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on
1
Intro
2
OUTLINE
3
USER BEHAVIOR ANALYTICS
4
MACHINE LEARNING
5
DATA SOURCES
6
DATA FORMATS
7
DATA NORMALIZATION: BEFORE
8
DATA NORMALIZATION: AFTER
9
ERP SECURITY LOGGING
10
THREAT MODEL Use Cases
11
ANOMALY TYPES
12
ANOMALIES VS. THREATS
13
STATIC ANOMALY DETECTION
14
CONTEXT BUILDING
15
CONTEXT THRESHOLD
16
CONTEXT MATCHING
17
ANOMALY ANALYSIS
18
TEMPORAL ANOMALY DETECTION
19
FEATURE ENGINEERING
20
FEATURE SELECTION
21
FEATURE ENCODING
22
MODEL IMPLEMENTATION
23
MODEL MEMORY
24
MODEL DESIGN Architecture
25
MODEL PARAMETERS
26
SEQUENCE LENGTH
27
KNOWLEDGE BASE SORTING
28
ADAPTIVE THRESHOLD
29
CONCLUSIONS
Description:
Explore advanced machine learning techniques for user behavior anomaly detection in this conference talk from the Hack In The Box Security Conference. Delve into various methods and ideas for building security analytics solutions that understand user behavior trends and identify abnormal activity using state-of-the-art neural networks. Learn about empowering feature selection with clustering algorithms, implementing behavioral whitelisting, tuning scoring engines, predicting user actions with recurrent neural networks, and generating synthetic datasets. Gain insights into peer group analysis and other innovative approaches to enhance cybersecurity in enterprise systems and business applications.

Applying Machine Learning to User Behavior Anomaly Analysis

Hack In The Box Security Conference
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