Weighted Network Traffic Anomaly Detection for Software Failures
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Solution Overview
Problem
Existing technologies fail to efficiently detect anomalies in network traffic caused by malfunctioning software applications, leading to network congestion and resource exhaustion at organizations, resulting in dropped calls and wasted network, processing, and memory resources.
Innovation Solution
A system that proactively detects anomalies in software applications by analyzing network traffic, determines a countermeasure action, and generates custom notifications to inform users, reducing network traffic and congestion, and improves underlying operations by addressing the anomaly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the organization attempts to satisfy a large volume of user contacts manually, then user service coverage is improved, but network resources are exhausted and network congestion occurs
Solution Approach 1:
The system performs preliminary anomaly detection and notifies users before they attempt to contact the organization about software issues. By detecting anomalies proactively and alerting users in advance, the system prevents the formation of large volumes of contact requests, thereby avoiding network resource exhaustion while maintaining user service coverage.
2Ease of operation
If the organization accepts all inbound communications, then user communication needs are met, but network congestion and dropped calls increase
Solution Approach 1:
The system applies preliminary anti-action by detecting software anomalies and notifying users before they initiate contact attempts. This preemptive notification prevents users from making calls that would ultimately fail due to the underlying software issue, thereby reducing network congestion and improving call completion rates while maintaining communication availability for legitimate requests.
3Ease of operation
If the system processes all incoming network traffic, then comprehensive user support is provided, but processing time and resource consumption increase
Solution Approach 1:
The system extracts and addresses the root cause of user support requests by detecting software anomalies proactively. By identifying and notifying users of software issues before they contact support, the system removes the need to process numerous redundant support requests, thereby reducing processing time and resource consumption while maintaining comprehensive user support through automated anomaly detection and notification.
Data Source
AI summary
A system receives a set of input data streams from different data sources. At least one of the set of input data streams comprises a message that indicates an anomaly with respect to a topic associated with a software application. The system determines the topic, the anomaly, and a set of metadata associated with the topic from the set of input data streams. The set of metadata comprises an occurrence frequency of the anomaly in messages, a number of data sources from which messages are received, or a timeframe window within which the messages are received. The system assigns a set of weight values to the set of metadata and determines an accumulated weight value. If it is determined that the accumulated weight value is more than a threshold weight value, the system communicates an alert indicating to execute a countermeasure action that addresses the anomalous topic.