Deep Packet Inspection Quarantine for Fraudulent Message Screening
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Solution Overview
Problem
Existing systems are ineffective in mitigating phishing attacks and fraudulent data messages that exploit personal computing devices, leading to vulnerabilities in private systems and potential data breaches.
Innovation Solution
A computing system employing deep packet inspection (DPI) to analyze network traffic, identify malicious sources, and quarantine suspicious messages, using machine learning algorithms to predict fraudulent sources and implement screening actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems are used to filter malicious messages, then system complexity is kept low, but security effectiveness deteriorates due to inability to detect sophisticated phishing attacks
Solution Approach 1:
The security system is segmented into multiple specialized components: a machine learning model for predicting fraudulent sources, a deep packet inspection engine for analyzing message contents, and a quarantine mechanism for isolated handling of suspicious messages. This segmentation allows each component to focus on specific detection tasks, improving overall security effectiveness while managing complexity through modular architecture
Solution Approach 2:
A quarantine zone is introduced as an intermediary between the trusted network environment and untrusted external sources. Suspicious messages are redirected to this intermediate zone for isolated analysis, preventing potential malware from directly accessing the private network. This intermediary layer enhances security by adding a buffer zone that contains threats before they can cause harm
2Measurement precision
If deep packet inspection is implemented to detect malicious code, then detection precision improves, but processing time increases due to examination of packet contents
Solution Approach 1:
The machine learning model performs preliminary action by predicting whether a message source is fraudulent before the deep packet inspection occurs. This pre-assessment allows the system to prioritize inspection resources for messages from predicted fraudulent sources, examining their contents in detail while potentially skipping or reducing inspection for messages from trusted sources, thereby maintaining high detection precision while reducing overall processing time
Solution Approach 2:
The system applies partial deep packet inspection by examining only the portions of messages from predicted fraudulent sources that contain potential malicious code, rather than performing complete inspection on all messages. This selective approach focuses computational resources on the most suspicious traffic, achieving effective detection while minimizing the time loss associated with comprehensive inspection of all network traffic
3Reliability
If machine learning algorithms are used to predict fraudulent sources, then security detection capability improves, but computational energy consumption increases
Solution Approach 1:
The machine learning model serves multiple functions: it predicts fraudulent sources, prioritizes traffic for inspection, and provides risk assessment scores that guide the deep packet inspection process. This multi-functionality allows a single computational component to perform several security tasks, improving overall detection capability while reducing the need for additional separate systems that would consume more energy
4Reliability
If comprehensive network traffic monitoring is implemented, then security coverage improves, but system performance deteriorates due to high processing load
Solution Approach 1:
The machine learning model performs preliminary filtering of network traffic to identify messages from predicted fraudulent sources before they reach the deep packet inspection stage. This pre-screening allows the system to apply comprehensive monitoring selectively only to suspicious traffic, maintaining high security coverage for critical threats while avoiding the performance degradation that would result from comprehensive inspection of all network traffic
Data Source
AI summary
Systems and methods receive, by an internet provider, a network traffic analysis subscription request to screen incoming network traffic using a DPI protocol, the network traffic including data messages from external parties to a plurality of recipient devices, the DPI protocol being configured to detect malicious code by examining contents of data packets as well as a packet header of the data packets and predict that a source of the data packets is likely a fraudulent source, the network traffic analysis subscription request identifying a plurality of subscribed devices. Incoming network traffic directed to the subscribed devices is monitored using the DPI protocol at a network gateway. Based on the monitoring, it is determined that a message that includes data packet(s) is coming from a source predicted to be fraudulent and a screening action is performed to quarantine the message.