This work was generated using this link so i won't lose this research work. Back up. Retraining Random Forest (RF) models with high-quality synthetic data is a powerful and necessary strategy for creating robust Distributed Denial of Service (DDoS) detection systems [executive_summary[0]][1]. This approach directly confronts the core challenges of real-world network security data: extreme…
Development of Network Anomaly Detection Models Using Random Forest
🛡️ Development of Network Anomaly Detection Models Using Random Forest In our upgraded-happines project, we built a robust pipeline for training models to detect network anomalies and cyberattacks using Random Forest. Key techniques included: Integration and normalization of public datasets Aggregated multiple sources (CIC-IDS2017, MAWI, Stratosphere, USTC-TFC2016). Data cleaning: removal of duplicates, null values, and irrelevant columns. Feature normalization…
Sobre el IDS que estoy desarrollando
He estado todo el verano investigando, tanto en temas de domotica con python, arquitectura zero trust, software distribuido y big data con scala/spark/java, un proyecto de ciberseguridad en la que descubrí una, en mi opinión, muy grave falla en youtube y en los proveedores de video, pues permite literalmente la exfiltración de información confidencial de…
About the IDS I’m developing
I've been researching all summer, both in home automation with Python, zero trust architecture, distributed software and big data with Scala/Spark/Java, a cybersecurity project in which I discovered a, in my opinion, very serious flaw in YouTube and video providers, as it literally allows the exfiltration of confidential information from the attacked company by saving…
Aironman techblog 