Intrusion Detection via Honeypot Active Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computer systems face evolving sophisticated attacks that existing security measures struggle to detect and respond to effectively, as attackers adapt to defense mechanisms, necessitating a more automated and adaptive approach to intrusion detection and prevention.
Innovation Solution
A computer-implemented method and system that automatically monitors honeypot trap environments, captures activity data, extracts relevant attributes, applies analytics to identify potential unauthorized intrusions, assigns risk scores, and generates security rules for intrusion detection and prevention systems, enabling real-time adaptation to emerging threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection systems are used, then basic security monitoring is provided, but they cannot effectively detect sophisticated dynamic attacks that adapt to defense mechanisms
Solution Approach 1:
The system dynamically adapts its detection rules by automatically generating new security rules from honeypot data in real-time. The intrusion detection system evolves its detection capabilities continuously by learning from captured attack patterns, transforming static detection mechanisms into dynamic, adaptive security defenses that improve reliability against sophisticated attacks.
Solution Approach 2:
The system performs self-updating by automatically generating security rules from honeypot-collected data without requiring manual intervention. The intrusion detection system serves itself by autonomously learning attack patterns and translating them into detection rules, enabling continuous adaptation to evolving threats while maintaining detection effectiveness.
2Measurement precision
If manual analysis of attack data is performed, then detailed security insights are obtained, but the process is time-consuming and cannot keep pace with rapidly evolving threats
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational processing. An analytics suite automatically extracts attributes from captured attack data, identifies patterns, and generates security rules without human intervention. This substitution maintains high analysis accuracy while eliminating time delays associated with manual processes, enabling real-time response to evolving threats.
Solution Approach 2:
The system introduces an automated analytics suite as an intermediary between data capture and rule generation. This intermediary component performs sophisticated pattern recognition and attribute extraction automatically, bridging the gap between raw honeypot data and actionable security rules, thereby maintaining precision while dramatically reducing processing time.
3Loss of information
If honeypot traps are deployed to gather attack information, then data on unauthorized access attempts is captured, but the system lacks automated processing capability to convert this data into actionable security rules
Solution Approach 1:
The system performs preliminary automated processing of honeypot data by automatically extracting attributes, identifying patterns, and generating security rules directly from captured attack information. This preliminary automation transforms raw attack data into actionable security rules without manual intervention, completing the full cycle from data collection to rule deployment and eliminating the gap that previously existed.
Solution Approach 2:
The system creates a universal automated pipeline that handles multiple functions: data capture from honeypots, attribute extraction, pattern identification, risk scoring, and security rule generation. This multi-functional automation system processes diverse attack data types uniformly, converting all honeypot-collected information into actionable security rules through a single integrated automated process.
Data Source
AI summary
A computer-implemented method comprising: automatically monitoring a honeypot trap environment, to capture activity data within the honeypot trap environment, wherein the honeypot trap environment comprises a plurality of software and hardware resources that are intended to attract attempts at unauthorized use of the honeypot trap environment; automatically extracting, from the captured activity data, a plurality of attributes representing entities, events, and relations between the entities and events; automatically applying an analytics suite to identify specific combinations of the attributes as representing a likelihood of being associated with an unauthorized intrusion attempt into the honeypot environment; automatically assigning a risk score to each of the specific combinations, wherein the risk score reflect the likelihood of being associated with an unauthorized intrusion attempt into the honeypot environment; and automatically generating at least one security rule for an intrusion detection and prevention system, based on at least one of the specific combinations.