Due to increasing incidents of cyber-attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. However, most of the conducted studies rely on static and one-time dataset where all the changes monitored are based on the dataset used. As network behaviors and patterns change and intrusions evolve, thus it has very much become necessary to move away from static and one-time dataset toward more dynamically configurable classifiers. The Current researches show that different classifiers provide different results about the patterns to be classified. These different results combined together (aka ensemble) yields better performance than individual classifiers. In this paper we have used a hybrid ensemble intrusion detection system consisting of a Misuse Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage. The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy. Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model. The misuse detection model will detect only known attacks. In-order to detect unknown attacks, we have an anomaly detection model as the second stage. SVM has been used, since it's the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior. The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup '99, NSL-KDD and UNSW-NB15 datasets.

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<b>This research is based on developing a drones which have significant importance in the current market with many new inventions. In this paper; we tried to build a drone that is controlled by using a virtual assistant called Amazon Alexa. This drone will be using only the receiver other than the transmitter and also built-in services of amazon help us to create voice recognition understandable by the system. We have succeeded 70% of the work and achieved good results in connecting Amazon Alexa and Arduino Yun based drone.</b>

I am pleased to introduce the distinguished Associate Editor-in-Chiefs of the JETWI.

Sabah Mohammed

Editor-in-Chief

August 2009

As my four-year term as the founding editor of the Journal of Emerging Technologies in Web Intelligence (JETWI ISSN 1798-0461) ends in Feb 2013, it gives me great pleasure to introduce my longtime friend and colleague Dr. Simon Fong as the next Editor-in- Chief of JETWI.  Dr. Fong is a Professor of Computer and Information Science at the University of Macau and among the founding members of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Dr. Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng in Computer Systems degree (1993) and a PhD in Computer Science degree (1998).  Prior to joining the University of Macau, he worked as an Assistant Professor in the School of Computer Engineering, Nanyang Technological University, Singapore. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Melbourne, Hong Kong and Singapore. Some companies that he worked before include Hong Kong Telecom, Singapore Network Services, AES Pro-Data and United Oversea Bank, Singapore.

Throughout his 16 year research history, Dr. Fong has managed to accomplish astounding results. You can actually feel it, if you follow the range of publications and the focused research goals that he outlined during academic service. His first important research mission started after he gained his PhD during 1998 where his research focuses was on Analytical Modeling of Computer Networks and in particular addressing the QoS challenge within distributed networking environments. Although this research area is still an active one, Dr. Fong started from the year 2000 till 2009 to project his distributed networking research experience on areas like social networking, e-commerce and business intelligence. His success in this direction attracted many large grants (external and internal) with large number of graduate students and associates. His contributions in this direction were more than notable especially in vital areas related to Agent-oriented Systems, Knowledge Oriented Negotiation in B2B/E-Commerce Systems and Recommenders. However, Dr. Fong research from 2010 started to be more focused on Data Mining and Web Intelligence especially on analyzing data on fly while such data are streamed to ubiquitous and mobile devices. In this direction he managed to be unique and take strong research leadership in providing effective analytical models for stream based data mining (e.g. OVFDT and iOVFDT).

Dr. Fong has extensive publications as well as editorial experience in the field of Web Intelligence and Data Mining. Her served as JETWI editor since 2009 and acted as a guest editor for two JETWI special issues. He is the EiC of the International Journal on Data Mining and Intelligent Technology Applications (IJMIA) and also on the editorial board of several other notable journals like (KIPS, IARI...