Neural Network Packet Filtering for Adaptive DDoS Defense
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Solution Overview
Problem
Firewalls struggle to effectively identify and block large-scale network attacks like DDoS, leading to system overload and resource exhaustion due to unpredictable attack directions, which existing static rule-based filtering methods cannot adequately address.
Innovation Solution
Implement a network-packet filtering system using neural networks to analyze system-performance vectors from end hosts and packet data, determining whether to allow or discard packets based on host-performance vectors and packet information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rule-based firewall filtering is used, then basic packet filtering capability is provided, but the system cannot effectively identify and block large-scale network attacks like DDoS
Solution Approach 1:
The patent transforms the static firewall rule-based filtering into a dynamic system by introducing neural network models that continuously learn from system performance data and attack patterns. The filtering rules are no longer fixed but adapt automatically based on real-time analysis of packet characteristics and host performance metrics, enabling the system to respond to unpredictable DDoS attacks effectively.
Solution Approach 2:
The system implements feedback mechanisms where the neural network models analyze system performance information from multiple hosts and use this feedback to continuously improve attack detection and blocking capabilities. The performance data from affected hosts feeds back into the neural networks, which adjust their filtering decisions accordingly, creating a self-improving defense system against evolving threats.
2Object-affected harmful factors
If firewall filters certain types of packets, then some protection is provided, but hosts still receive attack packets from external network when overloaded
Solution Approach 1:
The patent segments the packet filtering function across multiple neural network models deployed at different hosts rather than relying on a single centralized firewall. Each host's neural network independently analyzes packets and makes filtering decisions based on local performance conditions, allowing distributed intelligence to prevent any single point of failure and maintain system stability under attack.
Solution Approach 2:
The system dynamically changes filtering parameters based on real-time system performance conditions. When hosts detect overload or attack patterns, the neural networks adjust filtering thresholds and packet handling parameters automatically, transitioning from static rule-based filtering to adaptive parameter-based filtering that maintains system stability under varying attack conditions.
3Adaptability or versatility
If neural network models are deployed at each host, then dynamic packet filtering capability is achieved, but system complexity increases
Solution Approach 1:
Each host is equipped with its own neural network model that operates autonomously, making independent filtering decisions based on local performance data without requiring constant centralized control. This self-service approach distributes the computational burden across hosts while maintaining adaptive threat response capabilities, reducing the complexity burden on any single system component.
Data Source
AI summary
A network-packet filtering system is provided. The system includes one or more end hosts and a network-packet filtering device communicable with the end hosts and an external network. Each end host obtains its host-performance vector by executing a first neural network model based on its system-performance information. The network-packet filtering device determines whether to drop a packet from the external network or to pass the packet by executing a second neural network model based on the host-performance vectors and the packet.