Welcome to my site. I use this site as a repository to dump knowledge I come across, generally in the areas of software engineering and information technologies, but may include other miscellaneous tidbits. Most of the posts are written as notes for my personal reference.
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🔒 TCP/IP Attacks & Security 📑 Table of Contents Overview TCP/IP Protocols IP Security ICMP Attacks TCP Scanning & Spoofing DoS Attacks Countermeasures 📖 1. Overview and Context Learning Objectives Understand the fundamental architecture and vulnerabilities of the TCP/IP protocol stack Identify and analyze common network-layer attacks including spoofing, sniffing, and scanning Comprehend DoS […]
Network Security: Link Layer Security 3.1 ARP Cache Poisoning Link Layer Addressing Fundamentals Two Types of Addresses: IP Address (32-bit): Network-layer address used for routing packets to destination networks across the internet MAC Address (48-bit): Link-layer (physical/Ethernet) address used to identify source and destination on the same local network Most LANs use globally unique 48-bit […]
Network Security Symmetric Cryptography Sections: 2.1 Basics of Crypto | 2.2 Stream Ciphers & Adversaries | 2.3 Block Ciphers | 2.4 Hash Functions | 2.5 MAC and Authenticated Encryption 2.1 Basics of Cryptography Goals of Cryptography Primary Goal: Ensuring security of communication over an insecure medium What Does Secure Communication Mean? Confidentiality (Privacy/Secrecy): Only the […]
Part 1: Logistic Regression 1.1 Introduction to Logistic Regression Logistic regression is a probabilistic model for binary classification. Unlike linear regression, which predicts continuous values, logistic regression outputs probabilities between 0 and 1. The core idea is to apply the sigmoid (logistic) function to a linear score: \[ \sigma(z) = \frac{1}{1 + e^{-z}} \] where […]
This blog post provides a detailed explanation of optimization techniques in machine learning. It is intended for college students and explains concepts step-by-step with mathematical notations, examples, and illustrative diagrams. 1. Why Optimization Matters in Machine Learning In machine learning, training a model involves finding the best possible model from a dataset. This process is […]
This blog post provides a detailed explanation of the key concepts Logistic Regression and Support Vector Machines (SVMs). These are fundamental algorithms in machine learning for classification tasks. 1. Logistic Regression Logistic Regression is a probabilistic classification model used primarily for binary classification problems. Unlike linear regression, which predicts continuous values, logistic regression outputs the […]
This blog post provides a detailed explanation of the Nearest Neighbor algorithms, specifically focusing on k-Nearest Neighbors (KNN), as covered in predictive modeling for data mining. Introduction to Nearest Neighbor: Instance-Based Learning The Nearest Neighbor method is a discriminative classification algorithm that is non-parametric and instance-based. Unlike parametric models (e.g., linear regression) that learn explicit […]
This post explains predictive modeling as a data-mining workflow composed of four essential elements: (i) task specification, (ii) knowledge representation, (iii) learning (scoring + search), and (iv) prediction/evaluation. Contents 1. Introduction 2. The Four Components of a Predictive Modeling Algorithm 3. Task Specification 4. Knowledge Representation (Model Families) 5. Learning: Model Space, Scoring Functions, and […]
This blog post provides a detailed explanation of Naive Bayes Classifiers (NBC), a fundamental probabilistic classification algorithm in data mining and machine learning. We will explore the concepts step by step, including mathematical foundations, assumptions, learning processes, and practical considerations. Introduction to Naive Bayes Classifiers The Naive Bayes Classifier is a probabilistic model used for […]
What is Exploratory Data Analysis? Exploratory Data Analysis (EDA) is an approach to analyzing data when you do not yet have a clear hypothesis or modeling goal. Instead of jumping directly into modeling, EDA focuses on understanding the structure, patterns, and anomalies in the data. EDA aims to: Maximize insight into the dataset Uncover underlying […]