Login Risk Profiles for Large-Scale Intrusion Detection
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Solution Overview
Problem
Existing network intrusion detection systems rely on static heuristics that are easily evaded by threat actors, struggle with high volumes of login attempts, and fail to adapt to different website environments, leading to inefficiencies and disruptions.
Innovation Solution
A machine learning model trained on past login events generates pre-computed network intrusion risk profiles for login attempt sources, allowing for immediate assessment and adaptive intrusion detection, with periodic retraining to counter evolving threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static heuristics are used for intrusion detection, then the system is simple to implement, but the detection accuracy deteriorates as threat actors adapt their behavior
Solution Approach 1:
The patent implements dynamic detection by training machine learning models on historical login events and continuously updating risk profiles based on new data. The system adapts to changing threat behaviors by periodically retraining models and adjusting risk assessments, transforming the static heuristic approach into a dynamic, evolving detection mechanism that maintains accuracy against adaptive threats
Solution Approach 2:
The system changes parameters by using multiple features (geolocation, device characteristics, login patterns, time of day) to dynamically adjust risk scores. Instead of fixed thresholds, the system modifies detection parameters based on learned patterns from historical data, allowing flexible adaptation to new threat types while maintaining a relatively simple overall system architecture
2Reliability
If machine learning models are trained and pre-computed for each login attempt source, then detection accuracy and adaptability improve, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing risk profiles and training machine learning models in advance before actual login attempts occur. Historical login events are processed offline to create pre-trained models and baseline risk assessments, which are then quickly applied during live operations. This shifts the computational complexity to a preliminary phase, keeping the real-time detection system relatively simple while maintaining high accuracy
Solution Approach 2:
The system creates simplified copies or representations of complex threat patterns through pre-computed risk profiles. Instead of running full machine learning models for every login attempt, the system uses pre-generated risk assessments that capture the essence of threat patterns, reducing real-time computational complexity while preserving detection accuracy
3Speed
If real-time analysis of login attempts is performed, then detection speed is fast, but the system cannot handle high volumes of authentication traffic
Solution Approach 1:
By pre-computing risk profiles and training models in advance, the system prepares detection capabilities before traffic peaks occur. This preliminary processing allows the system to handle high volumes of authentication traffic in real-time without performing complex analysis on every single login attempt, thus maintaining both fast response times and high throughput capacity
Solution Approach 2:
The patent segments the detection process into distinct phases: offline model training and profile generation, and online real-time risk assessment. This segmentation allows computationally intensive operations to occur separately from time-critical operations, enabling the system to maintain fast response times for individual login attempts while processing large volumes of traffic through pre-prepared detection mechanisms
4Adaptability or versatility
If periodic retraining of the machine learning model is implemented, then adaptability to evolving threats improves, but loss of time for model maintenance increases
Solution Approach 1:
The system implements periodic action by scheduling regular retraining of machine learning models at predetermined intervals. This allows the system to systematically update its detection capabilities to adapt to evolving threats while maintaining predictable maintenance schedules. The periodic nature balances adaptability improvements with controlled time investment, preventing continuous retraining from overwhelming the system
Solution Approach 2:
The patent maintains continuity of useful action by implementing incremental or online learning capabilities that allow the model to continuously adapt to new threats without complete retraining. This enables the system to maintain adaptability through continuous small updates rather than periodic large-scale retraining operations, reducing the time loss associated with model maintenance while preserving adaptability to evolving threats
Data Source
AI summary
Past login activity is used to train a machine learning model and pre-compute network intrusion risk profiles. Subsequent login attempts can be evaluated with the pre-computed network intrusion risk profiles. If the profile shows sufficient intrusion risk, actions can be taken to avoid intrusion, such as requiring further authentication steps.