As novel technologies continue to reshape the digital era, cyberattacks are also increasingly becoming more commonplace and sophisticated. Distributed denial of service (DDoS) attacks are, perhaps, the most prevalent and exponentially-growing attack, targeting the varied and emerging computational network infrastructures across the globe. This necessitates the design of an efficient and early detection of large-scale sophisticated DDoS attacks. Software defined networks (SDN) point to a promising solution, as a network paradigm which decouples the centralized control intelligence from the forwarding logic. In this work, a deep convolutional neural network (CNN) ensemble framework for efficient DDoS attack detection in SDNs is proposed. The proposed framework is evaluated on a current state-of-the-art Flow-based dataset under established benchmarks. Improved accuracy is demonstrated against existing related detection approaches.

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A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks

우리나라의 流行性耳下腺炎에 關한 血淸學的 調査硏究 (1970)

In this work, we apply a two stage anomaly-based network intrusion detection process using the UNSW-NB15 dataset. We use Recursive Feature Elimination and Random Forests among other techniques to select the best dataset features for the purpose of machine learning; then we perform a binary classification in order to identify intrusive traffic from normal one, using a number of data mining techniques, including Logistic Regression, Gradient Boost Machine, and Support Vector Machine. Results of this first stage classification show that the use of Support Vector Machine reports the highest accuracy (82.11%). We then feed the output of Support Vector Machine to a range of multinomial classifiers in order to improve the accuracy of predicting the type of attacks. Specifically, we evaluate the performance of Decision Trees (C5.0), Naïve Bayes and multinomial Support Vector Machine. Applying C5.0 yielded the highest accuracy (74%) and F1 score (86%), and the two-stage hybrid classification improved the accuracy of results by up to 12% (achieving a multi-classification accuracy of 86.04%). Finally, with the support of our results, we present constructive criticism of the UNSW-NB15 dataset.

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Network Based Intrusion Detection Using the UNSW-NB15 Dataset

An artificial neural network method for lumen and media-adventitia border detection in IVUS.

Despite the extensive research of using web services for security purposes, there is a big challenge towards finding a no radical solution for NoSQL injection attack. This paper presents an independent RESTful web service in a layered approach to detect NoSQL injection attacks in web applications. The proposed method is named DNIARS. DNIARS depends on comparing the generated patterns from NoSQL statement structure in static code state and dynamic state. Accordingly, the DNIARS can respond to the web application with the possibility of NoSQL injection attack. The proposed DNIARS was implemented in PHP plain code and can be considered as an independent framework that has the ability for responding to different requests formats like JSON, XML. To evaluate its performance, DNIARS was tested using the most common testing tools for RESTful web service. According to the results, DNIARS can work in real environments where the error rate did not exceed 1%.

NoSQL Injection Attack Detection in Web Applications Using RESTful Service

The quantity of data transmitted in the network intensified rapidly with the increased dependency on social media applications, sensors for data acquisitions and smartphones utilizations. Typically, such data is unstructured and originates from multiple sources in different format. Consequently, the abstraction of data for rendering is difficult, that lead to the development of a computing system that is able to store data in unstructured format and support distributed parallel computing. To data, there exist approaches to handle big data using NoSQL. This paper provides a review and the comparison between NoSQL and Relational Database Management System (RDBMS). By reviewing each approach, the mechanics of NoSQL systems can be clearly distinguished from the RDBMS. Basically, such systems rely on multiple factors, that include the query language, architecture, data model and consumer API. This paper also defines the application that matches the system and subsequently able to accurately correlates to a specific NoSQL system.

Big Data: The NoSQL and RDBMS review

Rain fade slope has been measured in Kuala Lumpur, Malaysia, located in heavy rain zone. The data were collected from MEASAT3 with elevation angle of 77.4 $^{\circ}$ at a frequency of 10.982 GHz with several time interval lengths. The measured fade slope was compared to the ITU-R P.1623 model. Tendency of probability distribution model and measured data is discrepant, and leading estimation of fade slope is inaccurate. Dominant parameters to the model are analyzed and proposed for the link. Modification of fade slope model has been proposed based on data with different time interval lengths' effects.

Rain Fade Slope Model in Satellite Path Based on Data Measured in Heavy Rain Zone

Rain fade slope is one of fade dynamics behaviour used by system engineers to design fade mitigation techniques (FMT) for space-earth microwave links. Recent measurements found that fade slope prediction model proposed by ITU-R is unable to predict fade slope distribution accurately in tropical regions. Rain fade measurement was conducted  in Kuala Lumpur (3.3° N, 101.7° E) where located in heavy rain zone by receiving signal at 10.982 GHz (Ku-band) from MEASAT3 (91.5° E) on 77.4° elevation angle. The measurement has been carried out for one year period. Fade slope S parameter on ITU-R prediction model has been investigated. New parameter is proposed for the fade slope prediction modeling based on measured data at high elevation angle, Ku-band. ABSTRAK : Cerun hujan pudar adalah salah satu dinamik tingkah laku pudar yang digunakan oleh jurutera sistem untuk mereka bentuk teknik-teknik pengurangan pudar (FMT) bagi link gelombang mikro ruang bumi. Ukuran baru-baru ini mendapati bahawa cerun pudar ramalan model yang dicadangkan oleh ITU-R tidak mampu untuk meramalkan pengagihan cerun pudar tepat di kawasan tropika. Pengukuran  hujan pudar telah dijalankan di Kuala Lumpur (3.3° N, 101.7° E) yang terletak di kawasan hujan lebat dengan menerima isyarat pada 10,982 GHz (Ku-band) dari MEASAT3 (91.5° E) pada sudut ketinggian 77.4°. Pengukuran telah dijalankan untuk tempoh satu tahun. Parameter cerun pudar S pada model ramalan ITU-R telah disiasat. Parameter baru adalah dicadangkan untuk pemodelan cerun pudar ramalan berdasarkan data yang diukur pada sudut paras ketinggian, Ku-band. KEYWORDS: fade slope; ITU-R; fade mitigation techniques; sampling time interval

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Modification of ITU-R Rain Fade Slope Prediction Model Based on Satellite Data Measured at High Elevation Angle

Tropospheric scintillation prediction models for a high elevation angle based on measured data from a tropical region

In this paper we present an automated segmentation method to detect the boundary between adventitia and media on the cross sectional view of the artery of patients who have plaques. The problem encounter is that the boundaries of the adventitia, media, intima and lumen are embedded when plaques exist. Moreover, the artery disease has damaged the tissue layers. This paper proposed a method in segmenting and detecting the outer boundary which is the media adventitia area of the artery using intravascular ultrasound (IVUS) images. The proposed method for segmentation is to use Otsu thresholding, followed by empirical thresholding and binary - morphological operation. The data used in this study was 10 samples from dataset B of IVUS images, courtesy of Simone Balocco (Training set, Computer Vision Center, Bellaterra, Universitat de Barcelona, Dept. Matematica Aplicada i Analisi, Barcelona). The proposed method shows promising result in detecting and segmenting the media adventitia boundary of the IVUS images.

Hassan Dao

Papers

Big Data: The NoSQL and RDBMS review

Rain Fade Slope Model in Satellite Path Based on Data Measured in Heavy Rain Zone

Modification of ITU-R Rain Fade Slope Prediction Model Based on Satellite Data Measured at High Elevation Angle

Tropospheric scintillation prediction models for a high elevation angle based on measured data from a tropical region

Segmentation and detection of media adventitia coronary artery boundary in medical imaging intravascular ultrasound using otsu thresholding