scholarly journals A Deep Learning Approach to Network Intrusion Detection Using Deep Autoencoder

2020 ◽  
Vol 34 (4) ◽  
pp. 457-463
Author(s):  
Srikanthyadav Moraboena ◽  
Gayatri Ketepalli ◽  
Padmaja Ragam
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Human Interaction ◽  
Classification Model ◽  
Knowledge Discovery In Databases ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Intrusion Prevention ◽  
Network Intrusion

The security of computer networks is critical for network intrusion detection systems (NIDS). However, concerns exist about the suitability and sustainable development of current approaches in light of modern networks. Such concerns are particularly related to increasing levels of human interaction required and decreased detection accuracy. These concerns are also highlighted. This post presents a modern intrusion prevention deep learning methodology. For unattended function instruction, we clarify our proposed Symmetric Deep Autoencoder (SDAE). Also, we are proposing our latest deep research classification model developed with stacked SDAEs. The classification proposed by the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Canadian Institute for Cybersecurity -Intrusion Detection System (CICIDS 2017) data sets was implemented in Tensor Flow, a Graphics Procedure Unit (GPU) enabled and evaluated. We implemented and tested our experiment with different batch sizes using Adam optimizer. Promising findings from our model have been achieved so far, which demonstrates improvements over current solutions and the subsequent improvement for use in advanced NIDS.

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

scholarly journals MFVT:An Anomaly Traffic Detection Method Merging Feature Fusion Network and Vision Transformer Architecture

2021 ◽  
Author(s):  
Ming Li ◽  
Dezhi Han ◽  
Dun Li ◽  
Han Liu ◽  
Chin- Chen Chang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Feature Fusion ◽  
Imbalanced Data ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Network Intrusion

Abstract Network intrusion detection, which takes the extraction and analysis of network traffic features as the main method, plays a vital role in network security protection. The current network traffic feature extraction and analysis for network intrusion detection mostly uses deep learning algorithms. Currently, deep learning requires a lot of training resources, and have weak processing capabilities for imbalanced data sets. In this paper, a deep learning model (MFVT) based on feature fusion network and Vision Transformer architecture is proposed, to which improves the processing ability of imbalanced data sets and reduces the sample data resources needed for training. Besides, to improve the traditional raw traffic features extraction methods, a new raw traffic features extraction method (CRP) is proposed, the CPR uses PCA algorithm to reduce all the processed digital traffic features to the specified dimension. On the IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that the performance of the proposed MFVT model is significantly better than other network intrusion detection models, and the detection accuracy can reach the state-of-the-art level. And, When MFVT model is combined with CRP algorithm, the detection accuracy is further improved to 99.99%.

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 Based on an Efficient Neural Architecture Search

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1453
Author(s):  
Renjian Lyu ◽  
Mingshu He ◽  
Yu Zhang ◽  
Lei Jin ◽  
Xinlei Wang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Traffic Monitoring ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Traffic Classification ◽  
Neural Architecture ◽  
Network Intrusion ◽  
Network Traffic Classification

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.

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