Machine Learning and Cybersecurity

2.

Data Sources and Preprocessing for Cybersecurity

2.1.

Security Data Sources

2.1.1.

2.1.1.1.

Packet Captures (PCAP)

2.1.1.1.1.

2.1.1.1.2.

Payload Analysis

2.1.1.1.3.

Protocol Decoding

2.1.1.2.

2.1.1.2.1.

2.1.1.2.2.

2.1.1.2.3.

2.1.1.3.

2.1.1.3.1.

2.1.1.3.2.

2.1.1.3.3.

2.1.1.3.4.

2.1.1.3.5.

2.1.1.4.

Web Traffic Data

2.1.1.4.1.

HTTP/HTTPS Logs

2.1.1.4.2.

2.1.1.4.3.

Web Application Firewall (WAF) Logs

2.1.2.

Host-Based Data

2.1.2.1.

2.1.2.1.1.

Operating System Logs

2.1.2.1.1.1.

Windows Event Logs

2.1.2.1.1.2.

2.1.2.1.1.3.

2.1.2.1.2.

Application Logs

2.1.2.1.2.1.

Web Server Logs

2.1.2.1.2.2.

2.1.2.1.2.3.

Email Server Logs

2.1.2.2.

File System Data

2.1.2.2.1.

2.1.2.2.2.

2.1.2.2.3.

File Access Patterns

2.1.2.2.4.

Registry Changes (Windows)

2.1.2.3.

Process and Execution Data

2.1.2.3.1.

Process Creation Events

2.1.2.3.2.

Command Line Arguments

2.1.2.3.3.

2.1.2.3.4.

2.1.2.4.

Performance Metrics

2.1.2.4.1.

2.1.2.4.2.

2.1.2.4.3.

2.1.2.4.4.

2.1.3.

Security Tool Data

2.1.3.1.

Antivirus Alerts

2.1.3.2.

2.1.3.3.

2.1.3.4.

Vulnerability Scan Results

2.1.3.5.

Penetration Test Results

2.1.4.

Threat Intelligence Data

2.1.4.1.

Indicators of Compromise (IOCs)

2.1.4.1.1.

2.1.4.1.2.

2.1.4.1.3.

2.1.4.1.4.

2.1.4.2.

Threat Actor Information

2.1.4.3.

Attack Patterns

2.1.4.4.

Vulnerability Databases

2.1.5.

User and Identity Data

2.1.5.1.

Authentication Logs

2.1.5.2.

Access Control Events

2.1.5.3.

User Behavior Data

2.1.5.4.

Privilege Changes

2.2.

Data Preprocessing Techniques

2.2.1.

2.2.1.1.

Handling Missing Values

2.2.1.1.1.

Deletion Methods

2.2.1.1.2.

Imputation Techniques

2.2.1.1.2.1.

Mean/Median Imputation

2.2.1.1.2.2.

Forward/Backward Fill

2.2.1.1.2.3.

2.2.1.2.

Outlier Detection and Treatment

2.2.1.2.1.

Statistical Methods

2.2.1.2.2.

Isolation-Based Methods

2.2.1.2.3.

Clustering-Based Methods

2.2.1.3.

Data Validation

2.2.1.3.1.

Format Validation

2.2.1.3.2.

Range Validation

2.2.1.3.3.

Consistency Checks

2.2.2.

Data Transformation

2.2.2.1.

Normalization and Standardization

2.2.2.1.1.

Min-Max Scaling

2.2.2.1.2.

Z-Score Normalization

2.2.2.1.3.

2.2.2.2.

Encoding Categorical Variables

2.2.2.2.1.

One-Hot Encoding

2.2.2.2.2.

2.2.2.2.3.

Target Encoding

2.2.2.3.

Time Series Processing

2.2.2.3.1.

Timestamp Parsing

2.2.2.3.2.

Time Zone Handling

2.2.2.3.3.

Temporal Aggregation

2.2.3.

Feature Engineering

2.2.3.1.

Network Traffic Features

2.2.3.1.1.

2.2.3.1.2.

Packet Size Statistics

2.2.3.1.3.

Inter-Arrival Times

2.2.3.1.4.

Protocol Distribution

2.2.3.1.5.

Port Usage Patterns

2.2.3.2.

Text-Based Features

2.2.3.2.1.

N-gram Analysis

2.2.3.2.2.

TF-IDF Vectorization

2.2.3.2.3.

Word Embeddings

2.2.3.2.4.

Regular Expression Patterns

2.2.3.3.

Executable File Features

2.2.3.3.1.

PE Header Information

2.2.3.3.2.

Import/Export Tables

2.2.3.3.3.

Section Characteristics

2.2.3.3.4.

Entropy Calculations

2.2.3.3.5.

2.2.3.4.

Temporal Features

2.2.3.4.1.

Time-of-Day Patterns

2.2.3.4.2.

Day-of-Week Patterns

2.2.3.4.3.

Seasonal Patterns

2.2.3.4.4.

Sliding Window Statistics

2.2.4.

Feature Selection and Dimensionality Reduction

2.2.4.1.

2.2.4.1.1.

Correlation Analysis

2.2.4.1.2.

Chi-Square Test

2.2.4.1.3.

Information Gain

2.2.4.1.4.

Mutual Information

2.2.4.2.

Wrapper Methods

2.2.4.2.1.

Forward Selection

2.2.4.2.2.

Backward Elimination

2.2.4.2.3.

Recursive Feature Elimination

2.2.4.3.

Embedded Methods

2.2.4.3.1.

LASSO Regularization

2.2.4.3.2.

Tree-Based Feature Importance

2.2.4.4.

Dimensionality Reduction

2.2.4.4.1.

Principal Component Analysis (PCA)

2.2.4.4.2.

Independent Component Analysis (ICA)

2.2.4.4.3.

Factor Analysis

2.2.5.

Handling Imbalanced Data

2.2.5.1.

Understanding Class Imbalance

2.2.5.1.1.

Imbalance Ratio

2.2.5.1.2.

Impact on Model Performance

2.2.5.2.

Sampling Techniques

2.2.5.2.1.

2.2.5.2.1.1.

Random Undersampling

2.2.5.2.1.2.

2.2.5.2.1.3.

Edited Nearest Neighbors

2.2.5.2.2.

2.2.5.2.2.1.

Random Oversampling

2.2.5.2.2.2.

2.2.5.2.2.3.

2.2.5.2.2.4.

Borderline-SMOTE

2.2.5.2.3.

2.2.5.3.

Algorithmic Approaches

2.2.5.3.1.

Cost-Sensitive Learning

2.2.5.3.2.

Ensemble Methods

2.2.5.3.3.

Threshold Adjustment

2.2.5.4.

Evaluation Considerations

2.2.5.4.1.

Appropriate Metrics

2.2.5.4.2.

Stratified Sampling

2.2.5.4.3.

Cross-Validation Strategies

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3. Core Applications of Machine Learning in Cybersecurity