DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure

Performance Evaluation of Apache Spark MLlib Algorithms on an Intrusion Detection Dataset

The increase in the use of the Internet and web services and the advent of the fifth generation of cellular network technology (5G) along with ever-growing Internet of Things (IoT) data traffic will grow global internet usage. To ensure the security of future networks, machine learning-based intrusion detection and prevention systems (IDPS) must be implemented to detect new attacks, and big data parallel processing tools can be used to handle a huge collection of training data in these systems. In this paper Apache Spark, a general-purpose and fast cluster computing platform is used for processing and training a large volume of network traffic feature data. In this work, the most important features of the CSE-CIC-IDS2018 dataset are used for constructing machine learning models and then the most popular machine learning approaches, namely Logistic Regression, Support Vector Machine (SVM), three different Decision Tree Classifiers, and Naive Bayes algorithm are used to train the model using up to eight number of worker nodes. Our Spark cluster contains seven machines acting as worker nodes and one machine is configured as both a master and a worker. We use the CSE-CIC-IDS2018 dataset to evaluate the overall performance of these algorithms on Botnet attacks and distributed hyperparameter tuning is used to find the best single decision tree parameters. We have achieved up to 100% accuracy using selected features by the learning method in our experimentsComment: Journal of Computing and Security (Isfahan University, Iran), Vol. 9, No.1, 202

Editorial

Dear Reader,You are about to start the first number of volume 9 of Crime, Histoire & Sociétés/ Crime, History & Societies. We hope that you will find it as interesting as you appear to have found its predecessors. 2005 marks an important turning point for the journal since, in future, it will be available also in an electronic version. Henceforth, for a small additional payment, our subscribers will be able to have Internet access to the entire run of the journal and to benefit from the resea..

Editorial

Dear Reader,You are about to start the first number of volume 9 of Crime, Histoire & Sociétés/ Crime, History & Societies. We hope that you will find it as interesting as you appear to have found its predecessors. 2005 marks an important turning point for the journal since, in future, it will be available also in an electronic version. Henceforth, for a small additional payment, our subscribers will be able to have Internet access to the entire run of the journal and to benefit from the resea..

Supply chain network considerations for e-retail of luxury goods in Canada

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 85-89).The Internet has changed the way people purchase goods in the 21st century: many types of goods and services have become available online. Luxury goods followed this trend after an initial delay, primarily due to the nature of these goods. At the time of the preparation of this document, there were no proven guidelines for building the most successful e-retail website for luxury goods from the brand management and profitability perspectives. Ralph Lauren (RL) is an established American brand, well known for quality and consistent style in the following categories: clothing for women, men, and children; home goods, accessories; and fragrances. RL Corporation houses many labels that constitute premium and luxury offerings. RL currently sells through the Internet in many countries, in addition to countless company owned stores, 9 flagship stores, department stores and boutiques distributed around the world. To continue growth, RL wants to launch an e-retail website for Canada. This thesis aims to provide supply chain network considerations for the successful operation of the Canadian e-retail website for RL. These considerations stem from a careful look into potential luxury website characteristics that would meet the company objective of elevating the brand towards the luxury category. It is recommended that RL secure expansion capacity that will likely be necessary for B2C operation at its Toronto distribution center (DC). In addition, material handling equipment that will process a high volume of small orders should be placed in this DC. The Vancouver cross-docking facility could be expanded in the future as prompted by sales volume and coupled with a DC to cater to the West Coast of Canada. Also, it is recommended that advanced customer tracking systems and databases be employed, especially to determine high value customers for tailored offerings in the luxury segment.by Dilek Tansoy and Yi Linn Teo.M.Eng.in Logistic

Approaches for Future Internet architecture design and Quality of Experience (QoE) Control

Researching a Future Internet capable of overcoming the current Internet limitations is a strategic investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow applications to transparently, efficiently and flexibly exploit the available network resources with the aim to match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision problem

The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: the development of the IGD-20 Test

Background: Over the last decade, there has been growing concern about ‘gaming addiction’ and its widely documented detrimental impacts on a minority of individuals that play excessively. The latest (fifth) edition of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included nine criteria for the potential diagnosis of Internet Gaming Disorder (IGD) and noted that it was a condition that warranted further empirical study. Aim: The main aim of this study was to develop a valid and reliable standardised psychometrically robust tool in addition to providing empirically supported cut-off points. Methods: A sample of 1003 gamers (85.2% males; mean age 26 years) from 57 different countries were recruited via online gaming forums. Validity was assessed by confirmatory factor analysis (CFA), criterion-related validity, and concurrent validity. Latent profile analysis was also carried to distinguish disordered gamers from non-disordered gamers. Sensitivity and specificity analyses were performed to determine an empirical cut-off for the test. Results: The CFA confirmed the viability of IGD-20 Test with a six-factor structure (salience, mood modification, tolerance, withdrawal, conflict and relapse) for the assessment of IGD according to the nine criteria from DSM-5. The IGD-20 Test proved to be valid and reliable. According to the latent profile analysis, 5.3% of the total participants were classed as disordered gamers. Additionally, an optimal empirical cut-off of 71 points (out of 100) seemed to be adequate according to the sensitivity and specificity analyses carried

Superprocesses as models for information dissemination in the Future Internet

Future Internet will be composed by a tremendous number of potentially interconnected people and devices, offering a variety of services, applications and communication opportunities. In particular, short-range wireless communications, which are available on almost all portable devices, will enable the formation of the largest cloud of interconnected, smart computing devices mankind has ever dreamed about: the Proximate Internet. In this paper, we consider superprocesses, more specifically super Brownian motion, as a suitable mathematical model to analyse a basic problem of information dissemination arising in the context of Proximate Internet. The proposed model provides a promising analytical framework to both study theoretical properties related to the information dissemination process and to devise efficient and reliable simulation schemes for very large systems