Hardcoded Credential Detection Using Inline Login Signatures
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Solution Overview
Problem
Existing systems fail to distinguish between legitimate and malicious uses of hardcoded credentials, leading to cybersecurity risks and false positives, particularly in the context of IoT devices and operational systems.
Innovation Solution
A cybersecurity system that collects publicly disclosed hardcoded credentials, generates network traffic signatures, and analyzes login behavior across multiple devices to detect and prevent malicious uses of hardcoded credentials, using both data collection and login signatures to differentiate between legitimate and malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hardcoded credentials are used for device access, then legitimate uses such as customer support and initial setup are enabled, but cybersecurity risk increases due to widespread publication and exploitation
Solution Approach 1:
The system segments the evaluation of credential usage by analyzing multiple attributes separately (device characteristics, network behavior, temporal patterns, credential freshness) and combining them through a scoring mechanism. This allows legitimate uses to be distinguished from malicious ones by evaluating each segment independently and aggregating the results.
Solution Approach 2:
The system changes parameters by introducing a dynamic risk assessment model that evaluates multiple variables including device type, network location, time of access, and credential age. By monitoring changes in these parameters over time, the system can distinguish between legitimate access patterns and exploitation attempts while maintaining ease of operation for authorized users.
2Object-affected harmful factors
If a detection system blocks all hardcoded credential usage, then malicious attacks are prevented, but legitimate access for support and setup is obstructed
Solution Approach 1:
The system implements feedback by continuously monitoring access attempts and adjusting risk scores based on observed behavior patterns. Legitimate uses generate feedback that reinforces their legitimacy over time, while malicious attempts accumulate risk scores that trigger blocking. This feedback loop enables the system to prevent attacks while maintaining legitimate access without manual intervention.
Solution Approach 2:
The system applies dynamics by making the blocking decision adaptive rather than static. The risk assessment model dynamically adjusts its evaluation based on real-time behavior analysis, device characteristics, and credential freshness. This dynamic approach allows the system to block malicious attacks while permitting legitimate access to proceed unobstructed.
3Measurement precision
If detection accuracy is increased to distinguish legitimate from malicious uses, then false positives are reduced, but system complexity increases
Solution Approach 1:
The system segments the detection process into distinct analytical components: device characteristic evaluation, network behavior analysis, temporal pattern recognition, and credential freshness assessment. Each segment handles a specific aspect of the evaluation independently, improving detection accuracy while keeping individual components manageable in complexity.
Solution Approach 2:
The system achieves universality by creating a multi-functional risk assessment platform that handles various types of credential-based attacks and legitimate uses through a single integrated framework. The same core engine evaluates different device types, network configurations, and access patterns, reducing overall system complexity while maintaining high detection accuracy across diverse scenarios.
Data Source
AI summary
A hardcoded credential attack detection and prevention system as disclosed herein distinguishes between legitimate uses and malicious uses to prevent attacks using hardcoded credentials without obstructing legitimate uses that still rely on hardcoded credentials. The system collects publicly disclosed hardcoded credentials and creates network traffic based signatures to detect login attempts corresponding to the publicly disclosed hardcoded credentials. The system then uses the created “login signatures” to detect login attempts corresponding to the hardcoded credentials. While detection with the login signatures occurs inline at individual network devices with visibility of the network traffic, the system obtains relevant network traffic from other network devices to analyze login behavior on a wider scale. This more informed analysis can be profiling, reputation scoring, and/or behavior analysis with the aggregate network traffic to detect malicious use of hardcoded credentials that would go undetected or result in a false positive.