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Cyber-Physical Systems Virtual Organization
Read-only archive of site from September 29, 2023.
CPS-VO
malware
biblio
A dark web story in-depth research and study conducted on the dark web based on forensic computing and security in Malaysia
Submitted by grigby1 on Wed, 09/12/2018 - 10:15am
URL
website links
skynet
Malaysia
forensic computing
dark web story
dark web security
cryptocurrency
Government
Instruments
pubcrawl
Digital Forensics
dark web
Web sites
internet
drugs
Conferences
malware
Human Factors
Human behavior
biblio
Quantifying the impact of adversarial evasion attacks on machine learning based android malware classifiers
Submitted by Katie Dey on Mon, 08/06/2018 - 12:24pm
malicious apps
user devices
Training data
Training
Support vector machines
statistical analysis
smart phones
regression analysis
Random Forest
nonlinear classifiers
neural network
neural nets
malware
adversarial evasion attacks
machine learning
logistic regression
linear classifiers
learning (artificial intelligence)
invasive software
Humanoid robots
feature extraction
Androids
Android-based devices
Android Malware Detection
android malware classifiers
Extraction
biblio
Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense
Submitted by Katie Dey on Mon, 08/06/2018 - 12:23pm
feature manipulations
Windows application programming interface calls
Windows API calls
security regularization term
secure-learning paradigm
SecDefender
portable executable files
PE files
pattern classification
malware detection
malware
learning-based classifier
learning (artificial intelligence)
invasive software
Adversarial Machine Learning
feature extraction
Extraction
EvnAttack
evasion cost
evasion attack model
Evasion Attack and Defense
Data models
Data mining
computer security
Comodo cloud security center
classifier retraining technique
application program interfaces
Cognitive Security
Articles of Interest
Extraction
C3E 2019
biblio
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare
Submitted by grigby1 on Fri, 07/06/2018 - 1:06pm
Resiliency
malware
medical computing
Medical Informatics
models
Neoplasms
patient distress
pattern classification
poisoning attacks
pubcrawl
resilience
malicious data
Scalability
security
security breach
security of data
systematic poisoning attacks
targeted errors
Theoretical
Training
training datasets
health care
Algorithms
application domains
arbitrary errors
biomedicine
computer security
critical decisions
Databases
Factual
false diagnosis prompt users
false positive classification
AI Poisoning
health-related applications
Healthcare
Human behavior
Humans
learning (artificial intelligence)
life-threatening consequences
machine learning
machine learning algorithms
machine-learning algorithms
biblio
The dose makes the poison \#x2014; Leveraging uncertainty for effective malware detection
Submitted by grigby1 on Fri, 07/06/2018 - 1:05pm
Resiliency
Organizations
OS processes
Plan B
potential malware
promising avenue
pubcrawl
received borderline classifications
resilience
malware
Scalability
sensitive information
software execution
Standards organizations
time-consuming
traditional ML detectors
traditional ML methods
uncertain environment
devastating consequences
AI Poisoning
analyzed benign software
behavioral-based malware detectors
Chameleon
CPU-bound software
deep analysis
deep learning models
Detectors
100 malware samples
effective malware detection
Human behavior
I/O-bound software
Interference
invasive software
learning (artificial intelligence)
Linux
biblio
A Lightweight Malware Classification Method Based on Detection Results of Anti-Virus Software
Submitted by grigby1 on Wed, 06/20/2018 - 11:57am
Malware Analysis
static analysis
Resiliency
resilience
pubcrawl
program diagnostics
privacy
Phased arrays
pattern classification
Metrics
Malware Clustering
malware classification
antivirus software detection
malware
lightweight malware classification
internet
Human behavior
Electronic mail
dynamic analysis
Databases
Data mining
cyber threats
computer viruses
arrays
biblio
Hierarchical learning for automated malware classification
Submitted by grigby1 on Wed, 06/20/2018 - 11:56am
pubcrawl
machine learning solutions
malicious file detection
malicious files automated classification
malware
malware classification
malware label
Metrics
nonhierarchical classifier
pattern classification
privacy
machine learning algorithms
resilience
Resiliency
security domain
signature augmentation
Support vector machines
system monitoring
Training
Win32
Win64
Formal Specification
automated malware classification
binary error rate
classification models
commercial anti-virus products
Companies
computer security
computer viruses
corporate computers
family variant
feature extraction
anti-virus companies
hierarchical labels
hierarchical learning algorithms
Hierarchical Machine Learning
hierarchical structure
home computers
Human behavior
industrial-scale malware dataset
label hierarchy achieves
learning (artificial intelligence)
biblio
Binary malware image classification using machine learning with local binary pattern
Submitted by grigby1 on Wed, 06/20/2018 - 11:56am
machine learning
visualization
Trojan horses
Support vector machines
Resiliency
resilience
pubcrawl
privacy
pattern classification
Metrics
malware images
malware classification methodology
malware classification
malware
binary image
local binary pattern
learning (artificial intelligence)
LBP feature
invasive software
image texture
image descriptors
Image color analysis
image classification
Human behavior
feature extraction
Cyber-security
classification
binary malware image classification
biblio
Malware classification using static analysis based features
Submitted by grigby1 on Wed, 06/20/2018 - 11:56am
malware classification
Vegetation
Training
static analysis
Resiliency
resilience
pubcrawl
program diagnostics
privacy
pattern classification
ordinary opcode n-gram based features
Metrics
anti-virus vendors
malware binaries
malware
machine learning features
machine learning algorithms
learning (artificial intelligence)
Human behavior
feature extraction
Decision trees
control statement shingling
computer viruses
biblio
Profile hidden Markov model for malware classification \#x2014; usage of system call sequence for malware classification
Submitted by grigby1 on Wed, 06/20/2018 - 11:55am
obfuscation
Trojan horses
tools
system call sequence
system call
Resiliency
resilience
pubcrawl
proteins
protein sequences
profile hidden markov model
privacy
pattern classification
obfuscation technique
bioinformatics
microsoft windows
Metrics
malware technology
malware files
malware classification
malware
invasive software
Human behavior
Hidden Markov models
Grippers
DNA sequences
DNA
biological informatics
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