Machine Learning Model for Social Engineering Attack Detection
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Solution Overview
Problem
Existing cybersecurity solutions struggle to protect against social engineering attacks, particularly those involving human-like AI-backed chatbots, due to difficulties in comprehending dialogue and protecting user communications.
Innovation Solution
Training a machine learning model to detect social engineering attacks using data generated by a generative artificial intelligence, where the model is prompted to generate communications resembling social engineering attacks and is also trained with prior social engineering attack data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intrusion detection systems and firewalls are used to scan and analyze traffic, then network security is improved, but protection against social engineering attacks deteriorates due to inability to comprehend dialogue
Solution Approach 1:
A machine learning model is introduced as an intermediary component between traditional security systems and social engineering attack detection. The model specializes in analyzing dialogue and communication patterns,弥补ing the gap between traditional packet inspection and understanding human-like interactions. The model receives communication data, analyzes it for social engineering patterns, and provides detection results to the security system.
Solution Approach 2:
The system changes the analysis parameters from traditional network traffic metrics (packets, protocols, ports) to dialogue-specific parameters (communication patterns, language characteristics, interaction flow). By training the machine learning model on dialogue data and social engineering patterns, the system transforms how security analysis is performed, enabling detection of attacks that mimic human conversation.
2Ease of operation
If generative artificial intelligence is used to create human-like chatbots, then authenticity and user experience are improved, but vulnerability to AI-backed social engineering attacks worsens
Solution Approach 1:
The system uses generative AI to create both the threat (AI-backed social engineering attacks) and the solution (training data for detection). By having the generative AI model generate realistic attack scenarios and responses, the system creates comprehensive training datasets that capture the full range of AI-backed attack patterns. This transforms the harmful capability of generative AI into a beneficial tool for security training and detection.
Solution Approach 2:
The machine learning model is trained in advance on extensively generated attack patterns and realistic responses before deployment. This preliminary training enables the model to recognize and defend against AI-backed social engineering attacks before they occur in production environments. The system proactively prepares detection capabilities rather than reacting to attacks after they happen.
3Measurement precision
If machine learning models are trained on extensive dialogue data, then detection accuracy is improved, but training time and computational resources worsen
Solution Approach 1:
Comprehensive training data is generated in advance using a generative AI model, creating extensive datasets of attack patterns and realistic responses before the machine learning model training begins. This preliminary data generation step ensures that the training process has high-quality, diverse data available from the start, improving detection accuracy while allowing for efficient training execution.
Solution Approach 2:
The system uses the generative AI model to create synthetic copies of attack scenarios and communication patterns that mirror real-world threats. These synthetic training examples replicate the characteristics of actual social engineering attacks without requiring collection of extensive real attack data, enabling efficient model training with sufficient diversity and realism.
Data Source
AI summary
The present disclosure describes a machine learning model trained to identify social engineering attacks. A prompt may be provided to a generative artificial intelligence to create communications resembling social engineering attacks. The communications may be inputted into a machine learning model to train the machine learning model to identify social engineering attacks. The machine learning model may also be trained on actual social engineering attacks. Once trained, the machine learning model may be deployed to monitor a plurality of communication channels to detect social engineering attacks. Upon detecting a social engineering attack, a system may implement one or more remedial actions to mitigate the detected social engineering attack.