Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The monitoring and management of high-volume feature-rich traffic in large networks offers significant challenges in storage, transmission, and computational costs. The predominant approach to reducing these costs is based on performing a linear mapping of the data to a low-dimensional subspace such that a certain large percentage of the variance in the data is preserved in the low-dimensional representation. This variance-based subspace approach to dimensionality reduction forces a fixed choice of the number of dimensions, is not responsive to real-time shifts in observed traffic patterns, and is vulnerable to normal traffic spoofing. Based on theoretical insights proved in this paper,  propose a new distance-based approach to dimensionality reduction motivated by the fact that the real-time structural differences beten the covariance matrices of the observed and the normal traffic is more relevant to anomaly detection than the structure of the training data alone. Our approach, called the  distance-based subspace  method, allows a different number of reduced dimensions in different time windows and arrives at only the number of dimensions necessary for effective anomaly detection.  present centralized and distributed versions of our algorithm and, using simulation on real traffic traces, demonstrate the qualitative and quantitative advantages of the distance-based subspace approach.
['Tingshan Huang', 'Harish Sethu', 'Nagarajan Kandasamy']
I am writing a research paper on a idea A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic
[{"from": "human", "value": "I am writing a research paper on a idea A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The monitoring and management of high-volume feature-rich traffic in large networks offers significant challenges in storage, transmission, and computational costs. The predominant approach to reducing these costs is based on performing a linear mapping of the data to a low-dimensional subspace such that a certain large percentage of the variance in the data is preserved in the low-dimensional representation. This variance-based subspace approach to dimensionality reduction forces a fixed choice of the number of dimensions, is not responsive to real-time shifts in observed traffic patterns, and is vulnerable to normal traffic spoofing. Based on theoretical insights proved in this paper,  propose a new distance-based approach to dimensionality reduction motivated by the fact that the real-time structural differences beten the covariance matrices of the observed and the normal traffic is more relevant to anomaly detection than the structure of the training data alone. Our approach, called the  distance-based subspace  method, allows a different number of reduced dimensions in different time windows and arrives at only the number of dimensions necessary for effective anomaly detection.  present centralized and distributed versions of our algorithm and, using simulation on real traffic traces, demonstrate the qualitative and quantitative advantages of the distance-based subspace approach."}]