Clustering Botnet IP Addresses for Login Security
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Solution Overview
Problem
Credential stuffing attacks, where leaked login credentials are used across multiple servers via botnets to bypass conventional IP-based security measures, pose a significant risk as they distribute login traffic evenly across numerous IP addresses, making detection difficult and allowing unauthorized access to user accounts.
Innovation Solution
A clustering approach is used to identify IP addresses associated with botnets by analyzing login pairs of user identifiers and IP addresses, generating user and IP maps to detect clusters that exceed a threshold, and adding suspected botnet IP addresses to a blacklist for enhanced security measures like multi-factor authentication or CAPTCHA during subsequent login attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional IP-based security measures are used to block login attempts, then security against single-IP attacks is improved, but detection of botnet attacks is worsened because botnets distribute traffic evenly across numerous IP addresses
Solution Approach 1:
The patent merges multiple individual IP address analyses into a collective cluster analysis. By grouping IP addresses into clusters based on their login attempt patterns and combining their evaluation, the system detects botnet attacks that distribute traffic across many IPs. The cluster-level risk score aggregates threats from multiple IP addresses, enabling detection of distributed botnet attacks that individual IP monitoring would miss.
Solution Approach 2:
The patent creates a universal risk assessment mechanism that evaluates both individual IP addresses and clusters of IP addresses using the same risk scoring framework. This multi-functional approach allows the system to handle both single-IP attacks and distributed botnet attacks uniformly, adapting the evaluation scope based on the detected attack pattern while maintaining consistent security criteria.
2Reliability
If additional authentication measures like multi-factor authentication or CAPTCHA are applied to all login attempts, then security is improved, but user experience is worsened due to increased friction for legitimate users
Solution Approach 1:
The patent applies different authentication requirements to different IP addresses and clusters based on their localized risk profiles. Instead of uniform authentication policies, the system evaluates each IP cluster's risk score and applies additional authentication measures only to those exhibiting suspicious patterns. This localized approach enhances security for targeted clusters while maintaining smooth login experiences for legitimate users from low-risk clusters.
Solution Approach 2:
The patent performs preliminary risk assessment of IP clusters before processing individual login attempts. By pre-evaluating IP cluster characteristics and assigning risk scores in advance, the system can make rapid decisions about whether to apply additional authentication. This preliminary action allows legitimate logins to proceed without friction while pre-identifying clusters that require enhanced verification.
3Reliability
If IP addresses are blacklisted to block botnet traffic, then security is improved, but false blocking of legitimate users is worsened without accurate botnet identification
Solution Approach 1:
The patent replaces simple mechanical IP blacklisting with a sophisticated risk scoring mechanism based on cluster analysis. Instead of automatically blocking IP addresses based on rigid rules, the system evaluates clusters of IP addresses using multiple factors including login patterns, geographic distribution, and temporal characteristics. This substitution of mechanical blacklisting with analytical risk assessment reduces false positives while maintaining effective botnet blocking through nuanced decision-making.
Data Source
AI summary
Methods and systems are provided for identifying suspect Internet Protocol (IP) addresses, in accordance with embodiments described herein. In particular, embodiments described herein include obtaining a set of login pairs comprising login identifiers (e.g., user identifiers) and IP addresses used in attempts to login to a source. A set of IP clusters is generated using the set of login pairs. Each IP cluster can include one or more IP addresses identified as related based on a login identifier being used to attempt to login to the source via multiple IP addresses or an IP address being used to attempt to login to the source via multiple login identifiers. Thereafter, it is determined that a particular IP cluster exceeds a threshold amount of IP addresses. Each of the IP addresses within the particular IP cluster is designated as a suspect IP address.