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Cyber-Physical Systems Virtual Organization
Read-only archive of site from September 29, 2023.
CPS-VO
machine learning models
biblio
Improved Adversarial Attack against Black-box Machine Learning Models
Submitted by aekwall on Tue, 07/27/2021 - 1:58pm
composability
Resiliency
Metrics
pubcrawl
optimization
Perturbation methods
security
PSO
particle swarm optimization
machine learning models
boundary attack
Biological neural networks
adversarial samples
Black Box Attacks
convergence
search problems
biblio
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems
Submitted by grigby1 on Fri, 12/11/2020 - 2:33pm
neural nets
machine learning
machine learning models
malware
Malware Analysis
malware containment
malware detection
Malware-Detection-Cloud-Computing-Graph-Neural-Networks-Federated-Learning-Multicloud-Natural-Language-Processing
Metrics
learning (artificial intelligence)
privacy
Privacy Requirements
pubcrawl
resilience
Resiliency
Task Analysis
Training
virtual machines
feature representation
Biological system modeling
Bit error rate
Cloud Computing
cloud computing environments
cloud-specific optimization problems
data privacy
deep federated defenses
feature extraction
attentional sequence models
federated learning
graph neural networks
graph theory
Human behavior
hypergraph learning models
inductive graph
invasive software
biblio
Network Intrusion Detection in Smart Grids for Imbalanced Attack Types Using Machine Learning Models
Submitted by grigby1 on Fri, 11/20/2020 - 2:05pm
imbalanced attack types
network intrusion detection
advanced metering infrastructure
imbalanced data
privacy concerns
machine learning models
smart meter
automatic meter reading
boosting
critical security service
generation power grid paradigm
Smart Grids
legacy information
low detection rates
ongoing attacks
smart grid systems
smart information
system operator
time information
time-of-use pricing
smart grid consumer privacy
policy-based governance
learning (artificial intelligence)
security of data
Training
machine learning
machine learning algorithms
Intrusion Detection
pubcrawl
Human behavior
Metrics
privacy
collaboration
resilience
Resiliency
security
Scalability
power engineering computing
Smart Grid
smart power grids
composability
biblio
A Novel Machine Learning Based Malware Detection and Classification Framework
Submitted by aekwall on Mon, 10/26/2020 - 12:13pm
complex malware types
Predictive Metrics
computer systems
Feature Selection
dynamic analysis
feature selection algorithms
machine learning models
classification accuracy
accurate malware detection
analysis report
classification framework
malware samples
fine-grained classification
high detection
malware analysis framework
malware files
minimum computation cost
selection module
signature-based malware detection techniques
system activities
time progresses
Training
invasive software
machine learning algorithms
feature extraction
learning (artificial intelligence)
Resiliency
Human behavior
pubcrawl
Metrics
pattern classification
malware
machine learning
testing
privacy
Virtual machining
Malware Analysis
cuckoo sandbox
malware classification
malware detection
static and dynamic analysis
biblio
Insights into Malware Detection via Behavioral Frequency Analysis Using Machine Learning
Submitted by aekwall on Mon, 10/26/2020 - 12:11pm
known malware
malicious software
malicious activity
Microsoft Windows (operating systems)
machine learning models
behavioral frequency analysis
benign software
common defenses
common malware defenses
dynamic behavioral analysis
Predictive Metrics
Malware Behavioral Analysis
malware signature classifier
malware threat landscape
signature catalog
unknown malware
Windows API system function calls
zero-day
zero-day vulnerabilities
Metrics
malware detection
invasive software
machine learning algorithms
learning (artificial intelligence)
Resiliency
Human behavior
pubcrawl
machine learning
malware
pattern classification
Analytical models
privacy
microsoft windows
Malware Analysis
application program interfaces
dynamic analysis
Software algorithms
biblio
Certified Robustness to Adversarial Examples with Differential Privacy
Submitted by aekwall on Mon, 04/20/2020 - 10:31am
machine learning models
privacy models and measurement
Sophisticated Attacks
PixelDP
norm-bounded attacks
machine-learning
ImageNet
Google Inception network
defense
Deep-learning
cryptographically-inspired privacy formalism
certified robustness
certified defense
Adversarial-Examples
adversarial examples
Cryptography
deep neural networks
differential privacy
Predictive models
Measurement
Mathematical model
Databases
neural nets
standards
Robustness
Metrics
pubcrawl
learning (artificial intelligence)
security
data privacy
biblio
Measuring data privacy preserving and machine learning
Submitted by aekwall on Mon, 04/20/2020 - 10:23am
Organizations
Software
sensitive data
pubcrawl
Privacy-preserving
privacy violations
privacy preservation metric
Privacy Preferences
privacy models and measurement
privacy measurement
privacy levels
privacy
Computational modeling
Metrics
Measurement
machine learning techniques
machine learning models
machine learning
learning (artificial intelligence)
data publishing
data publisher
data privacy-preserving
data privacy
data owners
biblio
The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning
Submitted by aekwall on Mon, 03/30/2020 - 11:33am
end-to-end explainability
basic transformations
consistent data
data analysis pipeline
data pre-processing steps
data preparation
distributed file system
distributed machine learning
distributed setting
machine learning models
entire data set
explainable machine learning
explainable models
homogeneous data
linking provenance
machine learning experts
single data
Distributed databases
learning (artificial intelligence)
Resiliency
Human behavior
pubcrawl
composability
Computational modeling
Metrics
machine learning
Data models
data analysis
Entropy
Provenance
distributed system
Decision trees
data provenance
distributed computing
biblio
Don't Forget Your Roots! Using Provenance Data for Transparent and Explainable Development of Machine Learning Models
Submitted by aekwall on Mon, 03/30/2020 - 11:33am
explainable development
xai
transparent development
transparency
reasoning behaviour
provenance data
process transparency
post-hoc methods
machine learning models
Explainable ML
learning (artificial intelligence)
development process
artificial intelligent systems
Provenance
Metrics
composability
pubcrawl
Human behavior
Resiliency
biblio
Self-Secured Control with Anomaly Detection and Recovery in Automotive Cyber-Physical Systems
Submitted by grigby1 on Mon, 03/02/2020 - 1:35pm
learning (artificial intelligence)
System recovery
sensors
self-secured control
security of data
security
Resiliency
resilience
recovery estimation error
pubcrawl
neural nets
Mathematical model
machine learning models
machine learning
Anomaly Detection
generative adversarial networks
electric vehicle
cyber-physical systems
CPS
control loop behavior
control engineering computing
computer architecture
battery management system
battery
batteries
Automotive engineering
Automotive cyber-physical systems
automotive controllers
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