Distributed Bot Detection via Local and Central Analyzers
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Solution Overview
Problem
Existing malware detection methods are ineffective in identifying polymorphic malware and botnets, as they rely on signature matching that can be evaded by mutation, and often result in delayed detection and false positives, which can lead to significant damage and privacy loss.
Innovation Solution
A distributed system employing local and central analyzers to detect command and control communications (callbacks) using packet header signature matching, deep packet inspection, and machine learning to generate probability scores, thereby identifying malicious activity and updating global signature caches for improved detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature matching is used for malware detection, then known malware can be identified, but polymorphic malware can evade detection through mutation
Solution Approach 1:
The patent segments malware detection into multiple independent analysis components: static signature matching, dynamic behavioral analysis, and callback detection. Each component operates independently and contributes to the overall detection decision, allowing the system to detect both known and polymorphic malware through multiple pathways rather than relying on a single signature-matching approach
Solution Approach 2:
The system changes the detection parameters from static signature matching alone to a multi-parameter approach including behavioral metrics, communication patterns, and callback indicators. This allows detection of polymorphic malware by monitoring how the malware behaves and communicates rather than relying solely on its changing code signature
2Reliability
If manual signature library updates are performed, then new malware signatures can be added, but detection is delayed by days or weeks
Solution Approach 1:
The system performs preliminary detection using behavioral analysis and callback detection mechanisms that do not require updated signatures. When suspicious activity is detected, the system proactively monitors and analyzes the malware's communication patterns in real-time, enabling detection before formal signature updates are completed
Solution Approach 2:
The patent implements feedback loops where detection results from one system are automatically shared with other systems. Callback detection results feed back into the signature matching system, and behavioral analysis results inform both signature updates and policy adjustments, creating a continuous improvement cycle that reduces detection delays
3Measurement precision
If antivirus scanning is performed, then malware can be detected, but damage and privacy loss may have already occurred
Solution Approach 1:
The system performs preliminary detection of callback communications and suspicious behavioral patterns before malware can execute significant damage. By monitoring network traffic and communication patterns in real-time, the system identifies and alerts on potential threats at the earliest stages of malware operation
Solution Approach 2:
The patent implements rushed detection through real-time callback monitoring and immediate alert generation. When callback patterns are detected, the system rushes through the analysis process, immediately notifying security personnel and enabling rapid response before the malware can propagate or cause significant harm
4Measurement precision
If distributed analysis system is implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The distributed system is segmented into specialized components with clearly defined responsibilities: signature matching modules, behavioral analysis modules, callback detection modules, and coordination components. This segmentation reduces complexity by allowing each component to be developed, maintained, and optimized independently while contributing to the overall detection accuracy
Solution Approach 2:
The patent implements universal coordination mechanisms that allow the same communication protocols and data formats to be used across all distributed components. The central coordinator and local analyzers use standardized interfaces and shared data structures, reducing the complexity of integration and maintenance while enabling the system to handle multiple types of malware detection
Data Source
AI summary
A system and method for detecting malicious activity through one or more local analyzers and a central analyzer. The local analyzer captures packets that are part of communications over a network, generates a signature from information obtained from one or more of the captured packets, and determines whether the signature matches any signature of a first plurality of signatures stored in a first storage device that is accessible to the first local analyzer. The central analyzer remotely receives a portion of the information and the signature from the first local analyzer in response to the signature failing to match any of the signatures stored in the first storage device. The central analyzer determines whether the signature matches any global signature stored within a second storage device that is accessible to the central analyzer.