CGSA-RNN: Abnormal Network Traffic Detection Model Based on CycleGAN and Self-Attention Mechanism DOI
Saihua Cai,

Wen-Jun Zhao,

Hanmei Tang

и другие.

Опубликована: Окт. 22, 2023

Malicious attack is a major factor to endanger the cyberspace security. The accurate detection of abnormal network traffic generated by malicious attacks can effectively detect potential and thus protecting However, scale relatively small (i.e., there data imbalance phenomenon), which causes significant decrease accuracy. introduction CycleGAN model deal with phenomenon, but it suffers from semantic inconsistency, image distortion lack diversity. This paper proposes an called CGSA-RNN that incorporates CycleGAN, self-attention mechanism RNN overcome disadvantages model, thereby accurately detecting traffic. first takes advantage style migration perform augmentation for small-scale traffic, then replaces ReLU activation function LeakyReLU in generator reduce effects artifacts images. In addition, introduced into help better capture important features, further improving capability. Finally, uses Extensive experimental results on two publicly available datasets show compared four advanced models based augmentation, average precision, recall F1-measure are improved more than 2%.

Язык: Английский

NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts DOI Creative Commons
Moisés Cordeiro-Costas,

Hugo Labandeira-Pérez,

Daniel Villanueva

и другие.

International Journal of Electrical Power & Energy Systems, Год журнала: 2024, Номер 159, С. 110070 - 110070

Опубликована: Июнь 3, 2024

To conduct analysis on the field of electricity management in buildings is crucial to contribute clean energy promotion, efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling predictive calibrated system (EMS) using hybrid that combines long short-term memory multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast (GFS) data anticipate consumption fluctuations optimize use distributed sources, such as photovoltaic (PV) production, based knowledge prices free market one day ahead. trade-off building conducted with NSGA-II, guaranteeing exploration exploitation while minimizing costs wastes. research carried out demonstrates effectiveness LSTM-MLP model advantages NSGA-II hyperparameter tuning balance sustainable practices. tested an existing building, Industrial Engineering School located Campus Lagoas-Marcosende Universidade de Vigo, Spain.

Язык: Английский

Процитировано

6
A malicious network traffic detection model based on bidirectional temporal convolutional network with multi-head self-attention mechanism DOI
Saihua Cai, Han Xu, Mingjie Liu

и другие.

Computers & Security, Год журнала: 2023, Номер 136, С. 103580 - 103580

Опубликована: Ноя. 4, 2023

Язык: Английский

Процитировано

14
GCN-MHSA: A novel malicious traffic detection method based on graph convolutional neural network and multi-head self-attention mechanism DOI
Jinfu Chen, Haodi Xie, Saihua Cai

и другие.

Computers & Security, Год журнала: 2024, Номер 147, С. 104083 - 104083

Опубликована: Авг. 30, 2024

Язык: Английский

Процитировано

4
On the right choice of data from popular datasets for Internet traffic classification DOI Creative Commons

Jacek Krupski,

Marcin Iwanowski, Waldemar Graniszewski

и другие.

Computer Communications, Год журнала: 2025, Номер unknown, С. 108068 - 108068

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model DOI Creative Commons
Yong Cai Zhang, Pengtao Liu, Yingying Xu

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 4, 2025

Язык: Английский

Процитировано

0
MTCR-AE: A Multiscale Temporal Convolutional Recurrent Autoencoder for unsupervised malicious network traffic detection DOI
Mukhtar Ahmed, Jinfu Chen, Ernest Akpaku

и другие.

Computer Networks, Год журнала: 2025, Номер unknown, С. 111147 - 111147

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
eBiTCN: Efficient bidirectional temporal convolution network for encrypted malicious network traffic detection DOI
Ernest Akpaku, Jinfu Chen, Mukhtar Ahmed

и другие.

Journal of Computer Security, Год журнала: 2025, Номер unknown

Опубликована: Апрель 13, 2025

The growing prevalence of encrypted malicious network traffic poses significant challenges for cybersecurity, as it conceals the content from traditional detection methods. Temporal convolutional networks (TCNs) present promising capabilities extracting complex temporal features and patterns dynamic flow data. However, unidirectional nature TCNs limits their effectiveness in capturing full context traffic, which often exhibits bidirectional dependencies. Consequently, a few studies have proposed TCN (BiTCN) architectures to address limitations. these methods that require amount parameters be learned, imposes high memory requirements on computational resources training such models. In this study, we introduce efficient (eBiTCN) model, an BiTCN requires fewer yet not at expense cost effective detection. eBiTCN framework combines processor, lightweight gating mechanism, attention, dropout, novel loss function, dense layers. Extensive experiments show outperforms eight state-of-the-art competing models terms efficacy, speed, scalability. model showcased robust performance detecting evolving attacks excelled across various real-world datasets. Its efficiency speed reduced usage translates lower infrastructure costs, making accessible choice deployment. These findings highlight eBiTCN’s practicality dependability addressing contemporary security needs.

Язык: Английский

Процитировано

0
TLS-MHSA: An Efficient Detection Model for Encrypted Malicious Traffic based on Multi-Head Self-Attention Mechanism DOI Open Access
Jinfu Chen, Luo Song, Saihua Cai

и другие.

ACM Transactions on Privacy and Security, Год журнала: 2023, Номер 26(4), С. 1 - 21

Опубликована: Авг. 7, 2023

In recent years, the use of TLS (Transport Layer Security) protocol to protect communication information has become increasingly popular as users are more aware network security. However, hackers have also exploited salient features carry out covert malicious attacks, which threaten security space. Currently, commonly used traffic detection methods not always reliable when applied problem encrypted due their limitations. The most significant is that these do focus on key traffic. To address this problem, study proposes an efficient model for based transport layer and a multi-head self-attention mechanism called TLS-MHSA. Firstly, we extract during pre-processing perform statistics filter redundant features. Then, learning well generate important combined construct model, thereby detecting Finally, public dataset verify effectiveness efficiency TLS-MHSA experimental results show proposed high precision, recall, F1-measure, AUC-ROC higher stability than seven state-of-the-art models.

Язык: Английский

Процитировано

10
L2-BiTCN-CNN: Spatio-temporal features fusion-based multi-classification model for various internet applications identification DOI
Zhiyuan Li, Xiaoping Xu

Computer Networks, Год журнала: 2024, Номер 243, С. 110298 - 110298

Опубликована: Фев. 29, 2024

Язык: Английский

Процитировано

3
Enhanced Malicious Traffic Detection in Encrypted Communication Using TLS Features and a Multi-class Classifier Ensemble DOI

Cheemaladinne Kondaiah,

Alwyn Roshan Pais, Routhu Srinivasa Rao

и другие.

Journal of Network and Systems Management, Год журнала: 2024, Номер 32(4)

Опубликована: Авг. 10, 2024

Язык: Английский

Процитировано

3