Cyber Attack Early Warning System for Malware Detection
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Solution Overview
Problem
Existing cybersecurity measures are reactive and often fail to detect sophisticated malware attacks before they occur, leaving users vulnerable despite recent updates, as malware becomes increasingly targeted and sophisticated, evading generic security measures.
Innovation Solution
A cyber attack early warning system that analyzes network traffic in real-time, using an input engine, attack-specific engine, and correlation engine to classify and predict potential malware attacks, generating alerts for probable targets based on predetermined thresholds, thereby providing an early warning before an attack occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-virus software with recent updates is used, then detection capability against known malware is improved, but detection of sophisticated and targeted malware attacks fails
Solution Approach 1:
The system performs preliminary analysis of network traffic patterns, system calls, and behavioral characteristics before malware execution occurs. By establishing baseline behavior profiles and detecting anomalies in advance, the system identifies potential threats proactively rather than relying on post-infection detection, thereby improving reliability against sophisticated malware that evades traditional signature-based approaches
Solution Approach 2:
The patent replaces traditional mechanical signature-matching mechanisms with behavioral analysis and machine learning-based detection systems. Instead of relying on static virus definition files, the system analyzes dynamic behavioral patterns, system call sequences, and network traffic characteristics, enabling detection of previously unknown or heavily obfuscated malware variants without requiring constant signature updates
2Adaptability or versatility
If generic security measures are implemented, then coverage across multiple threats is improved, but detection of targeted attacks on specific users or industries fails
Solution Approach 1:
The system implements local quality by tailoring security analysis to specific contexts, industries, and organizational profiles. Instead of applying uniform generic rules, the system adapts detection parameters, behavioral baselines, and threat models based on the specific characteristics of each monitored entity, enabling precise detection of targeted attacks while maintaining broad coverage through contextualized security policies
Solution Approach 2:
The security system transitions from static generic rules to dynamic adaptive detection. Behavioral baselines are continuously updated based on observed normal operations, and detection thresholds adjust according to contextual factors such as industry-specific threats, organizational size, and historical attack patterns. This dynamic approach enables the system to maintain high detection accuracy for targeted attacks while adapting to evolving threat landscapes
3Loss of time
If real-time network traffic analysis is performed, then early detection of malware attacks is improved, but system complexity increases
Solution Approach 1:
The system segments the complex real-time analysis task into distinct modular components: network traffic capture and preprocessing, behavioral baseline establishment, anomaly detection engines, and response mechanisms. Each module handles a specific aspect of the analysis pipeline independently, allowing parallel processing of multiple traffic streams and reducing overall system complexity while maintaining real-time detection capabilities through distributed computation
Solution Approach 2:
The patent introduces intermediary layers between raw network traffic and final detection decisions, including behavioral baseline models that translate complex traffic patterns into simplified risk scores, and anomaly detection filters that pre-process data before detailed analysis. These intermediaries reduce the computational burden on core detection algorithms, enabling real-time analysis without requiring overly complex processing systems
Data Source
AI summary
A system and method for generating an alert regarding a potential attack is described. The method involves receiving data associated with previously analyzed or known malware attacks by a first network device. Additionally, the first network device receives an attack alert associated with an object analyzed and identified as suspicious by a second network device. The attack alert includes information associated with the suspicious object. For alert generation, at least a portion of the information of the attack alert is provided to a system configured to at least (i) extract feature(s) from the attack alert, (ii) determine similarities between the extracted features and features associated with the previously analyzed or known malware attacks to determine a result, (iv) compute an attack value based on the result and at least a portion of the extracted features including time-dependent and/or independent features, and (v) generate an alert based on the attack value.