Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Anomaly-based network intrusion detection methods

The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks

Analysis of intrusion detection system (IDS) in border gateway protocol

University of Technology, Sydney. Faculty of Engineering and Information Technology.Border Gateway Protocol (BGP) is the de-facto inter-domain routing protocol used across thousands of Autonomous Systems (AS) joined together in the Internet. The main purpose of BGP is to keep routing information up-to-date across the Autonomous System (AS) and provide a loop free path to the destination. Internet connectivity plays a vital role in organizations such as in businesses, universities and government organisations for exchanging information. This type of information is exchanged over the Internet in the form of packets, which contain the source and destination addresses. Because the Internet is a dynamic and sensitive system which changes continuously, it is therefore necessary to protect the system from intruders. Security has been a major issue for BGP. Nevertheless, BGP suffers from serious threats even today, DoS attack is the major security threat to the Internet today, among which, is the TCP SYN flooding, the most common type of attack. The aim of this DoS attack is to consume large amounts of bandwidth. Any system connected to the Internet and using TCP services are prone to such attacks. It is important to detect such malicious activities in a network, which could otherwise cause problems for the availability of services. This thesis proposes and implements two new security methods for the protection of BGP data plane, “Analysis of BGP Security Vulnerabilities” and “Border Gateway Protocol Anomaly Detection using Failure Quality Control Method” to detect the malicious packets and the anomaly packets in the network. The aim of this work is to combine the algorithms with the Network Data Mining (NDM) method to detect the malicious packets in the BGP network. Furthermore, these patterns can be used in the database as a signature to capture the incidents in the future

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

T2D2: A Time Series Tester, Transformer, and Decomposer Framework for Outlier Detection

The automatic detection of outliers in time series datasets has captured much amount of attention in the data science community. It is not a simple task as the data may have several perspectives, such as sessional, trendy, or a combination of the two. Furthermore, to obtain a reliable and untrustworthy knowledge from the data, the data itself should be understandable. To cope with these challenges, in this paper, we introduce a new framework that can first test the stationarity and seasonality of dataset, then apply a set of Fourier transforms to get the Fourier sample frequencies, which can be used as a support of a decomposer component. The proposed framework, namely TTDD (Test, Transform, Decompose, and Detection), implements the decomposer component that split the dataset into three parts: trend, seasonal, and residual. Finally, the frequency difference detector compares the frequency of the test set to the frequency of the training set determining the periods of discrepancy in the frequency considering them as outlier periods