Social Engineering Attack Detection via Linguistic Feature Analysis
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Solution Overview
Problem
Organizations face vulnerabilities in detecting social engineering attacks, particularly in digital communications channels like social media and mobile chat applications, as these attacks often evade traditional cybersecurity measures and lack visibility, leading to data loss, account takeover, and other fraudulent activities.
Innovation Solution
A system and method utilizing natural language processing, machine learning, and corpora analysis to detect social engineering attacks by extracting lexical, spelling, and topical features from messages, assessing the likelihood of an attack through a trained model, and flagging potentially malicious communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures are implemented, then network security is maintained, but social engineering attacks can still breach the network undetected
Solution Approach 1:
The patent replaces traditional rule-based and signature-based cybersecurity mechanisms with machine learning models that analyze lexical, spelling, and topical features of messages. The system uses trained classifiers to detect social engineering attacks by identifying patterns in language usage, such as unusual word choices, spelling errors, and topic deviations that indicate fraudulent communications.
Solution Approach 2:
The system changes the detection parameters from traditional security metrics to linguistic feature analysis. It extracts and analyzes multiple parameters including lexical features (word frequency, vocabulary diversity), spelling features (error patterns, character sequences), and topical features (message subject matter, context relevance) to identify social engineering attacks with high accuracy.
2Measurement precision
If advanced detection systems are deployed across all digital communication channels, then detection accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The detection system is segmented into independent modular components: a lexical feature extractor module, a spelling feature extractor module, a topical feature extractor module, and a classification module. Each module processes specific aspects of message analysis independently, allowing the system to achieve high detection accuracy while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The patent creates a universal detection framework that can analyze multiple types of digital communications (emails, SMS, social media messages) using the same machine learning models and feature extraction processes. The system is designed to handle various communication formats and channels through a unified approach, reducing overall system complexity while maintaining broad applicability.
3Speed
If real-time analysis of all incoming messages is performed, then attack detection speed improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial analysis by focusing on the most discriminative features for attack detection. Rather than analyzing every aspect of each message in equal detail, the machine learning models are trained to identify and prioritize key indicators of social engineering attacks, such as specific lexical patterns or spelling errors, allowing rapid detection with minimal processing overhead.
Solution Approach 2:
The patent implements preliminary action through pre-trained machine learning models that have already learned attack patterns from extensive training data. When a new message arrives, the system can immediately apply these pre-trained models for rapid classification without requiring extensive real-time computation, enabling fast detection while maintaining high accuracy.
Data Source
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AI summary
Embodiments of the present disclosure use natural language processing, machine learning and relevant corpora to detect social engineering attacks with a high degree of accuracy. In various embodiments, lexical features, spelling features and topical features are automatically analyzed from a source text and a model is employed to assess the likelihood that the source message is a social engineering attack.