In the aviation safety research domain, cause identification refers to the task of identifying the possible causes responsible for the incident described in an aviation safety incident report. This task presents a number of challenges, including the scarcity of labeled data and the difficulties in finding the relevant portions of the text. We investigate the use of annotator rationales to overcome these challenges, proposing several new ways of utilizing rationales and showing that through judicious use of the rationales, it is possible to achieve significant improvement over a unigram SVM baseline.

The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents. To effectively reduce these incidents, it is vital to accurately identify why these incidents occurred. More precisely, given a set of possible causes, or shaping factors, this task of cause identification involves identifying all and only those shaping factors that are responsible for the incidents described in a report. We investigate two approaches to cause identification. Both approaches exploit information provided by a semantic lexicon, which is automatically constructed via Thelen and Riloff's Basilisk framework augmented with our linguistic and algorithmic modifications. The first approach labels a report using a simple heuristic, which looks for the words and phrases acquired during the semantic lexicon learning process in the report. The second approach recasts cause identification as a text classification problem, employing supervised and transductive text classification algorithms to learn models from incident reports labeled with shaping factors and using the models to label unseen reports. Our experiments show that both the heuristic-based approach and the learning-based approach (when given sufficient training data) outperform the baseline system significantly.

Security of a network depends on a number of dynamically changing factors. These include emergence of new vulnerabilities and threats, policy structure and network traffic. Due to the dynamic nature of these factors, identifying security metrics that measure objectively the quality of security configuration pose a major challenge. Moreover, this evaluation must be done dynamically to handle real time changes in the threat toward the network. In this paper, we extend our security metric framework [2] that identifies and quantifies objectively the most significant security risk factors, which include existing vulnerabilities, historical trend of vulnerabilities of remotely accessible services, prediction of potential vulnerabilities for any general network service and their estimated severity and finally propagation of an attack within the network. We have implemented this framework as a user-friendly tool called Risk based prOactive seCurity cOnfiguration maNAger (ROCONA) and showed how this tool simplifies security configuration management using risk measurement and mitigation.