Automated Social Engineering Attack Simulation for Network Vulnerability Assessment
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Solution Overview
Problem
Existing methods for assessing vulnerabilities in networks of Data Processing Units (DPUs) are manual and lack automation, failing to effectively identify and address vulnerabilities in communication links and user interactions, which can lead to network compromise.
Innovation Solution
A system and method for simulating hacking attacks on networks of DPUs, users, and communication links, using a master agent to gather information, create an Information Model, generate Multiple Attack Vector (MAV) graphs, and launch attacks in a distributed manner, with slave agents to perform multi-stage attacks and generate reports on compromised networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual penetration testing methods are used to assess vulnerabilities, then human judgment and expertise can be applied to identify security flaws, but the process is time-consuming, labor-intensive, and cannot scale effectively across large networks
Solution Approach 1:
The system enables automated self-assessment of network vulnerabilities through software agents that autonomously perform penetration testing without continuous human intervention. The agents can independently execute attack scenarios, analyze results, and generate reports, allowing the network to self-diagnose security weaknesses while maintaining high detection accuracy through programmed vulnerability assessment algorithms.
Solution Approach 2:
The patent replaces manual mechanical penetration testing processes with automated software-based systems. Instead of human testers manually exploiting vulnerabilities, automated agents use programmed methods to simulate attacks, assess vulnerabilities, and identify security flaws, thereby eliminating the time-consuming nature of manual testing while preserving detection capability through systematic automated analysis.
2Productivity
If existing vulnerability assessment tools are used, then an overview of possible vulnerabilities can be provided, but they cannot replace human judgment entirely and miss contextual understanding of attack scenarios
Solution Approach 1:
The system introduces software agents as intermediaries between automated vulnerability scanning tools and human security analysts. These agents execute automated assessments, collect detailed information about network vulnerabilities, and present findings in a structured format that enhances human judgment rather than replacing it. The agents bridge the gap between automated speed and human contextual understanding by providing comprehensive, structured data for analyst review.
Solution Approach 2:
The patent divides the vulnerability assessment process into distinct modular components executed by specialized agents. Each agent focuses on specific aspects such as information gathering, vulnerability identification, attack simulation, and report generation. This segmentation allows parallel execution of multiple assessment tasks, improving productivity while maintaining reliability through specialized, focused analysis in each domain.
3Measurement precision
If comprehensive penetration testing covering all network elements is performed manually, then all vulnerabilities including those in communication links and user interactions can be identified, but the complexity and resource requirements become unmanageable
Solution Approach 1:
The system segments the network into discrete assessable units and deploys specialized software agents for different network elements including DPUs, communication links, and user interactions. Each agent type is designed to assess specific vulnerability categories, enabling comprehensive coverage across the entire network infrastructure while managing complexity through modular, targeted assessment approaches rather than monolithic manual testing.
Solution Approach 2:
The patent creates universal software agents capable of performing multiple assessment functions across different network elements. These multi-functional agents can adapt their assessment strategies based on the target system type, whether assessing DPU vulnerabilities, analyzing communication link security, or evaluating user interaction risks. This universality reduces overall system complexity by using standardized agent architectures rather than requiring separate specialized tools for each assessment type.
4Productivity
If automated systems are introduced to perform penetration testing, then productivity and scalability improve, but the system complexity and development requirements increase significantly
Solution Approach 1:
The automated penetration testing system is divided into independent, modular agent components that can be developed, deployed, and maintained separately. Each agent handles specific assessment tasks, allowing the system to scale by adding or configuring individual agents rather than redesigning the entire system. This modular architecture improves productivity through parallel execution while managing complexity through clear separation of concerns and standardized agent interfaces.
Data Source
AI summary
The present invention describes a method for performing one or more social engineering attacks on a plurality of humans connected in a network for assessing vulnerabilities of the humans, wherein the Network comprises at least one of a plurality of data processing devices, memory devices and a plurality of communication links. The method includes gathering information about human profiles including collecting information about target users from actively used social and search sites and performing an automated Social Engineering (SE) phase and updating an Information Model based on the gathered information. Furthermore, the method includes generating a Multiple Attack Vector (MAV) graph based on the information gathered and one or more scan parameters. Moreover, the method includes launching one or more social engineering attacks based on the MAV graph to assess vulnerabilities in the humans in the Network.