Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

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Index split decision tree and compositional deep neural network for text categorization

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.9953 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 449

Author(s):

N Ravikumar ◽

Dr P. Tamil Selvan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Decision Tree ◽

Text Categorization ◽

Deep Neural Network ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Decision Tree Model ◽

Tree Model ◽

The Impact

Text categorization with machine learning algorithms generally reckons to possess horizontal set of classes. Several advanced machine learning algorithms have been designed in the past few decades. With the growing research work for text categorization, it has become important to categorize the research outcome and provide the learners with an effective machine learning method, a framework called, Hierarchical Decision Tree and Deep Neural Network (HDT-DNN).It investigates machine learning algorithms to create horizontal set of classes and it is used for classification of text. With this objective, a novel and efficient text categorization framework based on decision tree model is used in order to categorize text according to superior and subordinate level. The text to be categorized is presented in the form of a tree with parent text category being superior to all. The intermediate level represents the text that is both superior and subordinate. Then Deep Neural Network model is presented initiating compositional model, where the text has to be categorized, as a layered integration of primitives from the constructed decision tree model. The extra layers enable composition of features from lower layers, potentially modeling complex text with fewer units than a similarly carried out shallow network producing hierarchical classification. The significance of the impact of HDT-DNN framework is evaluated through empirical study. Extensive experiments are carried out and the performance of HDT-DNN framework is evaluated and compared with existing state-of-art methods using parameters such as precision, classification accuracy, classification time, with respect to varied number of features and document size.

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A Prototype to Detect Anomalies Using Machine Learning Algorithms and Deep Neural Network

Computational Vision and Bio Inspired Computing - Lecture Notes in Computational Vision and Biomechanics ◽

10.1007/978-3-319-71767-8_93 ◽

2018 ◽

pp. 1084-1094 ◽

Cited By ~ 1

Author(s):

Malathi A. ◽

Amudha J. ◽

Puneeth Narayana

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Neural Network ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Privacy-preserving Machine Learning as a Service

Proceedings on Privacy Enhancing Technologies ◽

10.1515/popets-2018-0024 ◽

2018 ◽

Vol 2018 (3) ◽

pp. 123-142 ◽

Cited By ~ 29

Author(s):

Ehsan Hesamifard ◽

Hassan Takabi ◽

Mehdi Ghasemi ◽

Rebecca N. Wright

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Neural Network ◽

Learning Algorithms ◽

Privacy Preserving ◽

Machine Learning Algorithms ◽

Cloud Services ◽

Network Algorithms ◽

Encrypted Data

Abstract Machine learning algorithms based on deep Neural Networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers’ infrastructure. However, machine learning algorithms require access to the raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we present CryptoDL, a framework that develops new techniques to provide solutions for applying deep neural network algorithms to encrypted data. In this paper, we provide the theoretical foundation for implementing deep neural network algorithms in encrypted domain and develop techniques to adopt neural networks within practical limitations of current homomorphic encryption schemes. We show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed CryptoDL using a large number of datasets and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.

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Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

JMIR Medical Informatics ◽

10.2196/17364 ◽

2020 ◽

Vol 8 (6) ◽

pp. e17364 ◽

Cited By ~ 2

Author(s):

Can Hou ◽

Xiaorong Zhong ◽

Ping He ◽

Bin Xu ◽

Sha Diao ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Deep Neural Network ◽

Chinese Women ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Age At First Birth ◽

Cancer Prediction

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

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Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development (Preprint)

10.2196/preprints.17364 ◽

2019 ◽

Author(s):

Can Hou ◽

Xiaorong Zhong ◽

Ping He ◽

Bin Xu ◽

Sha Diao ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Deep Neural Network ◽

Chinese Women ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Age At First Birth ◽

Cancer Prediction

BACKGROUND Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. OBJECTIVE This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. METHODS A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. RESULTS The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. CONCLUSIONS The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

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Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm

Jurnal Penelitian Pos dan informatika ◽

10.17933/jppi.v11i1.341 ◽

2021 ◽

Vol 11 (1) ◽

pp. 89-100

Author(s):

Cucu Ika Agustyaningrum ◽

Muhammad Haris ◽

Riska Aryanti ◽

Titik Misriati

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Neural Network ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Market Potential ◽

Continuous Increase ◽

Network Algorithm ◽

Conventional Machine ◽

Neural Network Algorithm

The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.

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EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2021.01.pp.044-051 ◽

2021 ◽

pp. 44-51

Author(s):

E. Yu. Shchetinin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Deep Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Audio Recordings ◽

Computer Studies

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

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Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Water ◽

10.3390/w12102927 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2927

Author(s):

Jiyeong Hong ◽

Seoro Lee ◽

Joo Hyun Bae ◽

Jimin Lee ◽

Woon Ji Park ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Multilayer Perceptron ◽

Short Term Memory ◽

Learning Algorithms ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Applied Sciences ◽

10.3390/app8081280 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1280 ◽

Cited By ~ 14

Author(s):

Yong Kim ◽

Youngdoo Son ◽

Wonjoon Kim ◽

Byungki Jin ◽

Myung Yun

Keyword(s):

Neural Network ◽

Machine Learning ◽

Monitoring System ◽

Multinomial Logistic Regression ◽

Learning Algorithms ◽

Feedback System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Future Research ◽

Support Vector

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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