Network Abuse Mitigation via Traffic Diversion and Sink-Holing
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Solution Overview
Problem
Spam and Denial of Service (DoS) attacks significantly disrupt network services, with existing solutions like Deep Packet Inspection and statistical analysis being incomplete in identifying and filtering malicious traffic, leading to system slowdowns and blacklisting of legitimate users.
Innovation Solution
A system comprising a media provider network with elements such as Deep Packet Inspection, Policy Server, Application Manager, DNS/email sink-hole, and walled garden, which monitors and throttles malicious traffic, diverts it to a sink-hole, and notifies users through a walled garden, using heuristics and statistical analysis to identify and mitigate Spam and DoS attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Deep Packet Inspection and statistical analysis are used to identify malicious traffic, then detection accuracy is improved, but system performance deteriorates due to processing overhead and slowdown
Solution Approach 1:
The system performs preliminary actions by pre-configuring detection rules, thresholds, and classification models before malicious traffic occurs. Statistical baselines are established in advance, allowing rapid detection without real-time complex analysis during peak traffic periods, thus maintaining both accuracy and performance.
Solution Approach 2:
The inspection process is segmented into multiple stages: lightweight initial filtering using pre-defined rules, intermediate statistical analysis, and deep packet inspection only for suspicious traffic. This hierarchical segmentation reduces overall processing overhead while maintaining high detection accuracy for malicious traffic.
2Measurement precision
If mail systems scan each message to determine legitimacy, then spam filtering accuracy is improved, but system reliability deteriorates due to processing time and potential crashes
Solution Approach 1:
The system performs preliminary actions by pre-establishing spam detection rules, blacklists, and statistical baselines before message processing. This allows rapid classification of messages using pre-computed data structures, reducing processing time while maintaining filtering accuracy and preventing system crashes from excessive processing load.
Solution Approach 2:
The spam filtering system dynamically adjusts its inspection depth and processing intensity based on the suspected maliciousness level of each message. High-confidence spam is filtered quickly using lightweight rules, while ambiguous messages receive deeper inspection, creating a dynamic processing model that maintains both accuracy and system reliability.
3Reliability
If ISPs increase spending on anti-abuse systems and high-availability tools, then protection capability is improved, but cost increases significantly
Solution Approach 1:
The system enables self-service by allowing end-users to install and maintain anti-malware software on their own devices, and by providing users with tools to clean infected systems. This shifts part of the protection burden from the ISP to the user, reducing the cost burden on ISPs while maintaining overall network protection capability.
Solution Approach 2:
The system introduces intermediary components such as sandboxing environments, virtual machine-based analysis platforms, and collaborative threat intelligence networks that mediate between detection needs and computational resources. These intermediaries enable sophisticated analysis without requiring direct proportional increases in ISP spending.
4Reliability
If free anti-abuse software is provided to users, then user protection is improved, but adoption rate worsens as not all users download the software
Solution Approach 1:
The system implements feedback mechanisms that notify users when their devices are detected engaging in malicious activity, providing education and guidance on installing protective software. This targeted feedback approach increases adoption by addressing user needs at the moment of risk, rather than relying on passive distribution of free software.
Data Source
AI summary
Method of limiting offending messages communicated over a network, such as but not limited to messages associated with Spam and DoS attacks. The message limiting optionally including limiting bandwidth or other communication capabilities associated with an entity communicating or facilitating communication of the messages.