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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1. High-Level Overview – Probability and Statistics In the real world, we rarely have complete information. Data is noisy, measurements contain errors, and future events are uncertain. Probability theory provides a rigorous mathematical framework for: Quantifying uncertainty in a principled way Making optimal decisions when outcomes are uncertain Building models that generalize beyond observed […]
TLS (Transport Layer Security) – Real-World Application What is TLS? Transport Layer Security (TLS) is a cryptographic protocol that provides secure communication over computer networks. It’s the successor to SSL (Secure Sockets Layer) and is widely used to secure web traffic (HTTPS), email, VPNs, and other network communications. Core Security Objectives: Confidentiality: Data encrypted so […]
Complete Guide to RSA, Rabin, Goldwasser-Micali, and ElGamal Cryptosystems Part I: RSA Encryption – Plain and OAEP RSA Algorithm Overview RSA remains one of the most widely used public-key cryptosystems. Understanding both its basic form and secure implementations is crucial for modern cryptography. Key Generation Process Choose two large primes: p and q (typically 1024+ […]
Basic Cryptographic Definitions Core Security Goals Confidentiality: information is accessible only to authorized parties Integrity: information hasn’t been altered by unauthorized parties Authenticity: information originates from claimed source Non-repudiation: sender cannot deny sending the message Threat Models: Passive adversary: Only observes communications (eavesdropper) Active adversary: Can modify, insert, or delete messages Adaptive adversary: Can change […]
TF1.x vs TF2.0 Pioneering lirary for building deep learning models, launch November 2015. Its free, open source, originally developed by Google. Other libraries: PyTorch – from FB, October 2016 TensorFlow 2.0 Major new version, September 2019 Dynamoic computation graphs Not backward compatible with TF1 Closer to PyTorch TF1.x vs TF2.0 vs PyTorch TF1.x PyTorch Computation […]
Adversarial Examples Inputs generated by adversaries to fool neural networks. Two types: Semantic based perturbations Restricted area to manipulate pixels Modify a specific area of the image Simulate real world scenarios Pervasive Perturbations Full access to pixel alteration Modify the whole image Different distance metrics Defense and Detections Adversarial detections – determine whether input […]
How model inversion attack works? Attacker first trains a separate ML model known as Inversion Model based on the output of the target model Goal is to predict the input data (original dataset used to train the target model) Attacker can exploit information based on the input Types of MIA attacks: Query based attacks: […]
Below are various papers reviewed regarding security vulnerabilities and adversarial attacks against machine learning. 6thSense Intrusion Detection System (IDS) for smart devices This paper presents 6thSense, a novel intrusion detection system (IDS) designed to defend against sensor-based threats in smart devices, particularly Android smartphones. The framework uses context-aware models and machine learning techniques to detect […]
Adversary Knowledge White-box = adversary has complete knowledge of the targeted model, including its parameter values, architecture, training method and in some cases its training data Black-box = adversary has no knowledge about the ML model except input output samples of training data or input output pairings obtained using the target model as an oracle […]
Model Evaluation Techniques This notebook will only deal with commonly used evaluation metrics for regression and classification. This list is not exhaustive, you are encouraged to look at the other metrics that can be used. References: (1) Scikit-Learn : https://scikit-learn.org/stable/modules/model_evaluation.html (2) https://github.com/maykulkarni/Machine-Learning-Notebooks Useful Resources : https://scikit-learn.org/stable/modules/model_evaluation.html https://scikit-learn.org/stable/modules/model_evaluation.html#mean-absolute-error In [1]: import numpy as np import matplotlib.pyplot as […]