A convolutional neural network intrusion detection method based on data imbalance

Gan, Baiqiang; Chen, Yuqiang; Dong, Qiuping; Guo, Jian; Wang, Rongxia

doi:10.1007/s11227-022-04633-x

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…It is a form of machine learning in which previously learned information is applied when new examples come, and previously learned knowledge is updated in response to the new occurrences. References 41, 42 used CNN to complete network intrusion detection, which can quickly identify various types of attacks. The use of a hybrid deep learning model for intrusion detection was explored in Reference 43, which uses a hybrid model of LSTM, CNN, and GRU applied to the CICIDS2017 data set.…”

Section: Related Workmentioning

confidence: 99%

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Habib,

Khursheed

2024

Concurrency and Computation

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SummaryInternet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.

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Section: Related Workmentioning

confidence: 99%

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Habib,

Khursheed

2024

Concurrency and Computation

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“…[35,36]) and Convolutional Neural Networks (CNN) (ref. [37]), have been developed to enhance traditional detection algorithms. While they offer some improvements, selecting the best model for virus-spread network intrusion detection can be challenging, as it involves comparing multiple model groups using data from a single experiment.…”

Section: Related Workmentioning

confidence: 99%

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Ying,

Pan,

Chen

et al. 2024

Mathematics

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The field of network security is highly concerned with intrusion detection, which safeguards the security of computer networks. The invention and application of intrusion detection technology play indispensable roles in network security, and it is crucial to investigate and comprehend this topic. Recently, with the continuous occurrence of intrusion incidents in virus propagation networks, traditional network detection algorithms for virus propagation have encountered limitations and have struggled to detect these incidents effectively and accurately. Therefore, updating the intrusion detection algorithm of the virus-spreading network is imperative. This paper introduces a novel system for virus propagation, whose core is a graph-based neural network. By organically combining two modules—a standardization module and a computation module—this system forms a powerful GNN model. The standardization module uses two methods, while the calculation module uses three methods. Through permutation and combination, we obtain six GNN models with different characteristics. To verify their performance, we conducted experiments on the selected datasets. The experimental results show that the proposed algorithm has excellent capabilities, high accuracy, reasonable complexity, and excellent stability in the intrusion detection of virus-spreading networks, making the network more secure and reliable.

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“…The moderate performance is attributed to inherent limitations such as sensitivity to data noise and inability to process missing data. In Gan et al (2022) , a method combining a gradient coordination mechanism and focus loss is proposed exhibiting high accuracy. Nevertheless, it demands extensive parameter adjustments, posing a challenge in parameter tuning.…”

Section: Intrusion Detection System Based On Convolution Neural Networkmentioning

confidence: 99%

An intrusion detection system based on convolution neural network

Mo,

Li,

Wang

et al. 2024

PeerJ Computer Science

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With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system’s feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

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A convolutional neural network intrusion detection method based on data imbalance

Cited by 14 publications

References 44 publications

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

An intrusion detection system based on convolution neural network

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