scholarly journals A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1854
Author(s):  
Jevgenijus Toldinas ◽  
Algimantas Venčkauskas ◽  
Robertas Damaševičius ◽  
Šarūnas Grigaliūnas ◽  
Nerijus Morkevičius ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Image Recognition ◽  
Computer Network ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Suggested Approach ◽  
Network Intrusion ◽  
Novel Approach ◽  
Benchmark Datasets

The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two publicly available benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed approach achieves 99.8% accuracy in the detection of the generic attack. On the BOUN DDos dataset, the suggested approach achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.

scholarly journals Network Intrusion Detection using a Deep Learning Approach

2020 ◽  
Vol 9 (3) ◽  
pp. 59-64
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Computer Hardware ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Wide Range

At present situation network communication is at high risk for external and internal attacks due to large number of applications in various fields. The network traffic can be monitored to determine abnormality for software or hardware security mechanism in the network using Intrusion Detection System (IDS). As attackers always change their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection .The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including ID. Deep Learning (DL) is a subgroup of Machine Learning (ML) which is hinged on data description. The new model based on deep learning is presented in this research work to activate operation of IDS from modern networks. Model depicts combination of deep learning and machine learning, having capacity of wide range accurate analysis of traffic network. The new approach proposes non-symmetric deep auto encoder (NDAE) for learning the features in unsupervised manner. Furthermore, classification model is constructed using stacked NDAEs for classification. The performance is evaluated using a network intrusion detection analysis dataset, particularly the WSN Trace dataset. The contribution work is to implement advanced deep learning algorithm consists IDS use, which are efficient in taking instant measures in order to stop or minimize the malicious actions

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