Deep Learning-Based Framework for the Detection of Cyberattack Using Feature Engineering. (24th December 2021)

• Record Type: Journal Article
• Authors: Akhtar, Muhammad Shoaib; Feng, Tao
• Other Names: Karuppiah Marimuthu Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-12-24
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/6129210
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Digital systems are changing to security systems in contemporary days. It is time for the digital system to have sufficient security to defend against threats and attacks. The intrusion detection system can identify an anomaly from an external or internal source in the network system. Many kinds of threats are present, that is, active and passive. These dangers could lead to anomalies in the system by which data can be attacked and taken by attackers from the beginning to the destination. Machine learning nowadays is a developing topic; its applications are wide. We can forecast the future through machine learning and classify the right class. In this paper, we employed the new binary and multiclass classification model of Convolutional Neural Networks (CNNs) to identify the anomaly of the network system. In this respect, we used the NSLKDD dataset. Our model uses a Convolutional Neural Network (CNN) to conduct binary and multiclass classification. In both datasets, we build a DL-based DoS detection model. We focus on the DoS category in the most extensively used IDS dataset, KDD. As the name implies, CNN is the most extensively used the DL model for image recognition. Adding a pooling layer to the convolution layer minimizes the size of the feature data extracted from the image while maintaining I/O and spatial information. The CNN model has shown the promising results of multiclass and binary classification in terms of validation loss of 0.0012 at 11th epochs and validation accuracy of 98% and 99%, respectively.