User Access Anomaly Detection with Adaptive Machine Learning
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Solution Overview
Problem
Conventional rule-based security solutions are ineffective against novel threats and generate false alerts, being reactive and unable to detect emerging security breaches early, leading to potential significant damage before detection.
Innovation Solution
A machine learning-based framework using multiple unsupervised and supervised models to detect anomalies in user access behaviors, integrating feedback loops for model tuning and rule adaptation, to identify and alert critical security threats proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based solutions are used to detect security threats, then the system can detect violations of existing rules, but it cannot detect novel threats that have not been seen before
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical security data before deployment. The models learn normal and anomalous patterns in advance, enabling them to detect novel threats without relying on pre-defined rules. This preliminary training phase allows the system to adapt to new threat types as they emerge.
Solution Approach 2:
The system transitions from static rule-based detection to dynamic machine learning-based detection. The ML models continuously learn from new data and adapt their detection criteria, allowing the system to evolve with emerging threats rather than remaining fixed to predefined rules.
2Productivity
If conventional rule-based solutions are used, then the system can provide alerts based on predefined rules, but it generates false alerts and cannot distinguish novel patterns
Solution Approach 1:
The system changes the parameters used for detection from fixed rule thresholds to dynamic parameters learned by machine learning models. The models analyze multiple features simultaneously and adjust their sensitivity based on learned patterns, reducing false alerts while maintaining detection speed through optimized model inference.
Solution Approach 2:
The system implements feedback mechanisms where alert outcomes (true positives, false positives) are fed back into the machine learning models for continuous improvement. This feedback loop allows the models to learn from mistakes and refine their detection accuracy over time, reducing false alerts while maintaining productivity.
3Reliability
If rule-based solutions are employed, then the system can identify security threats, but it is reactive and cannot identify threats early before damage occurs
Solution Approach 1:
The machine learning models perform preliminary analysis of user behavior patterns and establish baselines of normal activity before threats occur. By continuously monitoring and learning from historical data, the system can detect subtle deviations that indicate emerging threats before they cause significant damage, enabling early intervention.
Solution Approach 2:
The system replaces the mechanical rule-based detection mechanism with machine learning-based anomaly detection. The ML models can identify complex, subtle patterns and correlations that rule-based systems miss, enabling earlier detection of threats by recognizing behavioral anomalies before they escalate into confirmed security incidents.
4Ease of manufacture
If hand-crafted rules are used, then the system can detect well-known security scenarios, but the rules become ineffective as the cybersecurity landscape evolves
Solution Approach 1:
The machine learning system performs self-service by automatically learning and adapting to new threat patterns without requiring manual rule updates. The models continuously train on new data, automatically adjusting their detection criteria to remain effective against evolving threats, eliminating the need for constant manual rule maintenance.
Solution Approach 2:
The system transitions from static hand-crafted rules to dynamic machine learning models that continuously adapt. The models evolve their detection strategies based on emerging threat patterns, maintaining effectiveness against evolving cybersecurity landscapes without requiring manual intervention to update rules.
Data Source
AI summary
A server for detecting anomalies associated with users accessing a network is caused to receive a dataset including static data and dynamic data. The static data includes location data of resources associated with the network and user data, and the dynamic data includes user access events. The server is further caused to detect, with a plurality of unsupervised machine learning models, an anomaly associated with a user accessing the network based on the static data and the dynamic data, determine whether the detected anomaly is critical, and in response to determining the detected anomaly is critical, generate and transmit a security alert specific to the detected anomaly to a security operation center. Other example servers, systems, apparatuses, methods, and non-transitory computer readable medium for detecting anomalies associated with users accessing a network are also disclosed.