Social Engineering Attack Detection via Contextual Risk Scoring
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Solution Overview
Problem
Social engineering attacks, which manipulate individuals through psychological or cognitive biases to gain access to restricted IT systems or data, pose a significant challenge as they often exploit human vulnerabilities, making them difficult to detect and prevent with traditional cybersecurity measures.
Innovation Solution
A system utilizing a combination of machine learning and rule-based techniques to detect social engineering attacks by analyzing communications for contextualization, intention classification, and security policy violations, generating a global social engineering attack score to trigger appropriate countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures are used to prevent unauthorized access, then system security is improved, but social engineering attacks exploiting human vulnerabilities cannot be detected
Solution Approach 1:
The patent introduces an intermediary detection system that sits between users and the system they interact with. This intermediary analyzes communication patterns, contextual information, and user behavior to detect social engineering attempts before they result in unauthorized access. The intermediary includes components for extracting contextual information from communications, analyzing user behavior patterns, and generating risk scores without interfering with normal system operations.
Solution Approach 2:
The patent replaces traditional mechanical security measures (firewalls, access controls) with an intelligent detection system that uses machine learning and behavioral analysis. Instead of relying solely on technical barriers, the system substitutes human judgment with automated analysis of communication contexts, user interactions, and anomaly detection to identify social engineering attacks.
2Ease of operation
If security credentials are provided to individuals for access, then system accessibility is improved, but vulnerability to social engineering attacks increases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor user communications and interactions. The system provides real-time feedback by analyzing communication patterns, comparing them against known social engineering tactics, and alerting users or blocking suspicious interactions. The feedback loop includes collecting data from communications, analyzing contextual information, generating risk assessments, and taking corrective actions such as warnings or access denials.
Solution Approach 2:
The patent performs preliminary analysis of communications and user interactions before granting access or taking action. By extracting contextual information in advance and analyzing it against security policies and known attack patterns, the system can prevent social engineering attacks before they succeed, while still maintaining ease of access for legitimate users.
3Difficulty of detecting and measuring
If comprehensive security monitoring is implemented to detect attacks, then attack detection capability is improved, but system complexity increases
Solution Approach 1:
The patent divides the security monitoring system into distinct functional modules: contextual information extraction, user behavior analysis, risk scoring, and response mechanisms. Each module handles a specific aspect of detection, making the overall system more manageable and maintainable. The segmentation allows for independent optimization of each component and reduces the complexity burden on any single part of the system.
Data Source
AI summary
In an example embodiment, a combination of machine learning and rule-based techniques are used to automatically detect social engineering attacks in a computer system. More particularly, three phases of detection are utilized on communications in a thread or stream of communications: attack contextualization, intention classification, and security policy violation detection. Each phase of detection causes a score to be generated that is reflective of the degree of danger in the thread or stream of communications, and these scores may then be combined into a single global social engineering attack score, which then may be used to determined appropriate actions to deal with the attack if it transgresses a threshold.