Network Security System Identifying Common Attackers Across Multiple IP Addresses
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Solution Overview
Problem
Current network security systems struggle to detect common attackers who use multiple source addresses to launch advanced, multistage attacks, as existing methods like IDS and WAFs are inadequate in identifying the original attacker in complex scenarios.
Innovation Solution
A network security system that analyzes attack data by adding derived attributes such as source location and time-based information, and uses a density-based clustering algorithm like DBSCAN to group attacks originating from the same attacker, even when they occur from different IP addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional IDS or WAF devices are used to detect attacks, then simple attacks can be detected, but advanced multistage attacks from multiple source addresses cannot be effectively identified
Solution Approach 1:
The patent combines multiple attack detection devices (IDS, WAF, endpoint protection) into a unified system that shares threat intelligence and attack data. This merging enables the system to correlate attacks from different source addresses and identify common attackers across multiple vectors, resolving the limitation of individual devices detecting only simple attacks.
Solution Approach 2:
The patent adds temporal and behavioral dimensions to attack detection by analyzing attack patterns over time and across multiple source addresses. Instead of detecting only single-point attacks, the system examines attack sequences, timing patterns, and behavioral characteristics to identify advanced multistage attacks, thereby expanding detection capability into new dimensional space.
2Ease of manufacture
If security systems focus on detecting simple attacks, then detection is straightforward, but identifying advanced multistage attacks with progression over time becomes difficult
Solution Approach 1:
The system performs preliminary actions by collecting and storing attack data, threat intelligence, and behavioral patterns before actual attacks occur. This preparatory data accumulation enables the system to quickly correlate and analyze advanced multistage attacks in real-time, reducing the complexity of detection without sacrificing implementation simplicity.
Solution Approach 2:
The patent introduces threat intelligence and attack data as intermediary elements that mediate between simple detection mechanisms and complex multistage attack analysis. These intermediaries provide contextual information that bridges the gap between straightforward detection and sophisticated attack identification, making the system both simple to implement and effective against advanced threats.
3Object-affected harmful factors
If attackers use different IP addresses for multiple attacks, then their identity remains hidden, but correlating multiple IP addresses to the same attacker becomes essential for effective detection
Solution Approach 1:
The system implements feedback mechanisms that continuously analyze attack patterns, source address behaviors, and temporal characteristics to identify correlations between multiple IP addresses. By feeding this analytical information back into the detection system, the network security platform can de-anonymize attackers and correlate their identities across multiple attacks, counteracting the anonymity they seek to maintain.
Data Source
AI summary
A network security system that analyzes data from network attacks to determine which attacks came from the same attacker, even if the attacker tries to disguise its identity by spreading attacks out over time and attacking from multiple IP addresses. Intrusion detection systems or firewalls may log data for each attack, such as the time of the attack, the type of attack, and the source and target addresses. Embodiments may augment this data with derived attributes that may profile the attacker's behavior. For example, some attackers may spread out attacks over time, but always attack on the same day of the week; some attackers may spread out attacks over different IP addresses, but these addresses may all be in the same country. The original and augmented data may be clustered using an algorithm such as DBSCAN, and each attacker may be identified with one of the resulting clusters.