Multi Level Deep Learning Model for Network Anomaly Detection

Abstract

The increasing use of internet-based solutions and services in private and corporate sectors has resulted in a significant increase in personal internet involvement. This shift, concurrently exposed a heightened susceptibility to potential vulnerabilities, given the ability of malicious actors to exploit external networks, network services, or corporate infrastructures utilized for personal purposes. In recent times, there has been considerable interest in harnessing deep learning methodologies for enhancing cybersecurity, owing to their utilization of sophisticated learning algorithms for addressing pertinent online security challenges. Machine Learning (ML) and Deep Learning (DL) paradigms have been extensively applied across diverse dimensions of cybersecurity, encompassing tasks such as vulnerability assessment, malware classification, spam detection, and spoofing identification.

In this paper, for hierarchical intrusion detection is proposed a novel multi-stage approach, The proposed system comprises two distinct classification modalities: multi-class classification and binary classification, contingent upon the nature of the attack within the dataset. the KDD99 dataset was leveraged to assess the classification performance of the proposed model. Both classification approaches involve main preprocessing steps, such as feature selection, feature normalization, building a Convolutional Neural Network (CNN) classifier apply on KDD99 dataset, deploying the CNN classifier for anomaly detection.