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QINN-Based Approach to Detect Anomalies in High-Dimensional Secure Data
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Author(s): V. Thamilarasi (Sri Sarada College for Women, India), Nitendra Kumar (Amity University, Noida, India), P Ganesh Kumar (College of Engineering, Anna University, Chennai, India), G. Sivaraman (M.G.R. College (Arts and Science), India), R. RajiniGanth (SNS College of Engineering, India)and Janaki Sivakumar (Global College of Engineering and Technology, Oman)
Copyright: 2026
Pages: 26
Source title:
Advancing Cyber Threat Detection Through Quantum and Edge Computing
Source Author(s)/Editor(s): Shenson Joseph (University of North Dakota, USA), Kishor Kumar Reddy C. (Stanley College of Engineering and Technology for Women, India), Asegul Hulus (Association for Computing Machinery, Cyprus)and Tatjana Sibalija (Union University, Serbia)
DOI: 10.4018/979-8-3373-3551-3.ch010
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Abstract
Anomaly discovery is a crucial aspect of modern data analysis to finding unusual trends or behaviours in datasets across various domains like cybersecurity and finance and the healthcare. However, overfitting, in which the model becomes overly adapted to training data, results in false negatives and misclassifications, makes it difficult to target optimal detection capabilities. To get around this, training data must be carefully sanitized, eliminating unknown and irregular anomalous instances to guarantee precise anomaly detection. It is already difficult to accomplish optimal and significant feature extraction when working with noisy, high-dimensional data, like that found in network traffic. When features are too similar, overfitting and incorrect classifications may occur. However, classification accuracy may suffer if important features are removed. By improving likelihood estimation and feature separability, unsupervised techniques can assist in finding and keeping pertinent features to enhance model performance.
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