scholarly journals A Hybrid Intrusion Detection Model Combining SAE with Kernel Approximation in Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5710
Author(s):  
Yukun Wu ◽  
Wei William Lee ◽  
Xuan Gong ◽  
Hui Wang
Keyword(s):  
Intrusion Detection ◽  
Large Scale ◽  
Feature Space ◽  
Training Model ◽  
Classification Performance ◽  
Gaussian Kernel ◽  
Kernel Approximation ◽  
Proposed Model ◽  
Joint Training ◽  
Linear Svm

Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the “curse of dimensionality” in a large-scale, high-dimensional feature space. This study proposes a joint training model that combines a stacked autoencoder (SAE) with an SVM and the kernel approximation technique. The training model uses the SAE to perform feature dimension reduction, uses random Fourier features to perform kernel approximation, and then random Fourier mapping is explicitly applied to the sub-sample to generate the random feature space, making it possible to apply a linear SVM to uniformly approximate to the Gaussian kernel SVM. Finally, the SAE performs joint training with the efficient linear SVM. We studied the effects of an SAE structure and a random Fourier feature on classification performance, and compared that performance with that of other training models, including some without kernel approximation. At the same time, we compare the accuracy of the proposed model with that of other models, which include basic machine learning models and the state-of-the-art models in other literatures. The experimental results demonstrate that the proposed model outperforms the previously proposed methods in terms of classification performance and also reduces the training time. Our model is feasible and works efficiently on large-scale datasets.

Glaucoma Assessment from Fundus Images with Fundus to OCT Feature Space Mapping

2022 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Divya Jyothi Gaddipati ◽  
Jayanthi Sivaswamy
Keyword(s):  
Large Scale ◽  
Feature Space ◽  
Space Mapping ◽  
Disease Assessment ◽  
Fundus Images ◽  
Irreversible Loss ◽  
Glaucoma Detection ◽  
Proposed Model ◽  
Attractive Option ◽  
Private Datasets

Early detection and treatment of glaucoma is of interest as it is a chronic eye disease leading to an irreversible loss of vision. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and cost-effectiveness. Optical Coherence Tomographic ( OCT ) images provide vital and unambiguous information about nerve fiber loss and optic cup morphology, which are essential for disease assessment. However, the high cost of OCT is a deterrent for deployment in screening at large scale. In this article, we present a novel CAD solution wherein both OCT and fundus modality images are leveraged to learn a model that can perform a mapping of fundus to OCT feature space. We show how this model can be subsequently used to detect glaucoma given an image from only one modality (fundus). The proposed model has been validated extensively on four public andtwo private datasets. It attained an AUC/Sensitivity value of 0.9429/0.9044 on a diverse set of 568 images, which is superior to the figures obtained by a model that is trained only on fundus features. Cross-validation was also done on nearly 1,600 images drawn from a private (OD-centric) and a public (macula-centric) dataset and the proposed model was found to outperform the state-of-the-art method by 8% (public) to 18% (private). Thus, we conclude that fundus to OCT feature space mapping is an attractive option for glaucoma detection.

Artificial Neural Network Based Intrusion Detection Method Combined with Manifold Learning Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 3170-3174
Author(s):  
Jin Guang Chen ◽  
Zhi Xiong Li
Keyword(s):  
Intrusion Detection ◽  
Manifold Learning ◽  
Detection Rate ◽  
Large Scale ◽  
Learning Algorithm ◽  
Feature Space ◽  
Locally Linear Embedding ◽  
Ann Model ◽  
Network Intrusion ◽  
Artificial Neural

Computer and network security is one of the most emergency issues for a large scale of applications. The unexpected intrusion may make terrible disaster to the network users. It is therefore imperative to detect the network attacks to prevent this kind of violations. The intrusion patter recognition is now a hot topic in this research area. The use of the artificial neural networks (ANN) can provide intelligent intrusion detection. However, the intrusion detection rate is often affected by the input feature vector of the ANN. This is because the original feature space always contains a certain number of useless features. To overcome this problem, a new network intrusion detection approach based on manifold learning nonlinear feature dimension descending and ANN classifier is presented in this paper. The locally linear embedding (LLE) algorithm was used to reduce the original intrusion feature space. Then the satisfactory ANN model with proper input features was obtained. The efficiency of the proposed method was evaluated with the real intrusion data. The analysis results show that the proposed approach has good intrusion detection rate, and performs better than the standard GA-ANN method.

scholarly journals A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

2017 ◽  
Vol 12 (5) ◽  
pp. 677 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Amin Namadchian
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approach ◽  
Detection Systems ◽  
Video Mining ◽  
Proposed Model ◽  
Evaluation Algorithm

A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

New Fuzzy Support Vector Machine Method Based on Entropy and Ant-Colony Optimization

2013 ◽  
Vol 380-384 ◽  
pp. 1580-1584
Author(s):  
Hao Guang Chen ◽  
Xiao Xi Li ◽  
Da Xi Li
Keyword(s):  
Large Scale ◽  
Feature Space ◽  
Classification Performance ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Paper Making ◽  
Large Scale Data ◽  
Vector Machines

Concerning the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current Fuzzy Support Vector Machines (FSVM), a new FSVM based on entropy and Genetic Algorithm (GA) named EGFSVM was proposed in this paper. Making use of evaluation of entropy and intelligence of GA, EGFSVM enhances the classification capability and makes clustering center more suitable and membership more accurate. Experimental results show EGFSVM has better precision and classification performance, especially to multi-class and large scale data.

scholarly journals Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks

IoT ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 428-448
Author(s):  
Imtiaz Ullah ◽  
Ayaz Ullah ◽  
Mazhar Sajjad
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Large Scale ◽  
Classification Model ◽  
Daily Lives ◽  
Network Intrusion ◽  
Proposed Model ◽  
High Level ◽  
Gated Recurrent Unit ◽  
Deep Learning Model

The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score.

MKEL: Multiple Kernel Ensemble Learning via Unified Ensemble Loss for Image Classification

2021 ◽  
Vol 12 (4) ◽  
pp. 1-21
Author(s):  
Xiangjun Shen ◽  
Kou Lu ◽  
Sumet Mehta ◽  
Jianming Zhang ◽  
Weifeng Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Hilbert Spaces ◽  
Large Scale ◽  
Reproducing Kernel ◽  
Multiple Kernel Learning ◽  
Feature Space ◽  
Classification Performance ◽  
Experimental Results ◽  
Multiple Kernel ◽  
Kernel Hilbert Spaces

In this article, a novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss. Different from the previous multiple kernel learning (MKL) methods, which attempt to seek a linear combination of basis kernels as a unified kernel, our MKEL model aims to find multiple solutions in corresponding Reproducing Kernel Hilbert Spaces (RKHSs) simultaneously. To achieve this goal, multiple individual kernel losses are integrated into a unified ensemble loss. Therefore, each model can co-optimize to learn its optimal parameters by minimizing a unified ensemble loss in multiple RKHSs. Furthermore, we apply our proposed ensemble loss into the deep network paradigm and take the sub-network as a kernel mapping from the original input space into a feature space, named Deep-MKEL (D-MKEL). Our D-MKEL model can utilize the diversified deep individual sub-networks into a whole unified network to improve the classification performance. With this unified loss design, our D-MKEL model can make our network much wider than other traditional deep kernel networks and more parameters are learned and optimized. Experimental results on several mediate UCI classification and computer vision datasets demonstrate that our MKEL model can achieve the best classification performance among comparative MKL methods, such as Simple MKL, GMKL, Spicy MKL, and Matrix-Regularized MKL. On the contrary, experimental results on large-scale CIFAR-10 and SVHN datasets concretely show the advantages and potentialities of the proposed D-MKEL approach compared to state-of-the-art deep kernel methods.

An Immune Intrusion Detection Model Based on One-Class Classification Technology

2012 ◽  
Vol 457-458 ◽  
pp. 979-984
Author(s):  
Yan Zhang ◽  
Cai Ming Liu ◽  
Run Chen ◽  
Hong Ying Qin ◽  
Bin Li
Keyword(s):  
Intrusion Detection ◽  
Feature Space ◽  
Support Vector ◽  
Network Attack ◽  
Detection Model ◽  
Model Based ◽  
Proposed Model ◽  
Domain Description ◽  
The One ◽  
One Class Classification

An intrusion detection model based on biological immune principle and one-class classification technology is proposed. The one-class classification technology named support vector domain description (SVDD) is applied to the proposed model. Simple multi-dimension feature vectors of network packets are mapped into high dimension feature space. The description models of the antibody and the self set are constructed. The evolution process of antibodies is described with math language. The theoretical analysis shows that the proposed model can detect network attack effectively, and unknown network attacks can be detected.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Sign in / Sign up

Export Citation Format

Share Document