Russell Sherwood

@itsverronicca I checked the comments and didn't see any nudes.

nudy version in comments (im not even joking)

Most people know only one knot. Here are 6 you'll actually use.

Ways to Secure Your #IT Network [Infographic] Read More: en.iguru.gr/infosec #networksecurity #itsecurity #technology

How to Read Engine Oil Grades

DevSecOps Tools

Malware analysis isn't just about finding a virus, it's about understanding how it works, what it's trying to do, and how to stop it. The process usually has three main parts: 1. Choosing a machine learning approach - Supervised learning: Trained with labeled malware samples. - Unsupervised learning: Finds hidden patterns without labels. - Semi-supervised learning: Combines a small amount of labeled data with a larger set of unlabeled data. 2. Extracting useful features Analysts collect clues from the malware, such as: - Byte sequences - API and system calls - Opcodes (CPU instructions) - Network activity - File system changes - CPU registers - PE file characteristics - Embedded strings These can be gathered through static analysis (without running the file), dynamic analysis (running it in a controlled environment), or a hybrid of both. 3. Achieving the objective The goal could be detecting malware, identifying its family, classifying its category, or comparing it with other samples to find similarities, differences, and new variants. Malware analysis is like digital detective work. Every piece of data is a clue that helps security researchers understand threats and build better defenses before they spread.

SQL -> Python -> Excel💯

IPCONFIG

2/2 - DR (Disaster Recovery)

Remember this 3-2-1 Rule for Backups (for Production)

🐧 21 Linux Commands Every Cybersecurity Student Should Know

How Different Bullets Behave in Ballistic Gel

Data Cleaning for Machine Learning

Python Developer Checklist 👇