Cloud Access Key Abuse Detection via Baseline Scoring
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Solution Overview
Problem
Cloud-based systems face increased security risks due to the extended enterprise network perimeter into the Internet, with unsecured and unmanaged devices posing threats to access key abuse detection.
Innovation Solution
A cloud-based system that receives activity data related to access keys, generates a baseline, monitors activities, and calculates a score for each activity based on a comparison to the baseline, sending alerts for high-risk activities and updating the baseline for low-risk activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud-based systems extend the enterprise network perimeter into the Internet to enable remote access, then user accessibility and flexibility are improved, but security risks and vulnerability to access key abuse increase
Solution Approach 1:
The system performs preliminary actions by generating a baseline of normal access key activities before monitoring for anomalies. This baseline is created by analyzing historical activity data to establish what constitutes normal behavior, enabling the system to detect deviations that may indicate security threats before they cause harm.
Solution Approach 2:
The system implements continuous feedback by monitoring access key activities, comparing them against the established baseline, and dynamically updating the baseline based on new information. This feedback loop enables adaptive security monitoring that improves detection accuracy over time while maintaining ease of access for legitimate users.
2Reliability
If the system monitors all access key activities in real-time to detect abuse, then security detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system extracts only the essential features and patterns from access key activity data that are relevant to detecting abuse. By focusing on specific behavioral indicators rather than analyzing every detail of all activities, the system maintains high detection capability while reducing computational complexity and resource requirements.
Solution Approach 2:
The system changes parameters by transforming raw activity data into normalized scores and comparisons against baseline values. This parameter transformation simplifies the monitoring process by converting complex activity patterns into comparable metrics that can be efficiently evaluated for anomalies.
3Measurement precision
If the system generates a baseline from historical activity data to identify normal usage, then accuracy in detecting anomalies is improved, but time for baseline generation and data processing increases
Solution Approach 1:
The system applies partial action by generating the baseline from a representative subset of historical activity data rather than processing every single data point. This approach achieves sufficient detection accuracy while significantly reducing the time and computational resources required for baseline generation.
Data Source
AI summary
Systems and methods for access key abuse detection, the systems and methods including steps of receiving activity data relating to an access key from cloud providers associated with a cloud-based system, generating a baseline for the access key based on the activity data, monitoring activities associated with the access key in the cloud-based system, and calculating a score for monitored activities based on a comparison of the monitored activities to the baseline. The present scoring system helps identify an abnormal and risky activity that indicates an attacker is abusing the access key. In addition, a baseline is created for a plurality of selected attributes that present the normal access key usage in order to identify malicious abnormal activities.