Network Packet Prioritization Using Suspicion Weights
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Solution Overview
Problem
Computing service systems face difficulties in distinguishing non-malicious from malicious network traffic during attacks like DDoS, leading to potential misclassification of legitimate packets and system unresponsiveness due to overloaded network conditions.
Innovation Solution
A system that analyzes network packet attributes using suspicion weights to calculate a suspicion score, prioritizing non-suspicious packets over suspicious ones, especially during bandwidth limitations, by identifying atypical values in packet headers and using machine learning to dynamically update these weights based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic shaping or packet analysis is used to mitigate malicious attacks, then network security is improved, but network performance deteriorates due to increased processing overhead
Solution Approach 1:
The system performs preliminary analysis of packet attributes and calculates suspicion scores before packets are fully processed through the network. By pre-identifying suspicious packets using machine learning models trained on historical attack patterns, the system can prioritize legitimate traffic and drop malicious packets earlier in the processing pipeline, reducing the processing overhead for subsequent network operations while maintaining security
Solution Approach 2:
The system dynamically adjusts suspicion score thresholds and weighting parameters based on network conditions and detected attack patterns. During normal operation, lower suspicion thresholds maintain high security. During legitimate traffic surges, thresholds are adjusted to prevent false positives, balancing security with network performance by changing the parameters of packet evaluation rather than the fundamental processing architecture
2Ease of operation
If all network packets are processed equally during overload, then fairness is maintained, but legitimate traffic is dropped along with malicious traffic
Solution Approach 1:
The system applies different quality of service treatments to different packets based on their suspicion scores. Packets with low suspicion scores (legitimate traffic) receive priority handling and are preserved during overload conditions, while packets with high suspicion scores (malicious traffic) are subject to dropping or rate limiting. This local differentiation of traffic treatment maintains fairness for legitimate users while actively protecting system responsiveness
Solution Approach 2:
The packet prioritization system dynamically adjusts traffic handling based on real-time suspicion score calculations and network conditions. During normal conditions, all traffic is handled equally. During overload conditions, the system dynamically shifts to differentiated handling where legitimate packets are protected and malicious packets are dropped, allowing the system to adapt its fairness criteria based on operational context rather than applying a static policy
3Measurement precision
If packet attributes are analyzed in detail to identify malicious traffic, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The packet analysis process is segmented into multiple stages: first, quick checks of critical packet header fields provide initial suspicion scores; second, more detailed analysis of payload and advanced attributes is performed only on packets with intermediate suspicion scores. This segmentation allows the system to achieve high classification accuracy for malicious packets while minimizing processing time for clearly legitimate or clearly malicious traffic that can be identified through faster initial checks
Solution Approach 2:
The system uses machine learning models trained on historical packet data to create simplified representations or copies of attack patterns. Instead of performing exhaustive analysis on every packet, the system compares packets against these pre-processed models and patterns, achieving accurate classification by matching against known attack signatures and behaviors without requiring full detailed analysis of each packet's complete attribute set
Data Source
AI summary
A technology is described for prioritizing network packets using suspicion weights assigned to packet attributes of the network packets. An example method may include analyzing a network packet for packet attributes that have values indicating that the network packet may be associated with a potential network attack. Suspicion weights for the packet attributes identified as having a value that indicates that the network packet is associated with the potential network attack may be obtained, and a suspicion score may be calculated for the network packet using the suspicion weights.