Search Results for author:
Found 13 papers, 4 papers with code
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism? A Path through the Accuracy-Privacy Ceiling Constraining Differentially Private ML
The state of the art and de facto standard for differentially private machine learning (ML) is differentially private stochastic gradient descent (DPSGD).
Detecting CAN Masquerade Attacks with Signal Clustering Similarity
Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle's CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks.
Time-Based CAN Intrusion Detection Benchmark
Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs).
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset
Current public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, which lack fidelity.
Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection
In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files?
Active Manifolds: A non-linear analogue to Active Subspaces
Overall, AM represents a novel technique for analyzing functional models with benefits including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more accurate regression than AS (at more computational expense), enabling more informative sensitivity analysis, and granting accessible visualizations(2-D plots) of parameter sensitivity along the AM.
Forming IDEAS Interactive Data Exploration & Analysis System
Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition.
Dimension Reduction Using Active Manifolds
This project was inspired by an approach known as Active Subspaces, which works by linearly projecting to a linear subspace where the function changes most on average.
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection
This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints.
Towards a relation extraction framework for cyber-security concepts
In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed.
Multi-Level Anomaly Detection on Time-Varying Graph Data
This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data.
Automatic Labeling for Entity Extraction in Cyber Security
Timely analysis of cyber-security information necessitates automated information extraction from unstructured text.
PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts
Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources months before proper classification into structured databases.
