Towards security monitoring patterns

Runtime monitoring is performed during system execution to detect whether the system’s behaviour deviates from that described by requirements. To support this activity we have developed a monitoring framework that expresses the requirements to be monitored in event calculus – a formal temporal first order language. Following an investigation of how this framework could be used to monitor security requirements, in this paper we propose patterns for expressing three basic types of such requirements, namely confidentiality, integrity and availability. These patterns aim to ease the task of specifying confidentiality, integrity and availability requirements in monitorable forms by non-expert users. The paper illustrates the use of these patterns using examples of an industrial case study

Deep Learning for User Comment Moderation

Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation