scholarly journals Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5213
Author(s):  
Chin-Shiuh Shieh ◽  
Wan-Wei Lin ◽  
Thanh-Tuan Nguyen ◽  
Chi-Hong Chen ◽  
Mong-Fong Horng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Denial Of Service ◽  
Gaussian Mixture ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Ddos Attacks ◽  
Open Set ◽  
The Impact

DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems—the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks’ technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.

Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model

2021 ◽  
Author(s):  
Thanh-Tuan Nguyen ◽  
Chin-Shiuh Shieh ◽  
Chi-Hong Chen ◽  
Denis Miu
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Ddos Attacks

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Live-load strain evaluation of the prestressed concrete box-girder bridge using deep learning and clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 1051-1063 ◽  
Author(s):  
Hanwei Zhao ◽  
Youliang Ding ◽  
Aiqun Li ◽  
Zhaozhao Ren ◽  
Kang Yang
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prestressed Concrete ◽  
Gaussian Mixture ◽  
Live Load ◽  
Box Girder ◽  
Concrete Box Girder ◽  
Box Girder Bridge ◽  
Girder Bridge

The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.

scholarly journals Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Preetha Jagannathan ◽  
Sujatha Rajkumar ◽  
Jaroslav Frnda ◽  
Parameshachari Bidare Divakarachari ◽  
Prabu Subramani
Keyword(s):  
Deep Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Vehicle Classification ◽  
Traffic Surveillance ◽  
Local Descriptor ◽  
Learning Technique ◽  
Institute Of Technology ◽  
Beijing Institute

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

A Method to Calculate Carbon Footprint of Processing Sectors Based on Gaussian Mixture Model

2013 ◽  
Vol 415 ◽  
pp. 692-696 ◽  
Author(s):  
Xue Ping Liu ◽  
Zhi Shan Liu ◽  
Dong Xiang ◽  
Lang Gao ◽  
Yang Cui ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Carbon Footprint ◽  
Gaussian Mixture ◽  
Statistical Character ◽  
The Impact

Carbon footprint is used to measure the impact of products or services on environment in recently years. The main technology in evaluating carbon footprint comes from LCA and PAS2050. However, the detail in calculating carbon footprint is not well studied. In this paper, the focus was drawn on the processing sectors. Due to the statistical character of data collected in processing sectors, the GMM (Gaussian Mixture Model) is introduced to calculate carbon footprint. With this method, the statistical meaning of carbon footprint data is well understood.

scholarly journals Detecting Vehicle Motion using Deep Gaussian Mixture Model with SCI-kit Learn

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1216-1218
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Mixture Models ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Vehicle Motion ◽  
Deep Learning Neural Network ◽  
Architecture Development

Detecting vehicle motions are a progressively significant part in road surveillance and Traffic organizing systems. This paper presents a new Deep Gaussian based mixture model that predicts accurate in detecting vehicle motions. Although the existing arrangements based on conventional Gaussian mixture model which is limited in insufficient of many distinct points which eliminate covariance and solutions relative to infinite likelihood. In the proposed scheme, the deep learning neural network is used for including the more points with nested mixture models. To overcome the effects of adding more points the modification achieved in architecture development. The validation of proposed scheme is achieved with real-time videos and process with scikit learn based model.

scholarly journals Research on the error probability distribution of photovoltaic output prediction based on output fluctuation characteristics and Generalized Gaussian Mixture Model

2021 ◽  
Vol 256 ◽  
pp. 02004
Author(s):  
Peng Yan ◽  
Chenmeng Xiang ◽  
Wen Zhou ◽  
Can Su
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prediction Error ◽  
Forecast Error ◽  
Gaussian Mixture ◽  
Error Distribution ◽  
Distribution Model ◽  
Coefficient Of Determination ◽  
Stable Operation ◽  
Accuracy Evaluation

Photovoltaic power output forecast error exists objectively and inevitably, and it can provide a guarantee for safe and stable operation of the power system through analyzing its characteristics. In this paper, the influence of predicted output fluctuation characteristics (predicted output amplitude and power variation) on prediction error was studied based on the analysis of variance (ANOVA) method. The prediction error conditions were classified into six types based on the clustering of numerical characteristics of predicted output. Then, a Generalized Gaussian Mixture Model (GGMM) was proposed to fit the prediction error distribution of each type of photovoltaic output. The mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE) were used as accuracy evaluation indexes. The example analysis showed that the GGMM can satisfy the asymmetry and kurtosis diversity of the error distribution after division by conditions, and the fitting result is better than that of the normal distribution, improved Laplace distribution and t Location-Scale distribution model.

Sign in / Sign up

Export Citation Format

Share Document