Biblio

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

Legal metrology embraces the regulation and control of measuring instruments (MI) used in a diversity of applications including industry, transportation, commerce, medical care and environment protection [3]. Only in Europe, MI are responsible for an annual turnover of more than 500 billion Euros [1]. In developing countries, MI demand has increased substantially due to the adoption of technologies and methods well established in developed countries [3]. MI also can be seen as elementary build blocks for new technologies such as smart grids, Internet of Things and cyber physical systems [1, 2]. Thus legal metrology is crucial to assure the correctness of measurements, protecting the economic system while regulating consumer relations and enhances MI reliability [2].