NLP XGBoost Classifier: The Best for Uncovering DDoS Attacks Easily jbower, September 2, 2023September 2, 2023 Looking to decode the secret weapon against DDoS threats? Enter the XGBoost Classifier: your beacon in the storm of cyber attacks! Introduction In the expansive landscape of the web, as businesses fortify their digital fortresses, adversaries evolve. One of the most formidable challenges they face? Distributed Denial of Service (DDoS)… Continue Reading
NLP Decision Intelligence and Cyber Espionage: Navigating the Digital Shadows Now jbower, August 31, 2023September 1, 2023 In the digital age, where secrets are coded and hidden in binary, what’s the key to uncovering the truths buried deep within cyberspace? Unraveling the answer: Decision Intelligence, the ace up the sleeve for cyber operatives worldwide. Introduction Cyber espionage, the stealth art of the 21st century. While some seek… Continue Reading
Active Defense Building a Next-Gen Active Learning Cyber Defense System jbower, August 22, 2023September 29, 2025 This post will outline my views on creating an “Active Learning Cyber Defense System.” The type of active learning defense system I am describing can make “Real-Time Security Decisions.” For clarification in this instance, “real-time” would be: Ingest –> Analyze –> Execute = (Under 1000ms) or under 1 second from… Continue Reading
LLM Data Augmentation Using LLMs for Better Phishing Datasets jbower, May 3, 2023August 28, 2023 Can data augmentation elevate your data science status, leaving your peers in awe? Absolutely! Earlier this week, during my day-to-day as an ML Engineer, I was dealing with a particularly small phishing dataset. I needed to figure out how to increase the dataset size while generally keeping the integrity intact…. Continue Reading
AWS VSCode SSH and SSM: The Ultimate Combo for Sleek Remote Dev in AWS jbower, April 16, 2023April 23, 2023 VSCode SSH Introduction So a major issue I had run into was being able to develop ML models on my local laptop using VSCode that is able to utilize AWS Sagemaker pipelines. A simple way around this is by hosting an EC2 instance in AWS that has Sagemaker permissions and… Continue Reading
NLG OpenAI GPT-3: How to Setup Easily NOW jbower, February 14, 2023April 23, 2023 This article assumes you have already created an account with OpenAI. Once you have done that you should be able to create your API key. You will need this API key to start doing the really cool things that OpenAI GPT-3 is capable of. I would suggest starting with some… Continue Reading
Books The Elements of Statistical Learning: Data Mining, Inference, and Prediction – Book Review jbower, February 8, 2023April 21, 2023 This book is really a great foundational book if you’re interested in machine learning. If you want to learn about all of the different machine learning tools that are out there, without getting bogged down in the mathematical details, then this book is for you. The authors do a great… Continue Reading
Python How to Install Conda and Miniconda3 on Ubuntu 22.04 LTS jbower, February 8, 2023April 21, 2023 This morning I find myself working on a new data mining project that relies on Conda. I ran into a few issues along the way and decided to write this up. My pain is your gain! Next I visited https://docs.conda.io/en/latest/miniconda.html#linux-installers to find the correct installer for my version of Python… Continue Reading
NLP Most Popular Word Embedding Techniques for the Win jbower, February 2, 2023April 21, 2023 Introduction Word embeddings techniques are a type of representation for natural language processing tasks in machine learning. They map words or phrases from a vocabulary to high-dimensional vectors, capturing semantic and syntactic relationships between the words. What are Word Embedding Techniques? Word embedding techniques represent words in a high-dimensional vector… Continue Reading
Deep Learning DeepSAD: The Triumph of Semi-Supervised Anomaly Detection Deep Learning jbower, March 1, 2020October 31, 2023 Anomaly detection deep learning methods have shown to be quite promising lately when dealing with complex datasets. We tend to think of anomaly detection as an unsupervised learning problem. This is due to the large amount of “unlabeled” samples we tend to have in our dataset. But in most cases,… Continue Reading