Cyberthreat Detection via First-Order Rule Learning on Social Media
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Solution Overview
Problem
Existing systems face challenges in modeling and reasoning about the veracity of social media posts, particularly on large-scale platforms like Twitter, due to complex and diverse data representation, missing attributes, and the need for dynamic learning of first-order rules to detect emerging cyber threats.
Innovation Solution
A cyberthreat detection system using a commodity cluster with a distributed file system, employing first-order logic, probability theory, and graph theory to construct a user-centric graph and automatically learn rules, ranking them based on weights to identify suspicious content and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If handcrafted rules are used in the knowledge base, then the system can reason about veracity of social media posts, but the system cannot adapt to emerging cyber threats as new rules appear and disappear over time
Solution Approach 1:
The system employs automatic rule learning algorithms that enable the knowledge base to self-update and adapt to emerging cyber threats without manual intervention. The algorithms analyze social media data patterns and automatically generate, refine, and update rules based on observed behaviors and threats.
Solution Approach 2:
The knowledge base transitions from a static set of handcrafted rules to a dynamic system where rules are continuously learned, updated, and adapted. The rule learning process operates iteratively, allowing the system to respond to changing threat landscapes and emerging patterns in social media data.
2Reliability
If the knowledge base contains a large number of entities and facts from many posts, then the system can comprehensively detect cyber threats, but the reasoning process becomes computationally complex and time-consuming
Solution Approach 1:
The large knowledge base is divided into smaller partitions or subsets that can be processed independently and in parallel. This segmentation allows the system to manage computational complexity by breaking down the reasoning task into manageable chunks while maintaining comprehensive coverage of all entities and facts.
Solution Approach 2:
The system applies reasoning to the most relevant and critical subsets of the knowledge base first, focusing computational resources on high-priority areas. By prioritizing partial reasoning on key threats and entities, the system achieves timely detection of critical cyber threats without requiring complete reasoning over the entire knowledge base.
3Measurement precision
If the system analyzes both user behavior and post content to ascertain veracity, then the detection accuracy improves, but the data representation becomes more complex with 100+ attributes per tweet
Solution Approach 1:
The system extracts and focuses on the most critical and discriminative attributes from the 100+ available tweet attributes. By selecting only the most relevant features for veracity detection, the system reduces data representation complexity while maintaining high detection accuracy. Irrelevant or redundant attributes are excluded from the analysis.
Solution Approach 2:
Different attributes and analysis methods are applied to different aspects of the data based on their specific characteristics. User behavior attributes are analyzed using appropriate methods, while post content attributes use different techniques. This localized approach optimizes the analysis for each attribute type while managing overall system complexity.
Data Source
AI summary
A cyberthreat detection method and system includes a distributed file system and a commodity cluster. The commodity cluster has a plurality of servers. A data array of key-value pairs related to social media is received; it stores a plurality of predetermined ground predicates. A ground predicate graph is constructed for each user then partitioned into balanced portions Pi each corresponding to a server and the ground predicates stored on that server. In parallel on each server, a plurality of leaned rules are determined for the files stored on that server. From a union of the plurality of learned rules, the system determines a respective weight for each of the learned rules. The plurality of rules are ranked in order of accuracy by the plurality of weights.