 | cloch001 wrote: Malware Detection: The Perfect Score That Meant Nothing
Before I trained a single model for this assignment, the dataset already offered a perfect score.
The task was static malware detection. Each Windows executable had to be classified as malicious or benign from its Portable Executable (PE) header (More) |
| cloch001 wrote: 97% Accurate and Still Missing Spam
A spam filter that never catches a single spam message would still score 87.4 percent accuracy on my dataset. That one fact shaped this whole assignment.
I built two classifiers for SMS spam: logistic regression and multinomial Naive Bayes. I tra (More) |
| cloch001 wrote: Linear Algebra Behind for Cybersecurity ML Models
Naive Bayes assumes every feature is independent. For spam detection, that's provably false — words in a sentence are obviously correlated. The model works well anyway.
Figuring out why a wrong assumption still produces a right answer taught me (More) |
| cloch001 wrote: How ML Detects DDoS Attacks: Benefits and Limits
A rule-based system catches the DDoS attack you've already seen. An attacker who stays under your volumetric threshold walks past it.
Machine learning changes the question from "is traffic above X requests per second" to "does this traffic look s (More) |