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ML for Cybersecurity, Malware (Nandbook of research on ML & DL for…
ML for Cybersecurity
Intrusion Detection
Malicious URLs
Spam Filtering
Phishing URL detection
Capturing NW traffic
NW anomaly detection
Botnet traffic detection
Insider Threat detection
Detecting DDoS
Credit Card farud detection
Counterfreit bank note detection
Ad blocking
Social Engineering
Phishing
Voice Impersonation
Facial recognition
Deepfake
Lie detection
Personality analysis
Fake review generator
Fake news
Social Mapper
Security Layers
Layer 1
(Physical Layer)
Layer 2
(Data Link Layer)
Layer 3
(Network Layer)
Layer 4
(Transport Layer)
Layer 5
(Session)
Layer 6
(Presentation)
Layer 7
(Application)
PenTesting with ML
Captcha Breaker
DeepExploit
Web server vulnerability Scanner
Deanonymizing
IoT device type identification
Malicious URL detector
Detection of Software Vulnerabilities
IoT
Adaptive Traffic Fingerprint
Attacks agains IoT Structures
Electricity Teft in Smart Grid
Botnet Traffic Detection
Cybersecurity Aspects
(Book: Machine & Deep Learning Applications for Cyber Security)
defensive AI
application
(What)
Endpoint Security
anti-malware
anti-viruses
endpoints
workstation
mobile device
IoT device
server
cloud intstance
container
etc
Appl. Security
(incl. mobile, web, desktop, wearable, etc)
database
logs
data
code analysis
API Endpoints
fuzzing
User Behavior
User Behavior Analytics
(UEBA)
login in unusual time, etc
continuous auth
Process Behavior
frauds
anomalies in processes
NW Security
intrusion (IDS)
traffic analytics
packet inspection
malicious node detection
Physical
Cameras
Physical Access Control Systems
Tasks
(Why)
Gartner’s PPDRM model
Prediction
Prevention
Detection
Response
Monitoring
Other
Risk Analisys
Classification
(How/ When)
Realtime
Historcial/ Resting
offensive AI
application
automated information gathering
Impersonation
Deepfakes for Audio/ Video/ Text
Malware/ Spyware/ Ransomware Generation
Password & Captcha breaking
Attack automation
Malware Detection
Obfuscated JS
PDF files
etc
Data Securing & Attacking
Password Cracking
Data Hiding with ML
(steganography)
Encryption with DL
Malware
(Nandbook of research on ML & DL for Cybersecurity: ch 6)
Types
Worm
Logic Bomb
Trojan Horse
Backdoor
Mobile Code
Exploit
Downloader
Auto Rooter
Kit (Virus Generator)
Spammer
Flooders
Keyloggers
Zombie or Bot
Spyware
Adware
Ransomware
Grayware
Virus
Detection Techniques
Signature-based
Behavior-based
Specification-based
Classification Methodology
Naïve Bayes (NB)
Decision Tree (DT)
Support Vector Machines (SVM)
etc
Features
Selection
Extraction
Cyber Threads
(Nandbook of research on ML & DL for Cybersecurity: ch 7)
Mitigation
Detection
Pre-attack
On-attack
Post-attack
Attacking pole
Network
Communication Link
End-User
Types
Hackers Based Attack
Method Based Attack
Active
System modification
Disruption
NAK attack
Mailformed Input
Brute force
Passive
Location Based Attack
Internal
External
EU Papers
Application of AI in Cybersecurity
Thread Intelligence (cybercrime forecast, trends, risk evaluation)
Malware identification
Malicious web resources identification
Red-teaming / pen-testing automation
Code analysis, vulnerability identification
Response and remediation automation and support
Forensic data analytics
Fake and malicious content identification
Context-based security
Authentication mechanisms
Attacker - defender games
Dynamic attack Detection
Problems
Anomaly detection at the edge (IoT edge nodes)
"human immune system"-like security intelligence
Protect additional attack surface
support active incident response