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Found 11 papers, 0 papers with code
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models
We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters.
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
Enabling and Enforcing Social Distancing Measures using Smart City and ITS Infrastructures: A COVID-19 Use Case
Internet of Things is a revolutionary domain that has the caliber to impact our lives and bring significant changes to the world.
Analyzing CNN Based Behavioural Malware Detection Techniques on Cloud IaaS
Cloud Infrastructure as a Service (IaaS) is vulnerable to malware due to its exposure to external adversaries, making it a lucrative attack vector for malicious actors.
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement
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.
Cyber-All-Intel: An AI for Security related Threat Intelligence
It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence.
Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports
We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph.
Preventing Poisoning Attacks on AI based Threat Intelligence Systems
As AI systems become more ubiquitous, securing them becomes an emerging challenge.
Using Deep Neural Networks to Translate Multi-lingual Threat Intelligence
We create a neural network based system that takes in cybersecurity data in a different language and outputs the respective English translation.
Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses.
Using Data Analytics to Detect Anomalous States in Vehicles
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.