Omnichannel Social Engineering Attack Detection System
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Solution Overview
Problem
Current technologies are inadequate in effectively identifying and mitigating social engineering attacks across various communication channels, which rely on human interaction and deception, often evading detection and compromising network security.
Innovation Solution
A method and system that identify potential social engineering activity by analyzing communications across multiple channels, using characteristics such as voice and text analysis, and implementing real-time restrictions and quarantine actions to limit access and prevent damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security measures are used to detect social engineering attacks, then detection capability is limited, but system complexity and false positives increase
Solution Approach 1:
The system segments social engineering attack detection into distinct categories: digital attacks (phishing, spear phishing), phone attacks (vishing), and in-person attacks (pretexting). Each category is analyzed using channel-specific characteristics and patterns, allowing targeted detection without requiring a single complex universal system.
Solution Approach 2:
The system implements a universal detection framework that operates across multiple communication channels (email, phone, in-person) using a common architecture. The energy vector analysis and machine learning models provide multi-functional detection capabilities that adapt to different attack types without requiring separate specialized systems for each channel.
2Reliability
If omnichannel monitoring is implemented to detect attacks across multiple communication channels, then detection effectiveness improves, but processing complexity and resource requirements increase
Solution Approach 1:
The system merges monitoring capabilities across multiple communication channels (digital, phone, in-person) into a unified detection platform. By combining data from email, text messages, voice calls, and face-to-face interactions, the system achieves comprehensive omnichannel detection while using centralized processing to manage complexity.
Solution Approach 2:
The system introduces an intermediary layer that translates and normalizes data from different communication channels into a common format for analysis. This intermediary processing layer handles the complexity of multi-channel data integration, allowing the core detection algorithms to operate on standardized inputs without being burdened by channel-specific variations.
3Speed
If real-time analysis of communication patterns is performed to identify attacks, then response time improves, but computational energy consumption increases
Solution Approach 1:
The system performs preliminary analysis by establishing baseline communication patterns and energy vectors for legitimate interactions before attacks occur. By pre-computing normal behavior profiles and setting threshold values, the system can quickly compare incoming communications against these pre-established standards, enabling real-time detection without requiring intensive computational resources during active monitoring.
Solution Approach 2:
The system dynamically adjusts analysis parameters and thresholds based on the communication context and risk level. For low-risk communications, simplified parameter checks are used to minimize energy consumption, while high-risk or suspicious interactions trigger more intensive analysis with adjusted parameters, optimizing the balance between response time and computational energy usage.
Data Source
AI summary
A method, computer program product, and computer system for identifying social engineering activity associated with at least one of a first communication and a second communication based upon, at least in part, correlation to a predetermined rule. Characteristics of the communications are compared to the predetermined rule to determine if there is a correlation.