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Found 22 papers, 2 papers with code
KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation
The novel framework augments the training of generative models with supplementary context about attribute values and enforces domain constraints during training.
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation
To address these challenges, we propose a Privacy-Driven framework that utilizes a knowledge-infused Generative Adversarial Network for generating synthetic network activity data (KiNETGAN).
Ranked #1 on
Synthetic Data Generation
on UNSW-NB15
PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification
Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules.
Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants.
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction
Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing event information into comprehensive intelligence reports.
Change Management using Generative Modeling on Digital Twins
These digital twins act as a non-production environment where changes can be applied, and the system can be securely tested before patch release.
Knowledge-enhanced Neuro-Symbolic AI for Cybersecurity and Privacy
Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems.
A Practical Entity Linking System for Tables in Scientific Literature
Entity linking is an important step towards constructing knowledge graphs that facilitate advanced question answering over scientific documents, including the retrieval of relevant information included in tables within these documents.
Recognizing and Extracting Cybersecurtity-relevant Entities from Text
Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG).
CAPD: A Context-Aware, Policy-Driven Framework for Secure and Resilient IoBT Operations
This ability to reason over the mission sensed environment and attack context permits the autonomous IoBT system to exhibit resilience in contested conditions.
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.
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion
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 for Nepali Language
Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language.
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.
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.
Preventing Poisoning Attacks on AI based Threat Intelligence Systems
As AI systems become more ubiquitous, securing them becomes an emerging challenge.
UMBC at SemEval-2018 Task 8: Understanding Text about Malware
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
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.
