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Found 14 papers, 3 papers with code
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
However, we observe that both model predictions and feature attributions for input samples are sensitive to noise.
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection
Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical.
Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI
The framework characterizes attack patterns by capturing the phases of an attack in Android and enterprise networks and systematically maps them to the MITRE ATT\&CK pattern framework.
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability
Interpretability, trustworthiness, and usability are key considerations in high-stake security applications, especially when utilizing deep learning models.
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
These approaches have resulted in a multitude of attack and defense techniques and the emergence of a field known as `adversarial machine learning.'
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.
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.
Malware Knowledge Graph Generation
Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources.
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.
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.
