Malfeasant Targeting Detection via Threat Level Analysis
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Solution Overview
Problem
Current network security measures fail to effectively identify and prevent targeted malfeasant attacks on individual users, as they primarily rely on blocking communications without analysis, allowing malicious actors to repeatedly target users with similar or slightly altered malicious communications, which can evade detection.
Innovation Solution
A system and method that identifies malfeasant communication data packets, generates reports based on previous malicious communications and user access attributes, determines a threat level, and transmits alerts when the threat meets or exceeds a predetermined threshold, incorporating machine learning to enhance detection and prevention of targeted attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If network security blocks communications without analysis, then protection speed is improved, but detection precision deteriorates
Solution Approach 1:
The system performs preliminary analysis of communication data packets before blocking them. By examining packet contents, headers, and patterns in advance, the system can make more accurate blocking decisions, preventing both malicious communications and false positives from reaching users.
Solution Approach 2:
The system implements feedback mechanisms by monitoring blocked communications and adjusting detection algorithms based on observed patterns. This continuous learning process improves detection precision over time while maintaining fast blocking responses through optimized decision trees and machine learning models.
2Measurement precision
If network security stores communication contents for analysis, then detection precision is improved, but system complexity increases
Solution Approach 1:
The system extracts only the essential features and metadata from communication data packets for analysis, rather than storing complete communication contents. This selective extraction approach maintains detection precision by capturing critical security indicators while significantly reducing storage requirements and system complexity.
Solution Approach 2:
Instead of storing original communication contents, the system creates simplified representations or copies of critical packet attributes for analysis. These condensed data structures preserve the information needed for detection while minimizing storage overhead and processing complexity.
3Reliability
If network security blocks all communications, then security coverage is improved, but productivity deteriorates
Solution Approach 1:
The system applies different security inspection depths to different communication types and users based on risk assessment. High-risk communications receive thorough analysis and blocking, while low-risk communications undergo minimal inspection and pass through quickly, maintaining both security coverage and communication productivity.
Solution Approach 2:
The system performs partial security analysis on most communications and reserves intensive inspection for suspected malicious packets. This selective approach ensures comprehensive security coverage for threats while minimizing processing overhead on benign communications, thereby maintaining overall productivity.
Data Source
AI summary
Systems, computer program products, and methods for detecting and preventing malfeasant targeting of individual users in a network are provided. The method includes identifying a malfeasant communication data packet directed to a target user in a network. The method also includes causing a transmission of a malfeasant report for the target user. The malfeasant report includes information relating to previous malfeasant communication data packet(s) directed to the target user and one or more user access attributes including an access level to the network for the target user. Based on the malfeasant report and the malfeasant communication data packet, the method further includes determining a malfeasant threat level. The malfeasant threat level indicates a threat of a malfeasant communication to the target user. The method further includes causing a transmission of a malfeasant threat level alert in an instance in which the malfeasant threat level meets or exceeds a predetermined threat threshold.