Botnet Detection via Network Event Proximity and Time Density
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Solution Overview
Problem
The increasing complexity of cyber threats, particularly malicious botnets exploiting IoT devices, makes it difficult to track and block distributed denial-of-service attacks and other malicious activities due to their distributed nature and use of dynamic IP addresses.
Innovation Solution
A method and system for identifying botnets by analyzing network data to determine network event proximity, time density, and trend patterns, using a detection server with processing circuitry and memory to detect botnet activity and trigger alerts or mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional IP blocking methods are used to prevent botnet attacks, then single IP addresses can be easily blocked, but thousands of dynamically changing IP addresses from distributed botnets become much more difficult to control and block
Solution Approach 1:
The patent segments the problem of botnet detection by breaking down network traffic analysis into multiple analytical dimensions: network event proximity (temporal clustering), time density (event frequency over time), and trend patterns (behavioral sequences). This segmentation allows the system to detect botnet activity through multiple independent indicators rather than relying on a single blocking mechanism, effectively addressing the challenge of distributed dynamic IP addresses.
Solution Approach 2:
The patent transitions from traditional single-dimension IP address blocking to multi-dimensional network behavior analysis. By introducing temporal dimensions (time density, event proximity) and pattern dimensions (trend analysis, behavioral sequences), the system creates a higher-dimensional detection space that can identify botnet activity regardless of IP address changes, effectively resolving the contradiction between blocking effectiveness and system complexity.
2Power
If botnets use distributed IoT devices with dynamic IP addresses to conduct attacks, then the attack coverage and processing power increase significantly, but the ability to track and block the attacks deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring network events and updating detection models in real-time. The system analyzes network event proximity, time density, and trend patterns with continuous feedback loops that adjust detection thresholds and parameters based on observed botnet behavior, enabling the system to track and detect distributed botnet attacks despite their dynamic nature and high processing power.
Solution Approach 2:
The patent introduces an intermediary detection layer between network traffic and blocking actions. This intermediary system analyzes network events through multiple dimensions (temporal proximity, time density, trend patterns) and generates detection signals that trigger appropriate responses. This intermediary layer simplifies the tracking problem by abstracting complex distributed botnet behavior into detectable pattern signatures.
3Loss of time
If real-time analysis of network data is performed to detect botnet activity, then timely identification and mitigation of attacks is achieved, but the computational resources and system complexity increase
Solution Approach 1:
The patent applies partial action by focusing detection efforts on specific critical dimensions rather than analyzing all possible network parameters. The system selectively monitors network event proximity, time density, and trend patterns - three key dimensions that provide sufficient detection capability without requiring exhaustive analysis of all network data, thus achieving timely detection with moderate system complexity.
Solution Approach 2:
The patent implements preliminary action by establishing detection thresholds and analysis frameworks in advance. The system pre-configures detection criteria for network event proximity, time density, and trend patterns, allowing it to quickly evaluate incoming network data against predetermined standards. This preliminary preparation enables rapid real-time detection without requiring complex runtime decision-making, reducing both response time and operational complexity.
Data Source
AI summary
A system and method for identifying botnets. The method includes determining a network event proximity based on collected network data, where the network data relates to at least one network device; determining time density of the network data; determining trend patterns of the network data; and determining, based on the network event proximity, time density, and trend patterns, when a botnet activity is present within the network data.