Deep Comparison Analysis : Statistical Methods and Deep Learning for Network Anomaly Detection

Abstract—The detection of attacks and anomalies in superviseddatasets is critical for maintaining the security and integrity ofsystems and networks. Traditional methods for attack detectionoften rely on supervised learning techniques, which may struggleto accurately identify novel or complex threats. In this study,we propose a novel approach to enhancing attack detectionmodels by leveraging deep learning techniques for anomaly detection.Specifically, we introduce a deep learning framework thateffectively captures intricate patterns and relationships withinsupervised datasets to identify anomalous behavior indicativeof potential attacks. Through extensive experimentation andevaluation on diverse datasets, we demonstrate the superiorperformance of our proposed approach compared to traditionalmethods. Our results highlight the effectiveness of deep learningin enhancing the accuracy and efficiency of attack detectionin supervised datasets, paving the way for more robust andadaptive security systems. Overall, this research contributes tothe advancement of anomaly detection methodologies and underscoresthe importance of integrating deep learning techniques intothe realm of cybersecurity for improved threat mitigation anddefense.Keywords: Attack Detection , Supervised Datasets , DeepLearning Approach , Anomaly Detection , Cybersecurity.2021