Security of data is considered to be one of the most important concerns in today’s world. Data is vulnerable to various types of intrusion attacks that may reduce the utility of any network or systems. Constantly changing and the complicated nature of intrusion activities on computer networks cannot be dealt with IDSs that are currently operational. Identifying and preventing such attacks is one of the most challenging tasks. Deep Learning is one of the most effective machine learning techniques which is getting popular recently. This paper checks the potential capability of Deep Neural Network as a classifier for the different types of intrusion attacks. A comparative study has also been carried out with Support Vector Machine (SVM). The experimental results show that the accuracy of intrusion detection using Deep Neural Network is satisfactory. © Springer Nature Singapore Pte Ltd. 2017.

Sanjiban Sekhar Roy

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Mallik A

Gulati R

Obaidat M.S

Krishna P.V.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection

Communications in Computer and Information Science Mathematics and Computing

The precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end-to-end learning framework. © 2019 John Wiley &amp; Sons, Ltd.

International Journal of Communication Systems

Estimating rainfall using machine learning strategies based on weather radar data

This paper presents an image classification model using a convolutional neural network with Tensor Flow. Tensor Flow is a popular open source library for machine learning and deep neural networks. A multi-category image dataset has been considered for the classification. Conventional back propagation neural network has an input layer, hidden layer, and an output layer but convolutional neural network, has a convolutional layer, and a max pooling layer. We train this proposed classifier to calculate the decision boundary of the image dataset. The data in the real world is mostly in the form of unlabeled and unstructured format. These unstructured data may be image, sound and text data. Useful information cannot be easily derived from neural networks which are shallow i.e. the ones which have less number of hidden layers. We propose deep neural network based CNN classifier which has a large number of hidden layers and can derive meaningful information from images. © 2017 IEEE.

2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

Classifying multi-category images using deep learning : A convolutional neural network model

Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

International Journal of Engineering Research in Africa

Classifying Spam Emails Using Artificial Intelligent Techniques

After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network (ANN) method named self organizing maps (SOM) with a supervised classifier called stochastic gradient descent (SGD). Also a comparative analysis is performed between the proposed approach and three supervised state of the art machine learning techniques decision tree (DTs), random forests (RF) and support vector machine (SVM). Initially SGD method is used in isolation for the classification task and then it is made to perform the classification after being hybridized with the unsupervised ANN technique on Wisconsin Breast Cancer Database (WBCD) [10]. The comparison is based up on classification accuracy that is produced by generating a confusion matrix. For verifying consistency of accuracy values, the classification task was repeated with Internet Advertisements Dataset [11]. The results of the classification experimentation using hybridization of SOM with SGD are much more superior to SGD in isolation. All the accuracy values have been computed after achieving a ten-fold cross validation on the both the datasets to further verify the classifier's performance. © 2015 IEEE.

2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

An effective hybridized classifier for breast cancer diagnosis

The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being a large margin classifier is a powerful pattern recognition and machine learning methodology that is widely used for both linear and non-linear classification problems. Comparing testing on different kernel methods, we have noticed that our method gives the better accuracy. Choosing the optimal value of the parameters is a crucial criterion and this was achieved by performing 3 fold cross-validations. © 2015 IEEE.

2015 International Conference on Computing Communication Control and Automation

A Novel Diagnostic Approach Based on Support Vector Machine with Linear Kernel for Classifying the Erythemato-Squamous Disease

Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series' exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model. © Springer International Publishing Switzerland 2015.

Journal	Data powered by TypesetCommunications in Computer and Information Science Mathematics and Computing
Publisher	Data powered by TypesetSpringer Singapore
ISSN	1865-0929
Open Access	0