CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

Liu, Guojie; Zhang, Jianbiao

doi:10.1155/2020/4705982

Cited by 55 publications

(30 citation statements)

References 31 publications

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“…The system can automatically perform the processes of feature extraction and classification. In the research by Liu et al [ 31 ], a CNN was used to identify an intrusion, and the accuracy of the model was improved via data enhancement technology. Xiao et al [ 32 ] adopted an auto-encoder (AE) to reduce the dimension of the data to decrease the interference of redundant features, and a CNN was adopted to identify the intrusion information.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Yao

Wang

Liu

et al. 2021

Sensors

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Among the key components of a smart grid, advanced metering infrastructure (AMI) has become the preferred target for network intrusion due to its bidirectional communication and Internet connection. Intrusion detection systems (IDSs) can monitor abnormal information in the AMI network, so they are an important means by which to solve network intrusion. However, the existing methods exhibit a poor ability to detect intrusions in AMI, because they cannot comprehensively consider the temporal and global characteristics of intrusion information. To solve these problems, an AMI intrusion detection model based on the cross-layer feature fusion of a convolutional neural networks (CNN) and long short-term memory (LSTM) networks is proposed in the present work. The model is composed of CNN and LSTM components connected in the form of a cross-layer; the CNN component recognizes regional features to obtain global features, while the LSTM component obtain periodic features by memory function. The two types of features are aggregated to obtain comprehensive features with multi-domain characteristics, which can more accurately identify intrusion information in AMI. Experiments based on the KDD Cup 99 and NSL-KDD datasets demonstrate that the proposed cross-layer feature-fusion CNN-LSTM model is superior to other existing methods.

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Section: Related Workmentioning

confidence: 99%

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Yao

Wang

Liu

et al. 2021

Sensors

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“…Drewek-Ossowicka et al [18] compared various architectures of neural network utilized for creating an Intrusion Detection System. Liu et al [19] implemented a deep Convolutional Neural Network model in order to detect network attack of an organization. Initially, the raw data was preprocessed to convert into two dimensional data.…”

Section: Related Workmentioning

confidence: 99%

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

et al. 2021

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Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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“…A particle swarm optimization (PSO) algorithm has been used to select significant features from data, and a developing system can automatically perform the processes of selecting features and classifications. In Liu et al's [23] research, a CNN algorithm was applied to identify attacks, and it was noted that deep learning based on the CNN improved the system. Xiao et al [24] adopted an autoencoder to reduce the dimension of the intrusion detection data to decrease the interference of redundant features; these features were processed using a CNN to classify the attacks.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

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CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

Cited by 55 publications

References 31 publications

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

Intrusion Detection System in the Advanced Metering Infrastructure: A Cross-Layer Feature-Fusion CNN-LSTM-Based Approach

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

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