SMS Spam Detection via Call Detail Record Feature Extraction
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Solution Overview
Problem
Current methods for detecting SMS spam in cellular networks are inefficient, requiring significant computational resources, raising privacy concerns, and are not effective in curbing spamming activities, as spammers can easily evade detection by changing SIM cards or using pre-paid accounts.
Innovation Solution
A method and apparatus that analyze call detail records (CDRs) to identify potential SMS spam sources by extracting features such as sender and receiver numbers, account types, device identifiers, and geographic locations, using a classification model like a decision tree-based algorithm to detect patterns indicative of spam activity, thereby increasing detection accuracy and reducing the cost of evasion for spammers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current SMS detection solutions are used, then spam detection is performed, but detection accuracy is insufficient and spammers can easily evade by changing SIM cards
Solution Approach 1:
The system performs preliminary analysis of call detail records to extract behavioral patterns and features before spam detection. By pre-processing CDR data to identify suspicious patterns such as high message volumes, frequent SIM changes, and abnormal calling behaviors, the system establishes detection criteria in advance that make it harder for spammers to evade detection through simple SIM card changes
Solution Approach 2:
The system implements feedback mechanisms where detection results and spammer behaviors are continuously analyzed to refine detection algorithms. By monitoring evolving spam patterns and updating detection models with new data, the system adapts to counter spammer evasion tactics while maintaining high detection accuracy
2Measurement precision
If comprehensive analysis methods are used to improve detection accuracy, then detection precision increases, but computational resources and time requirements increase significantly
Solution Approach 1:
The detection system segments the analysis process into distinct stages: CDR data collection, feature extraction, pattern matching, and detection decision-making. By dividing the comprehensive analysis into modular segments that can be processed independently and in parallel, the system maintains high detection accuracy while reducing overall computational burden and processing time
Solution Approach 2:
The system extracts only the most relevant features from call detail records such as message volume, frequency, destination patterns, and device identifiers. By selecting and analyzing only critical features rather than processing all available data, the system achieves high detection accuracy with reduced computational resource requirements
3Difficulty of detecting and measuring
If detailed feature extraction from CDRs is performed to identify spam patterns, then detection capability improves, but privacy concerns and data processing overhead increase
Solution Approach 1:
The system extracts only essential anonymized features from call detail records such as aggregated message counts, time patterns, and device identifiers without capturing sensitive personal information. By selectively extracting only the minimum necessary data elements required for spam detection, the system improves pattern detection capability while minimizing privacy invasions and data processing overhead
Data Source
AI summary
A method and apparatus for identifying a potential source of SMS spam are disclosed. For example, the method collects a plurality of call detail records, extracts at least one feature from each of the plurality of call detail records, and identifies the potential source of the short message service spam by analyzing the at least one feature that is extracted from each of the plurality of call detail records.