Network Intrusion Detection Based on an Efficient Neural Architecture Search

Abstract

Deep learning has been applied in the field of network intrusion detection and has yielded good results. In malicious network traffic classification tasks, many studies have achieved good performance with respect to the accuracy and recall rate of classification through self-designed models. In deep learning, the design of the model architecture greatly influences the results. However, the design of the network model architecture usually requires substantial professional knowledge. At present, the focus of research in the field of traffic monitoring is often directed elsewhere. Therefore, in the classification task of the network intrusion detection field, there is much room for improvement in the design and optimization of the model architecture. A neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal. For this reason, we propose a model that can perform NAS in the field of network traffic classification and search for the optimal architecture suitable for traffic detection based on the network traffic dataset. Each layer of our depth model is constructed according to the principle of maximum coding rate attenuation, which has strong consistency and symmetry in structure. Compared with some manually designed network architectures, classification indicators, such as Top-1 accuracy and F1 score, are also greatly improved while ensuring the lightweight nature of the model. In addition, we introduce a surrogate model in the search task. Compared to using the traditional NAS model to search the network traffic classification model, our NAS model greatly improves the search efficiency under the premise of ensuring that the results are not substantially different. We also manually adjust some operations in the search space of the architecture search to find a set of model operations that are more suitable for traffic classification. Finally, we apply the searched model to other traffic datasets to verify the universality of the model. Compared with several common network models in the traffic field, the searched model (NAS-Net) performs better, and the classification effect is more accurate.

Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

“…MIDS usually gives excellent detection accuracy, particularly for previously known intrusions. Nonetheless, this approach is questionable due to its inability to detect novel attacks and requires more extended time to analyze and process the massive volume of data in the signature database [14][15][16]. The authors of [17] proposed an exceptional signature-based intrusion detection system that effectively enhanced the detection rate of SQL injections within a database.…”

A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)

“…Nevertheless, this approach is questionable due to its inability to detect novel attacks. Also, it requires more time to analyze and process the massive volume of data in the signature databases ( Khraisat et al, 2020 ; Lyu et al, 2021 ). The authors of Jabbar & Aluvalu (2018) presented an exceptional high-level SIDS architecture, which includes both distributed and centralized modules that effectively enhanced the protection of IoT networks against internal and external threats.…”