Overview of Advanced Threat Analytics and IntelligenceThe 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
Apache Metron OverviewApache Metron is a cybersecurity application framework which 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 SecurityApache 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 LearningA 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 LearningA 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 SecurityThreat 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
Enabling Cyber Security with Apache Metron and SpotApache Metron is a cyber security application to enable streaming data security, detect and respond to threats.It provides Security Monitoring and Analysis having mechanism to store, capture, normalise any type security telemetry at extremely high rates.Enables Real-Time processing, Advanced Behavioural Analytics and application of enrichments.Efficient information storage for concise security visibility.Moreover, centralised view of data and alerts passed through the system. It comprises of four key capabilities -
- Security Data Lake provides mechanism to search and query for Operational Analytics. Cost Effective storage of enriched telematic data.
- Pluggable Framework to add new enrichment services to raw streaming data. Pluggable Extensions for threat feeds and ability to customize security dashboards.
- Security Application including alerting, replay utilities, evidence store, agents to ingest data sources.
- Threat Intelligence Platform includes defense techniques using Anomaly detection and Machine Learning algorithms applied in Real-Time events.
Apache Spot for Cyber Security and Threat Intelligence SolutionsApache Spot is cyber security project to leverage insights from packet and flow analysis involving threat detection, investigation, advanced analytics, identifying unknown threats, perimeter flows, internal flows and DNS flows to create fast and efficient data analysis. It uses Parallel Ingest Framework, Machine Learning and Operational Analytics to detect -
- Suspicious DNS Packets
- Reduction of Mean Time to Incident Detection & Resolution (MTTR)
- Threat Incident and Response
- Threat Hunting
- Open Data Models
- Detection of Known and Unknown Threats via Machine Learning