Solar Engineering Of Thermal Processes

C4.5: Programs for Machine Learning (書評)

As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Deep learning based ensemble approach for probabilistic wind power forecasting

With the continuous increase of wind power penetration in power systems, the problems caused by the volatile nature of wind speed and its occurrence in the system operations such as scheduling and dispatching have drawn attention of system operators, utilities and researchers towards the state-of-the-art wind speed and power forecasting methods These methods have the required capability of reducing the influence of the intermittent wind power on system operations as well as of harvesting the wind energy effectively In this context, combining different methodologies in order to circumvent the challenging model selection and take advantage of the unique strength of plausible models have recently emerged as a promising research area Therefore, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance Aiming to fill the mentioned research gap, this paper outlines the combined forecasting approaches and presents an up-to date annotated bibliography of the wind forecasting literature Furthermore, the paper also points out the possible further research directions of combined techniques so as to help the researchers in the field develop more effective wind speed and power forecasting methods

A review of combined approaches for prediction of short-term wind speed and power

The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

https://file.scirp.org/pdf/JSIP_2013101711003963.pdf

An overview of principal component analysis

Authentication in mobile cloud computing

With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

https://www.mdpi.com/2079-9292/9/7/1120/pdf

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

Among the diverse forms of malware, Botnet is the most widespread and serious threat which occurs commonly in today's cyber attacks. Botnets are collections of compromised computers which are remotely controlled by its originator (BotMaster) under a common Commond-and-Control (C&C) infrastructure. They provide a distributed platform for several illegal activities such as launching distributed denial of service (DDOS) attacks against critical targets, malware distribution, phishing, and click fraud. Most of the existing Botnet detection approaches concentrate only on particular Botnet command and control (C&C) protocols (e.g., IRC, HTTP) and structures (e.g., centralized), and can become ineffective as Botnets change their structure and C&C techniques. The detection of Botnet has been a major research topic in recent years. Different techniques and approaches have been proposed for detection and tracking of Botnet. This survey classifies Botnet detection techniques into two approaches. One approach is based on setting up honeynets and another approach is based on Intrusion Detection System( IDS) which has been categorized into signature-based and anomaly-based detection techniques.

A taxonomy of Botnet detection techniques

Steganography is a form of security technique through obscurity, the science and art of hiding the existence of a message between sender and intended recipient. Steganography has been used to hide secret messages in various types of files, including digital images, audio and video. The three most important parameters for audio steganography are imperceptibility, payload, and robustness. Different applications have different requirements of the steganography technique used. This paper intends to give an overview of image steganography, its uses and techniques.

Mazdak Zamani

Papers

An overview of principal component analysis

Authentication in mobile cloud computing

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

A taxonomy of Botnet detection techniques

An Introduction to Image Steganography Techniques