Physical Sciences and Mathematics Commons^™

Active Snort Rules And The Needs For Computing Resources: Computing Resources Needed To Activate Different Numbers Of Snort Rules, Chad A. Arney, Xinli Wang

School of Technology Publications

This project was designed to discover the relationship between the number of enabled rules maintained by Snort and the amount of computing resources necessary to operate this intrusion detection system (IDS) as a sensor. A physical environment was set up to loosely simulate a network and an IDS sensor monitoring it.

The experiment was conducted in five trials. A different number of Snort rules was enabled in each trial and the corresponding utilization of computing resources was measured. Remarkable variation and a clear trend of CPU usage were observed in the experiment.

The Effects Of Multiple Query Evidences On Social Image Retrieval, Zhiyong Cheng, Jialie Shen, Haiyan Miao

Research Collection School Of Computing and Information Systems

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo …

Negative Factor: Improving Regular-Expression Matching In Strings, Xiaochun Yang, Tao Qiu, Bin Wang, Baihua Zheng, Yaoshu Wang, Chen Li

Research Collection School Of Computing and Information Systems

The problem of finding matches of a regular expression (RE) on a string exists in many applications such as text editing, biosequence search, and shell commands. Existing techniques first identify candidates using substrings in the RE, then verify each of them using an automaton. These techniques become inefficient when there are many candidate occurrences that need to be verified. In this paper we propose a novel technique that prunes false negatives by utilizing negative factors, which are substrings that cannot appear in an answer. A main advantage of the technique is that it can be integrated with many existing algorithms …