Source: Deep Learning on Medium
Advanced Threat Analytics and Intelligence — XenonStack
Overview of Advanced Threat Analytics and Intelligence
The security aspect has changed dramatically over recent years. The cyber-attacks nowadays have become more pervasive, persistent, and proficient than ever at escaping and contaminating traditional security architecture. Cyber threats have become more complex and complicated. Many companies meet stealthy attacks in their systems. These attacks are targeted towards intellectual property and consumer information theft or encryption of important data for ransom. Therefore, to protect your IT assets, you must know what is coming, secure your digital interactions, detect, and manage inevitable breaches, and safeguard business chain and regulative compliance.
Threat Detection is the art of identifying attacks on a computer. While there are a large variety of Cyber Security attacks, most of them fit into one of four categories –
- Denial of Service (DoS)
- User to Root
- Remote to User
Hence, companies are looking for Cyber Security Services and Solutions to ensure the security of their IT network. In this use case, we will guide you through how we built an effective cybersecurity and threat detection system using machine learning.
Apache Metron Overview
Apache Metron is a cybersecurity application framework that provides the ability to ingest, process and store various security data feeds at a scale level to detect cyber anomalies and enable organizations to take action against them rapidly.
Apache Spot Architecture for Cyber Security
Apache Spot is a cybersecurity project, aimed to bring Advanced Analytics to all IT Telemetry data on an open, scalable platform. Apache Spot expedites the threat detection, investigation, and remediation via machine learning and consolidates all enterprise security data into a comprehensive IT telemetry hub based on open data models.
Threat Detection Using Deep Learning
A multi-layered Deep Learning-based system is very robust, scalable and adaptable. All the identified incidents & patterns are denoted by a risk score, to help investigate the breach, control data loss and take precautionary actions for the future.
Threat Detection Using Machine Learning
A Machine Learning-based Threat Detection system automates the process of extracting insights from file samples through better generalization at identifying unknown variations. It also helps in reducing human analysis time.
Challenges to Real-Time Cyber Threat Intelligence
- To perform Real-Time Threat Intelligence on trillions of messages per year.
- Storing and Processing the unstructured security data.
- Combine Machine Learning and Predictive Analytics to perform Real-Time Threat Analytics.
Solution Offerings for Threat Detection and Cyber Security
Threat Analytics and Intelligence by automating the process of Threat Detection and Analysis. Following steps are performed to Automate the process –
- Network Dataset
- Pre-Processing of Data
- Feature Extraction
- Reduce Data Amount
- Improve Accuracy
- Avoid Overfitting
Training and Testing of Data Using Classification Models
- Decision Tree
- Random Forest
- Naive Bayes
- Result Analysis
Originally published at https://www.xenonstack.com.