Active Learning Malicious Behavior Detection System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current malicious behavior detection systems face challenges in efficiently identifying new threats and anomalies due to overwhelming data volumes, lack of prioritization based on severity, and difficulty in maintaining large numbers of rules or signatures, leading to unmanageable workloads for security analysts.
Innovation Solution
A malicious behavior detection/prevention system utilizing active learning to classify entries into multiple classes, where entries are automatically classified and ambiguous ones are labeled by human analysts, allowing the system to learn and improve over time, enabling real-time evaluation and periodic updates similar to anti-virus signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based systems are used to detect malicious behavior, then detection capability is improved, but the number of rules to maintain becomes unmanageably large
Solution Approach 1:
The patent replaces manual rule-based detection systems with an automated machine learning classification system. The classifier automatically learns patterns from training data to identify malicious behavior, eliminating the need for manual creation and maintenance of numerous detection rules. This substitution of mechanical rule-based approaches with automated intelligent systems directly resolves the contradiction between detection capability and rule complexity.
2Measurement precision
If signature-based systems are used to detect anomalies, then identification of known threats is improved, but new or very rare events are missed
Solution Approach 1:
The system performs preliminary training with labeled data to build a classifier that can proactively identify both known and unknown threat patterns. By pre-training the model with diverse examples including rare events, the system gains the ability to detect new threats before they become common, resolving the contradiction between precise identification of known threats and adaptability to new threats.
Solution Approach 2:
The classification system is dynamic and can be retrained with new data to adapt to emerging threats. Unlike static signature-based systems, the machine learning classifier can evolve its detection capabilities by learning from new labeled examples, enabling it to maintain high identification accuracy for known threats while simultaneously adapting to detect new and rare events.
3Reliability
If all non-normal entries are sent to analysts for review, then detection thoroughness is improved, but analyst workload becomes unmanageable
Solution Approach 1:
The system extracts and automatically classifies the majority of entries using the trained classifier, removing only the most ambiguous or suspicious entries for human analyst review. This extraction approach filters out clearly benign or clearly malicious entries automatically, leaving only a small subset requiring manual analysis, thereby maintaining detection thoroughness while dramatically reducing analyst workload.
Solution Approach 2:
The classification system performs self-service by automatically processing and categorizing entries without human intervention. The classifier handles routine classification tasks autonomously, serving itself to reduce the burden on analysts. Only entries that require human judgment are presented to analysts, enabling the system to maintain thorough detection while improving productivity by eliminating unnecessary manual work.
4Measurement precision
If manual classification of all entries is performed, then classification accuracy is improved, but time and resource consumption increase significantly
Solution Approach 1:
The system applies partial manual classification only to ambiguous entries that the automated classifier cannot confidently categorize. Rather than manually classifying all entries, the system uses automated classification for clear-cut cases and reserves manual effort for borderline cases. This partial application of manual action maintains high classification accuracy while significantly reducing time and resource consumption compared to complete manual classification.
Data Source
AI summary
A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst. The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.