Dynamic DCA Indicator Loading via Inflammatory Signals
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Solution Overview
Problem
Existing Dendritic Cell Algorithm (DCA) implementations for malware detection in computing systems do not effectively utilize inflammatory cytokine signals and fail to dynamically update malware protection based on feedback from other nodes or processes, leading to inadequate detection and response to malicious software.
Innovation Solution
The implementation of a DCA system that dynamically loads and updates indicators by propagating inflammatory signals between nodes or processes, adjusting sensitivity and parameters of indicators based on the received signals to enhance malware detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If DCA implementations use static indicators for malware detection, then the system structure is simple and easy to implement, but the detection capability cannot adapt to new malware threats dynamically
Solution Approach 1:
The patent implements dynamic indicator parameters that can be adjusted in real-time based on inflammatory signals from the immune system analogy. Indicators transition between static and dynamic states, allowing the system to adapt detection sensitivity and thresholds based on detected threats without requiring complete system redesign.
Solution Approach 2:
The system incorporates feedback mechanisms where inflammatory signals from detected threats feed back into the indicator parameter adjustment process. This creates a closed-loop system where detection results automatically trigger parameter modifications, enabling continuous adaptation to new malware patterns.
2Reliability
If DCA implementations do not use inflammatory signals, then the system is simpler to implement, but the response to malware attacks is inadequate and slow
Solution Approach 1:
Inflammatory signals serve as intermediary carriers that transmit threat information between different components of the DCA system. These signals mediate the interaction between threat detection and indicator parameter adjustment, enabling coordinated response without direct complex coupling between components.
Solution Approach 2:
The system changes parameters of indicators dynamically based on inflammatory signal intensity and type. Different signal conditions trigger different parameter modifications, allowing the system to adjust its detection behavior to match the severity and nature of detected threats.
3Extent of automation
If manual updates of malware protection are required, then the system has lower automation complexity, but user intervention is needed and response time is delayed
Solution Approach 1:
The DCA system performs self-updates of indicator parameters automatically based on detected threats and inflammatory signals. The system serves itself by automatically adjusting its detection capabilities without requiring external user intervention or manual configuration updates.
Solution Approach 2:
The system prepares indicator parameters for potential threats in advance by maintaining a library of adjustable parameters and pre-configured inflammatory signal responses. When threats are detected, the system can quickly apply pre-prepared parameter adjustments rather than calculating them from scratch.
Data Source
AI summary
Artificial Immune Systems (AIS) including the Dendritic Cell Algorithm (DCA) are an emerging method to detect malware in computer systems. The DCA implementation may use an inflammation signal to communicate information among the processes of device or a network or among nodes of a network, where the inflammatory signal indicates a likelihood that a process or a node has been attacked by malicious software. The DCA implementation may dynamically change the malware sensitivity and responsiveness based on the inflammation signals without requiring user intervention. The inflammatory signal includes one or more inflammatory tuples, which may include multiple components such as a strength, a PrimeIndicator, and an optional third element, p. The strength component may be an indication of the magnitude of an attack and provide a degree of certainty of the attack. The PrimeIndicator may be an identifier of the indicator type that is the source of the inflammation tuple.