Bio-Inspired Cyber Security Framework for APT Detection
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Solution Overview
Problem
Current Intrusion Detection Systems (IDSs) are insufficient in detecting low-signature Advanced Persistent Threats (APTs) due to their focus on anomaly detection and failure to identify vague attack clues, leading to missed alerts and inadequate data collection, which hampers effective threat identification and response.
Innovation Solution
A Bio-Inspired Cyber Security Assurance Framework (BICSAF) that employs distributed software and hardware agents capable of reconfiguring data collection, executing targeted workflows, and analyzing forensic data to automatically identify and adapt to emerging threats, using an Attack Hypotheses Generation module and Workflow Generation module to generate and adjust workflows based on threat intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy IDSs collect all sensor data continuously, then complete data is available for analysis, but organizational resources are overloaded
Solution Approach 1:
The IDS dynamically adjusts sensor activation and data collection intensity based on current threat levels and investigation needs. Sensors are activated on-demand rather than continuously, allowing the system to scale resource usage according to actual security requirements while maintaining detection effectiveness.
Solution Approach 2:
The system changes operational parameters of sensors based on investigation context. During active threat investigations, sensors are reconfigured to collect more detailed data; during normal operation, data collection is reduced to essential levels, optimizing the balance between detection accuracy and resource consumption.
2Reliability
If IDSs use anomaly detection algorithms, then normal system behavior is learned, but vague attack clues are missed
Solution Approach 1:
The system incorporates feedback loops where investigation outcomes and threat intelligence are fed back into the detection algorithms. This allows the system to learn from both normal behavior patterns and actual attack patterns, improving its ability to distinguish between benign anomalies and genuine threat indicators.
Solution Approach 2:
The system performs preliminary analysis of sensor data to identify potential attack clues before full investigation. By pre-processing and flagging suspicious patterns early, the system can focus resources on promising leads while maintaining awareness of broader system behavior.
3Measurement precision
If IDSs reduce false-positive alerts through filtering, then alert quality improves, but vague attack clues are filtered out
Solution Approach 1:
The alert processing system is segmented into multiple filtering stages with different specificity levels. Early stages filter obvious false positives, while later stages preserve vague but potentially significant clues for human analyst review. This multi-layered approach maintains alert quality without discarding valuable investigative leads.
Solution Approach 2:
An intermediary analysis layer is introduced between automated filtering and final alert generation. This intermediary evaluates vague clues in context, determining whether they represent genuine threats or benign anomalies, thereby preserving important attack indicators while filtering true false positives.
4Productivity
If sensors are pre-coded to collect specific data, then data collection is efficient, but adaptive response to emerging threats is limited
Solution Approach 1:
Sensor configurations are made dynamic rather than static. Sensors can be reprogrammed in real-time to collect different types of data based on emerging threat patterns. This allows the system to maintain efficient data collection while adapting to new threat vectors and attack methods.
Solution Approach 2:
Sensors are designed with multi-functionality, capable of collecting multiple types of data through reconfiguration. A single sensor platform can switch between different collection modes depending on threat context, eliminating the need for dedicated pre-coded sensors for each threat type while maintaining collection efficiency.
Data Source
AI summary
A framework for efficiently and automatically exploring a data network and accurately identifying network threats, which comprises a plurality of software and hardware-based agents, distributed over the data network. The agents are capable of adjusting or reconfiguring, on the fly, the behavior of the agents and their ability to collect data in a targeted manner, so as to investigate suspicious incidents and alerts and collect data that was not yet collected by the system; collecting forensic data by executing tasks defined in workflows, being distributed threat intercepting programs and reporting about the collected forensic data, back to a Central Control Unit (C&C). Distributed threat intercepting programs (“workflows”) are used to provide instructions to agents, to perform branching and provide instructions to the Central Control Unit (C&C), which orchestrates the agents to assure proper execution of the workflows; analyzes the collected information and presents ongoing status to an operator supervising the data network.