China, which can parallel acquire and process up to 8 data streams of 100bps~3.5Mbps and optimize automatically distribution of processing tasks by using load-balance technique. PCM PSK QPSK PACM may be suitable to the system and the format switched within less than 1 second. The system has been successfully used in the field of aerospace. There are models of automobile, shipboard, airborne as well as ground station for the system. This paper discusses mainly system architecture, performance, principle, and system features.

M.Com. / The proliferation of computers and the advances in technology introduced a number of new and additional management and control considerations. The inherent complexity of these environments has also increased the need to evaluate the adequacy of controls from an audit perspective. Due to the increasing use of database management systems as the backbone of information processing applications and the inherent complexities and diversity of these environments, the auditor is faced with the challenge of whether and to what extent reliance may be placed on the data contained in these databases...

<p>Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into the data, leading to concept drift that causes the anomaly detection to misclassify the actual attack as benign. Our goal is to estimate the vulnerability of an anomaly detection method to an unknown attack, in particular the expected</p> <p>minimum number of poison samples the adversary would need to succeed. Such an estimate is a necessary step in risk analysis: do we expect the anomaly detection to be sufficiently robust to be useful in the face of attacks? We analyze DBSCAN, LOF,</p> <p>one-class SVM as an anomaly detection method, and derive estimates for robustness to poisoning attacks. The analytical estimates are validated against the number of poison samples needed for the actual anomalies in standard anomaly detection test</p> <p>datasets. We then develop defense mechanism, based on the concept drift caused by the poisonous points, to identify that an attack is underway. We show that while it is possible to detect the attacks, it leads to a degradation in the performance of the</p> <p>anomaly detection method. Finally, we investigate whether the generated adversarial samples for one anomaly detection method transfer to another anomaly detection method.</p>