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Found 19 papers, 2 papers with code
A White-Box Adversarial Attack Against a Digital Twin
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs).
Designing an Artificial Immune System inspired Intrusion Detection System
The Human Immune System (HIS) works to protect a body from infection, illness, and disease.
CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models
As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks.
Creating an Explainable Intrusion Detection System Using Self Organizing Maps
We leverage SOM's explainability to create both global and local explanations.
Explainable artificial intelligence
Explanation Generation
+1
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions.
A Policy Driven AI-Assisted PoW Framework
Proof of Work (PoW) based cyberdefense systems require incoming network requests to expend effort solving an arbitrary mathematical puzzle.
An Intrusion Response System utilizing Deep Q-Networks and System Partitions
Intrusion Response is a relatively new field of research.
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.
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.
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.
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.
