Search Results for author:
Found 12 papers, 4 papers with code
Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI
In this paper, we present LADDER, a knowledge extraction framework that can extract text-based attack patterns from CTI reports at scale.
SoK: Modeling Explainability in Security Monitoring for Trust, Privacy, and Interpretability
Security operations centers have a number of security monitoring tools that analyze logs and generate threat alerts which security analysts inspect.
CyNER: A Python Library for Cybersecurity Named Entity Recognition
Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet.
Adversarial Patterns: Building Robust Android Malware Classifiers
We first present an extensive background in building a machine learning classifier for android malware, covering both image-based and text-based feature extraction approaches.
Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles
We present a model that explains \textit{certainty} and \textit{uncertainty} in sensor input -- a missing characteristic in data collection.
Ontology-driven Knowledge Graph for Android Malware
This ontology forms the basis for the malware threat intelligence knowledge graph, MalKG, which we exemplify using three different, non-overlapping demonstrations.
TINKER: A framework for Open source Cyberthreat Intelligence
The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts.
Malware Knowledge Graph Generation
Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources.
DANTE: Predicting Insider Threat using LSTM on system logs
For this, system logs are modeled as a natural language sequence and patterns are extracted from these sequences.
MALOnt: An Ontology for Malware Threat Intelligence
The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports.
Personal Health Knowledge Graphs for Patients
Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients.
Exploring Information Centrality for Intrusion Detection in Large Networks
Information Centrality (IC) labels network nodes with better vantage points for detecting network-based anomalies as central nodes and uses them for detecting a category of attacks called systemic attacks.
