Real-Time ISP Blacklist Feed for DDoS Mitigation
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Solution Overview
Problem
Current DDoS attack reporting systems lack real-time capabilities, making it ineffective to block malicious IP addresses promptly, as attackers often change locations and IP addresses between attacks, and existing solutions do not provide timely insights for subscribers to mitigate imminent threats.
Innovation Solution
A system that collects and analyzes DDoS attack data in real-time, identifies malicious IP addresses, determines associated Internet Service Providers (ISPs), and sends alerts when a threshold of malicious activity is exceeded, enabling proactive blocking and notification to subscribers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time data collection and analysis is implemented, then response time to DDoS attacks is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: data collection module that gathers attack data from multiple sources, analysis module that processes the collected data in real-time, and notification module that sends alerts to subscribers. This segmentation allows each module to specialize in specific tasks, improving real-time response capability while managing overall system complexity through modular architecture.
Solution Approach 2:
A centralized database serves as an intermediary component that stores and manages attack data, IP address information, and ISP details. This intermediary layer decouples the data collection and analysis functions from the notification functions, enabling real-time processing while simplifying the overall system architecture through a central coordination point.
2Measurement precision
If comprehensive attack data is collected and analyzed, then identification accuracy of malicious IPs is improved, but data processing time increases
Solution Approach 1:
The system pre-collects and stores IP address information, ISP details, and attack patterns in a centralized database before actual attacks occur. This preliminary preparation of data allows the analysis module to quickly query and process information during attacks, achieving high identification accuracy without excessive processing time during critical response periods.
Solution Approach 2:
The analysis module applies different processing strategies to different types of data: frequently accessed IP information is cached for rapid retrieval, while detailed attack pattern analysis is performed on-demand. This local optimization of data processing quality based on access frequency and criticality enables accurate identification while minimizing overall processing time.
Data Source
AI summary
Embodiments are provided for an actionable blacklist of DDoS offenders and ISPs associated offenders. The system can collect real-time attack data and perform real-time analysis, which can be fed into a centralized database for intelligent analysis to identify offenders and report to interested subscribers. The system can receive an indication that network resources are being targeted as part of one or more DDoS attacks, and then obtain the malicious IP address of devices associated with those DDoS attacks. The system can determine the Internet Service Provider (ISP) associated with malicious IP addresses. A metric can be computed that is associated with an ISP involved in the one or more DDoS attacks. If the metric exceeds a threshold, then an alert message indicating that the first ISP is involved in the one or more DDoS attacks can be sent to a list of subscribers.