We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters.
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system.
Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language.
It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence.
A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried.
We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph.
We create a neural network based system that takes in cybersecurity data in a different language and outputs the respective English translation.
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses.
Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them.