Hae-Duck Joshua Jeong

Comparison of various estimators in simulated FGN

Abstract The Hurst parameter is the simplest numerical characteristic of self-similar long-range dependent stochastic processes. Such processes have been identified in many natural and man-made systems. In particular, since they were discovered in the Internet and other multimedia telecommunication networks a decade ago, they have been the subject of numerous investigations. Typical quantitative assessment of self-similarity and long-range dependency, begins with the estimation of the Hurst parameter H. There have been a number of techniques proposed for this. This paper reports results of a comparative analysis of the six most frequently used estimators of H. To set up a credible framework for this, the minimal acceptable sample size is first determined. The Hurst parameter estimators are then compared for bias and variance. Our experimental results have confirmed that the Abry–Veitch Daubechies Wavelet-Based (DWB) and the Whittle ML (Maximum Likelihood) estimators of H are the least biased. However, the latter has significantly smaller variance and can be applied to shorter data samples than the Abry–Veitch DWB estimator. On the other hand, the Abry–Veitch DWB estimator is computationally simpler and faster than the Whittle ML estimator.

Anomaly Teletraffic Intrusion Detection Systems on Hadoop-Based Platforms: A Survey of Some Problems and Solutions

Telecommunication networks are getting more important in our social lives because many people want to share their information and ideas. Thanks to the rapid development of the Internet and ubiquitous technologies including mobile devices such as smart phones, mobile phones and tablet PCs, the quality of our lives has been greatly influenced and rapidly changed in recent years. Internet users have exponentially increased as well. Meanwhile, the explosive growth of teletraffic called big data for user services threatens the current networks, and we face menaces from various kinds of intrusive incidents through the Internet. A variety of network attacks on network resources have continuously caused serious damage. Thus, active and advanced technologies for early detecting of anomaly teletraffic on Hadoop-based platforms are required. In this paper, a survey of some problems and technical solutions for anomaly teletraffic intrusion detection systems based on the open-source software platform Hadoop has been investigated and proposed.

ATMSim: An anomaly teletraffic detection measurement analysis simulator

Over the last few years, the quantity of teletraffic is rapidly growing because of the explosive increase of Internet users and its applications. The needs of collection, storage, management, analysis, and measurement of the subsequent teletraffic have emerged as some of the very important issues. To this point many studies for detecting anomaly teletraffic have been done. Detection, measurement, and analysis studies for traffic data, however, are not actively being made based on Hadoop. In this paper, some problems and solutions for those systems have been suggested. We have also designed and developed an Anomaly Teletraffic detection Measurement analysis Simulator, called the ATMSim. One strong point of the ATMSim is able to store, measure, and analyze traffic data for detecting anomaly teletraffic. The other strength is to generate sequences of input synthetic anomaly teletraffic with various network attacks for practical network security applications. All simulations were executed under the control of the ATMSim simulator to investigate how input anomaly teletraffic with network attacks can be different from real Ethernet local area network (LAN) traffic. Our numerical results show that the values of the estimated Hurst parameter obtained from the anomaly teletraffic are much higher when compared to real Ethernet LAN traffic.

Distributed steady-state simulation of telecommunication networks with self-similar teletraffic

Abstract Recent measurement studies of teletraffic data in modern telecommunication networks have shown that self-similar processes may provide better models of teletraffic than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. We show how arrival processes with self-similar input influences the run-length of a distributed steady-state simulation of queueing systems in telecommunication networks. For this purpose, the simulation run-length of SSM/M/1/∞ queueing systems in the method based on the batch means, conducted for estimating steady-state mean waiting times is compared with the results obtained from simulations of M/M/1/∞ queueing systems when a single processor and multiple processors are used. We also investigate speedup conducted stochastic simulation of SSM/M/1/∞ queueing systems on multiple processors under a scenario of distributed stochastic simulation known as MRIP (Multiple Replications In Parallel) in a local area network (LAN) environment on Solaris operating system. We show that, assuming self-similar inter-event processes (i.e., SSM/M/1/∞ queueing systems), many more observations are required to obtain the final simulation results with a required precision, as the value of the Hurst parameter H increases, than when assuming Poisson models, exhibiting short-range dependence (i.e., M/M/1/∞ queueing systems) on a single processor and multiple processors. Our results show that the time for collecting many numbers of observations under the MRIP scenario is clearly reduced as traffic intensity and the value of the Hurst parameter increase, and as the engaged processor increases one to four. In particular, the value of H influences much more the speedup than traffic intensity and the engaged processor.

Self-Similar Properties of Spam

We often receive unwanted information from a variety of electronic systems mainly through emails, electronic boards and messengers, called spam. Spam is the use of electronic messaging systems to send unsolicited bulk messages indiscriminately. Widely varying estimates of the cost associated with spam are available in the literature. However, a stochastic and quantitative analysis of the determinant characteristics of spam traffic is still an open problem. This work fills this gap. A 4-year data sample of real-time inbound traffic between May 2005 and July 2009 was collected to investigate and analyze characteristics of spam traffic through JIRANSOFTs Spam Sniper on the network at Korean Bible University. Our major findings of a statistical analysis of spam traffic are that (i) real-time inbound spam traffic is statistically more correlated (self-similar) when compared to normal traffic, and (ii) the degree of self-similarity measured in terms of the Hurst parameter H and obtained from different estimation techniques is very high.

Generation of self-similar processes for simulation studies of telecommunication networks

It is generally accepted that self-similar processes may provide better models for teletraffic in modern telecommunication networks than Poisson processes. If stochastic self-similarity of teletraffic is not taken into account, it can lead to inaccurate conclusions about the performance of networks. Thus, an important requirement for conducting simulation studies of networks is the ability to generate long synthetic self-similar sequences of incremental processes, to transform them into interevent time intervals, and to do this accurately and quickly. A fast generator for count processes based on wavelets is described. Then a method for transformation of count processes into interevent processes proposed by Leroux and Hassan [1] and an alternative method, that is, inverting the empirical distribution directly, are studied. A case study is discussed to show how long sequences are needed in the steady-state simulation of queueing models with self-similar input processes. This is compared with simulation run lengths of the same queueing models fed by Poisson processes.

A Remote Computer Control System Using Speech Recognition Technologies of Mobile Devices

This paper presents a remote control computer system using speech recognition technologies of mobile devices for the blind and physically disabled population. These people experience difficulty and inconvenience using computers through a keyboard and/or mouse. The purpose of this system is to provide a way that the blind and physically disabled population can easily control many functions of a computer via speech. The configuration of the system consists of a mobile device such as a smartphone, a PC server, and a Google server that are connected to each other. Users can command a mobile device to do something via speech such as directly controlling computers, writing emails and documents, calculating numbers, checking the weather forecast, and managing a schedule. They are then immediately executed. The proposed system also provides the blind people with a function via TTS (text to speech) of the Google server if they want to receive contents of the document stored in a computer.

Detecting Anomaly Teletraffic Using Stochastic Self-Similarity Based on Hadoop

In recent years, the quantity of teletraffic is rapidly growing because of the explosive increase of Internet users and its applications. The needs of collection, storage, management, analysis, and measurement of the subsequent teletraffic have been emerged as one of very important issues. So far many studies for detecting anomaly teletraffic have been done. However, measurement and analysis studies for big data in cloud computing environments are not actively being made based on Hadoop. Thus, this paper presents for detecting anomaly teletraffic using stochastic self-similarity based on Hadoop. All simulations are conducted under control of our proposed platform, called ATM tool, for anomaly teletraffic intrusion detection system on Hadoop. Our numerical results show that the values of the estimated Hurst parameter obtained from the anomaly teletraffic are much higher when compared to ordinary local area network traffic.

Design and Implementation of Location-Based SNS Smartphone Application for the Disabled Population

The disabled have settled down as smart phone users in this age as users of these phones have exponentially increased in recent years. The theme of this paper is how to create a better world using the information that people want to exchange with each other between the disabled and the general population. On the other hand, the main goal is to provide the information that they need from each other which can be displayed on the map in real-time. We propose a new location-based SNS application for the physically disabled population having three major characteristics of this application to be considered as follows: One uses Social Networking Service (SNS) by constructing a friend matching system such as Face book and Twitter, which are the most widely-used SNS in the world, the general population registers real-time information of a specific location on the map for the physically disabled population using SNS. This information with photos and messages is given and evaluated by users, and this system makes it easier to see that the menu in the GUI was implemented.

Suggestions of efficient self-similar generators

The growth of Grid computing and the Internet has been exponential in recent years. These high-speed communication networks have had a tremendous impact on our civilisation. High-speed communication networks offer a wide range of applications, such as multimedia and data intensive applications, which differ significantly in their traffic characteristics and performance requirements. Many analytical studies have shown that self-similar network traffic can have a detrimental impact on network performance, including amplified queueing delays and packet loss rates in broadband wide area networks. Thus, full understanding of the self-similar nature in teletraffic engineering is an important issue. This paper presents a detailed survey of self-similar generators proposed for generating sequential and fixed-length selfsimilar pseudo-random sequences for simulation in communication networks. We evaluate and compare the operational properties of the fixed-length and sequential generators of self-similar pseudo-random sequences. The statistical accuracy and time required to produce long sequences are discussed theoretically and studied experimentally. The evaluation of the generators concentrated on two aspects: (i) how accurately self-similar processes can be generated (assuming a given mean, variance and self-similarity parameter H), and (ii) how quickly the generators can generate long self-similar sequences. Overall, our results have revealed that the fastest and most accurate generators of the six sequential and five fixed-length sequence generators considered are the SRP-FGN, FFT and FGN-DW methods. � 2006 Elsevier B.V. All rights reserved.