Essential Security Practices for Computing Resources
🧩 4.1 Security Administration on Computing Resources
🔹 Secure Baselines
Define secure configurations (disable unused ports, enforce strong passwords, patch systems).
Deploy via configuration tools (Group Policy, Ansible, SCCM).
Maintain through regular audits and updates.
🔹 System Hardening
| Target | Key Techniques |
|---|---|
| Workstations/Servers | Disable Telnet/FTP; patch OS/apps; use AV + firewall; limit admin privileges. |
| Mobile Devices | Use MDM, enforce encryption, PIN/biometrics, remote wipe, containerization. |
Machine Learning Model Types and Data Preprocessing
Machine Learning Model Types and Descriptions
1. Geometric Models
Geometric models represent data as points in a multidimensional space. Learning involves finding geometric structures like hyperplanes, clusters, or nearest neighbors that can separate or classify the data. These models rely on the distance between data points, vector spaces, and geometric transformations.
- Examples: Linear Classifiers (Perceptron, Logistic Regression, SVM), Nearest Neighbor Classifiers (k-NN), Clustering Models (K-means)
Understanding Independent Component Analysis and Particle Filters
Independent Component Analysis (ICA)
ICA is a statistical technique used to separate a multivariate signal into independent, non-Gaussian components. It assumes the observed data are linear mixtures of independent source signals.
Mathematically, X = A S
where X is the observed mixed signals, A is the unknown mixing matrix, and S is the statistically independent source signals.
The aim is to estimate A and S such that S = W X, where W is the separating matrix.
Steps in ICA
- Centering: Subtract mean to make
Understanding Decision Trees and Ensemble Learning Techniques
Decision Tree: A decision tree is a supervised learning algorithm that splits data into branches based on feature values to make predictions or classifications.
Gini Impurity: Gini impurity measures how often a randomly chosen element would be incorrectly classified; it indicates node impurity in a decision tree.
Nearest Neighbor Method: Classifies a sample based on the majority class of its closest training samples.
Difference Between Boosting and Bagging: Bagging reduces variance by averaging multiple
Read MoreCore Machine Learning Definitions: Algorithms and System Design
Core Machine Learning Concepts and Algorithms
Essential Definitions in Machine Learning
- Machine Learning: A field of AI where systems learn patterns from data to make predictions or decisions without being explicitly programmed.
- Find-Specific Hypothesis Algorithm: A concept learning algorithm that finds the most specific hypothesis consistent with the training examples.
- Concept Learning Task: The task of inferring a Boolean-valued function from training examples of inputs and outputs.
- Multilayer Network:
Deep Learning Fundamentals: GANs, Activation Functions, and Advanced RL
Generative Adversarial Networks (GANs)
A deep learning model featuring two competing neural networks (NNs) — the Generator and the Discriminator — designed to create realistic synthetic data.
(Mnemonic: The Generator creates fake data, and the Discriminator identifies it as real or fake.)
GAN Data Flow
Noise → Generator → Fake Data → Discriminator → Real/Fake
Core Components of GANs
- Generator: Creates synthetic data, attempting to fool the Discriminator (G).
- Discriminator: Evaluates data input,