Miracle-dz/UniFAR-FTUs huggingface dataset:
DDoS Detection in SDN Switches using Support Vector Machine Classifier

Keywords: SDN Switches, Distributed Denial-of-Service (DDoS), Support Vector Machine (SVM), Genetic Algorithm (GA) Abstract. Compared with traditional network, Software Defined network (SDN) technology contains data plane, control plane and application plane. The control plane centralized controls multiple switches instead of only one switch. Therefore, SDN has more security requirements. The existing network security equipment already can no longer adapt to the environment of SDN. Distributed Denial-of-Service Attacks (DDoS) is one of the most major threats. DDoS detection is necessary for SDN switches. Support vector machine (SVM) classification technology is widely used in various fields. In this article, we will detect DDoS attacks using SVM optimized parameter c and g with cross validation-genetic algorithm (CV-GA). The experiments show that CV-GA-SVM classification performs better than others. Intr oduction In recent years, Software defined network (SDN) as a new research highlight appears in the development of computer network (1). SDN was originated from the Clean Slate project at Stanford University in the United States. With further researches, SDN which gradually obtained the wide recognition of academia and industry, has become the mainstream direction of the Internet's development in the future. The network control plane is separated from the underlying network in SDN technologies. Instead of the traditional closed control plane, the open plane controls the entire network by the centralized controller, and allows a programmable network. SDN has good openness and flexibility to bring the huge change of network. According to the SDN's architecture which defined by Open Networking Foundation (ONF) (2), SDN is divided into the infrastructure layer, the control layer, the application layer, the north interface and the south interface which connect the layers of data exchange. Dist ributed Denial-of-Service (DDoS) is a destruction of the effectiveness of network service. It leads that a suffered host or network can't receive and deal with the request from outside world. So the host or network cannot provide normal service for a legitimate user. Thus the attack forms a denial of ser vice. Compared with the traditional network, SDN has more flexibility and controllability so that the SDN is more vulnerable to DDoS attacks (3). Therefore, the detection of DDoS attacks is one im portant research direction of SDN security. In this paper, compared with other existing methods, we first prove the superiority of SVM based on traffic flow for DDoS detection in SDN switches. Secondly, this paper proposes a parameter optimization for SVM classification based on traffic flow to improve the quality of detection. We come up with CV-GA (cross validation - genetic algorithm) with adjusting factor to optimize parameter. At last, we compare results with un-optimized SVM.
Structure, Properties, and Reactivity of Solid Fuels

Solid-fuel combustion is one of man's oldest technologies, but it continues to play an important societal role as we approach the third millennium. Various economic and environmental factors are leading to a diversification in fuels—the practitioner can no longer restrict his interest to the combustion behavior of one or several regional coals but must consider a wide variety of potential solid fuels, including biomass, solid wastes, and internationally traded coals. This paper begins with a brief discussion of the technological issues that define research needs in solid-fuel combustion and then presents a brief overview of the fuels themselves and their important fundamental properties. A central theme in the paper is the remarkable variation in the structure, reactivity, and properties of chars from the diverse array of practical solid fuels. The density, oxidation reactivity, and physical adsorption capacity are identified as key char properties, as they strongly influence the degree of carbon burnout in pulverized fuel combustion, the volume of the solid waste produced, and the behavior of the resulting fly ash in post-combustion utilization schemes, most notably concrete. Many char properties are shown to depend on char crystalline structure, or nanostructure, which varies from disordered to ordered as a function of precursor chemistry and char formation conditions. Another theme in the paper is the possibility of predicting combustion rates and burnout times of different solid fuels in a common combustion environment. The paper discusses predictive schemes, which are based on hybrid correlative and mechanistic approaches, in which intraparticle and boundary-layer transport processes are described mechanistically, while surface reactivity and particle density are obtained from empirical correlations with parent fuel properties. Although these complex solids are intrinsically more difficult to describe and model than are gaseous and liquid fuels, there is great potential for developing more fundamental descriptions of the char formation and reaction processes.
Download Process in Distributed Systems, Flow-level Algorithm vs. Packet-level Simulation Model

Parallelism in the download process of large files is an efficient mechanism for distributed systems. In such systems, some peers (clients) exploit the power of parallelism to download blocks of data stored in a distributed way over some other peers (servers). Determining response times in parallel downloading with capacity constraints on both the client downloads and server uploads necessitates understanding the instantaneous shares of the bandwidths of each client/server is devoted to each data transfer flow. In this report, we explore the practical relevance of the hypothesis that flows share the network bandwidth according to the max-min fairness paradigm. We have implemented into a flow-level simulator a version of the algorithm which calculates such a bandwidth allocation, which we have called the ''progressive-filling flow-level algorithm''. We have programmed a similar model over NS2 and compared the empirical distributions resulting from both simulations. Our results indicate that flow-level predictions are very accurate in symmetric networks and good in asymmetric networks. Therefore, PFFLA would be extremely useful to build flow-level simulators and, possibly, to perform probabilistic performance calculations in general P2P networks.
Using discrete event qualitative simulation as a knowledge acquisition tool

The automated acquisition of knowledge by machine has not lived up to expectations and knowledge engineering remains a human intensive task. Part of the reason for the lack of success is the difference in cognitive focus of the expert. The expert must shift their focus from the domain to that of the representation environment. In doing so, this shift of context introduces opportunity for errors and omissions. This paper presents work which provides the expert with a simulation domain in which they operate. The system then observes and logs the expert's actions during the performance of the task. This is followed by the application of inductive reasoning to move the domain-specific rules observed to general domain rules. <<ETX>>
Ontogenetic development of retinotopy and of ocular dominance columns

A mathematical model of the activity-dependent self-organizing mechanism is given for the unified description of the ontogenetic formation of ocularity domains and of the retinotopy. The model is also capable of describing the plasticity of ocular dominance columns. According to the model the connections between cells are not preprogrammed genetically; only a certain algorithm is given to select favorable connections. In accordance with K.D. Miller et al. (1989), the role of activities is emphasized. The model of Miller et al. is based on four biological features which are also explicitly incorporated into the authors' models, with a different technical realization. While Miller et al. set up a differential equation, the authors' model is described as a multistage algorithm. Another main difference is that the authors use a two-level neurodynamic model taking explicitly into consideration even the single neuron activity dynamics. <<ETX>>
Privacy Preserving File Accessing System Using Location Proof and System Proof

Today’s location-based services are mainly based on user’s mobile devices to determine the current location and send this location to the application. This may allows users to send false location and enable the user to access restricted resources. Here we present a privacy preserving file accessing system using location proof and system proof in which each mobile users having a mobile node such as a laptop can able to access files from a server. We also introduce system proof which is the IP and MAC address of the system to provide a high security application that allows confidential data access to its users. It can be implemented with Wi-Fi infrastructure and can be easily deployed with little computational cost.
Programmable High Speed 8-Bit Binary Incrementer Decrementer

: This paper presents the eight bit CMOS base incrementer and decrementer logic design using eight bit adder and subtractor. The parametric simulation is done on MICROWIND layout editor tool. The any conventional static CMOS adder with pullup and pulldown logic requires 32 MOSFET whereas our design adder requires 30 MOSFETs. Our design methodology is based on static CMOS logic and transmission gate logic to achieve smaller delays, reduce power dissipation and optimized area. We can use a high-speed parallel adder in incrementer / decrementer to improve the operating speed which can count up or count down from the loaded value by one step in one clock cycle.
A neural approach for determination of global energetic efficiency indicator in groundwater wells

In most of the cases, the systems of water distribution from groundwater wells use electrical submersible pumps. All electrical energy is applied to the pumps; however, other components (pipes, valves, etc.) of these systems are also responsible by the higher or lower consumption of electric energy. The supervisors and operators of the systems should thus have knowledge of the global energetic behavior of the process in order to administrate it properly. This work suggests a 'global energy efficiency indicator' for groundwater wells by using mathematical equations and neural networks. Simulation results are presented in order to demonstrate the validity of the proposed approach.
Flower Color Development and Nano-Science

Red, purple and blue flower color is mainly due to anthocyanins. The color development and stability of anthocyanin are effected by co-existence of co-pigment and metal ions, and pH. Our chemical studies elucidated that the color is stabilized and varied by formation of a supramolecule. Especially beautiful blue flower colors, found in blue dayflower and blue salvia, are developed by a metalloanthocyanin, a stoichiometric supramolecular metal complex pigment.The metalloanthocyanins are composed with six molecules of anthocyanins, six molecules of glycosylflavones, and 2 atoms of metal ion and the diameter is ca. 3 nano meter. Under formation of metalloanthocyanin fine chiral molecular recognition occurs. To clarify the mechanism we synthesized chiral glycosylflavones derivatives according to our newly invented method of direct C-and O-glycosylation to the flavonoid nucleus. Reproduction experiment from the components (anthocyanin, flavone and metal ion) including chiral isomer of the flavones bearing L-glucose was carried out. As a result, the 4'-D-glucoside of the flavone is essential for formation of metalloanthocyanin, but the L-sugar is not.As the petal tissue is not homogenous, both the epidermides are colored. For elucidation of true flower color development in a living petal cell, various analytical methodologies including nano-technology must be applied. For this purpose one cell analysis by pH-microelectrode, a nano-HPLC, a pico mole metalanalysis and microscopic photometry were carried out. The mech-anism of flower color change of morning glory, Ipomoea tricolor, from purple to blue during blooming, and color variation of hydrangea was studied.
Working with Classes and Interfaces

Dirk RiehleSKYVA Internationalwww.skyva. com, www.skyva. deriehle@acm.org, www.riehle. orgClasses are fundamental to object-oriented design and programming in C++. In this article, we take a look at fivefundamental patterns of designing and using classes. We use a story, the evolution of a seemingly simple class, toillustrate the Simple Class, Design by Primitives, Interface Class, Abstract Base Class, and Narrow InheritanceInterface patterns. This story and the ensuing discussion provide us with some insight on what makes up a patternand a good description thereof.
Image Deblocking Using Local Segmentation

Images compressed using lossy DCT-based compression schemes result in blocking artifacts being visible in the image, their presence more apparent the higher the compression ratio. This review analyses and evaluates the literature in the field of image deblocking, in order to try and determine what the “state of the art” method is. Image deblocking may be carried out in several ways, either by sending extra information from the encoder to the decoder so that a better image reconstruction can be made. This would not conform to the existing image compression standards (like JPEG). The other approach, the one explored in this review, is solely based on the decoder side and extracts additional information from an image by exploiting the empirical correlation property between neighbouring pixels, as well as between neighbouring DCT coefficients in the same subband (image described in terms of a single DCT basis function), as well as between different subbands. The review attempts to model the degradation of image quality in some way so it can be used in the reconstruction process. This review also attempts to discover the existence of a suitable blockiness measure which can be used to correctly analyse an image numerically, so that the result agrees with subjective analysis of an image by a person. Being able to subjectively analyse an image numerically allows deblocking methods to be accurately compared, thus allowing the “state of the art” method to be made self-evident.
Unified Development of Automatically Adapted Interactions

This Thesis introduces a new software interface engineering paradigm, called unified interface development, for constructing interactive software applications that can be automatically adapted to individual user attributes, as well as to the particular contexts of use. The design and implementation processes of the unified development approach are structured according to: • The unified interface design method, which drives the design of dialogues in which diverse userand usage-contextattribute values (i.e. design parameters) are to be addressed, and • The unified interface implementation method, which provides an architectural framework for “packaging” the implementation of automatically adapted interactions as independent co-operating software components. The technical profile of the development tools supporting unified development is provided as a list of seven fundamental functional requirements. The I-GET User Interface Management System (UIMS) is presented, developed specifically to support unified development, showing how those seven key requirements are practically met, by the supplied implementation mechanisms of the I-GET UIMS. Finally, existing User Interface development practices are studied and positioned with respect to the unified interface development strategy, and recommendations are given for future work which would promote the unified interface development paradigm.
Change Detection Algorithms for Information Assurance of Computer Networks

Abstract : As our reliance on computer networks grows, the need for better and more accurate intrusion detection systems to identify and contain attackers becomes a fundamental research topic. In this thesis, the author will focus on the detection of three attack scenarios: the spreading of active worms throughout the Internet, distributed denial of service attacks, and routing attacks to wireless ad hoc networks. For the first two attacks, he will determine anomalous changes in the network flow. For the third attack, he provides an abstract representation of a highly mobile ad hoc network to establish a baseline for detecting abnormalities generated by intrusions that change the behavior of the routing protocol. The author considers these problems in the framework of sequential change detection theory, as he wants to detect the appearance of an attack early in its development.
Charge-Remote Fragmentation: An Account of Research on Mechanisms and Applications

Abstract Charge-remote fragmentation is one class of decomposition reactions of gas-phase ions. Their discovery and applications had to await the development of soft ionization, particularly fast atom bombardment, and tandem mass spectrometry. The decompositions are particularly informative and allow functional groups to be identified and located in fatty acids, surfactants, steroids, complex lipids, and related materials. These are difficult structure assignments to make by other mass-spectrometry methods or by nuclear magnetic resonance and other spectroscopic techniques. Thus, charge-remote fragmentation fills an important need in structural chemistry. The mechanisms of charge-remote fragmentation underpin their structural utility, and they are still a matter of some debate. In this account, we discuss the discovery of charge-remote fragmentation in our laboratory. Further, we describe our efforts to understand their mechanism and to exploit their high information content in structure determinations of fatty acid and related materials.
Wearable Biosensors in terms of Energy Consumption, Placement and Communication Level: A Survey

This document gives formatting instructions for authors preparing manuscripts for publication in the International Journals/Conferences with ER Publications on a wide range of subject areas given in "Topics Covered". The document contains information regarding desktop publishing format, type sizes, and typefaces. Style rules are provided that explains how to handle equations, units, figures, tables, abbreviations, and acronyms. Sections are also devoted to the preparation of acknowledgments, references, and authors' biographies. The abstract is limited to 150 words and cannot contain equations, figures, tables, or references. It should concisely state what was done, how it was done, principal results, and their significance.
Large data computing using Clustering algorithms based on Hadoop

The Hadoop Distributed File System (HDFS) is designed to store large data sets reliably and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers and host are directly attached and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical at every size. Hadoop is a popular opensource implementation of MapReduce for the analysis of large datasets. To manage storage resources across the cluster, Hadoop uses a distributed user-level file system. This paper analyzes the performance of two major clustering algorithms K-means and DBSCAN on Hadoop platform and uncovers several performance issues. The experimental result demonstrates that K-means clustering algorithm is more efficient than DBSCAN algorithm based on MapReduce. Experimental results also show that DBSCAN algorithm based on MapReduce alleviates the problem of time delay caused by large data sets.
Ethical Privacy Guidelines for Mobile Connectivity Measurements

The Internet is a highly complex and pervasive information environment. Everyday activities increasingly have an online component, from talking to friends and family, watching TV programs, dating, to interacting with government. To understand and make sense of the complex Internet architecture underpinning these activities, network researchers need to collect and share datasets regarding the measurements of the network, from detailed traces on an individual basis to aggregated data on a regional level. Data on individuals’ Internet behaviour will frequently contain sensitive information about the data subjects’ lives. On the other hand, data that only reveal an Internet users’ connection to a given point on the network is not necessarily privacy invasive.The aim of these guidelines is to help researchers navigate the challenges of preserving the privacy of data subjects, publishing and disseminating datasets, while adhering to and advancing good scientific practice. These guidelines will help researchers assess the potential privacy risks and associated harms of a research project, and how these can be managed. They identify some of the common privacy problems that mobile networking researchers face, and offer ethical recommendations and considerations that need to be taken into account when designing a research project.
A structured dictionary learning framework for sparse representation

With the development of the computer, BOW model and SC model are applied to a large number of image classifications, and exhibit excellent performance, which become a hot topic in the field of computer vision. In the paper, we proposed a new framework of dictionary learning. the objective function based on sparse representation just consider the sparsity, while ignoring the spatial information of image and the correlation information of features, we apply spatial pyramid matching and add the discrimination fisher regularized penalty, by performing iterative optimization can get an excellent dictionary for representing image features, finally, we use max pooling and svm classifier for image classification. Experimental results in ORL and YALE face database show that the method has a high resolution.
An Improved Method for Efficient Power System Frequency Estimation

Accurate and efficient estimation of power system frequency is relevant to many power system applications. This paper provides a method for improving the efficiency of power system frequency estimation by combining the two techniques namely Smart Discrete Fourier Transform (SDFT) and Sliding DFT. Smart Discrete Fourier Transform (SDFT) has been proved to be a precise and easy implementable method for power system frequency estimation whereas Sliding DFT establishes a relationship between DFT of successive windowed sequence. Computational efficiency for power system frequency estimation has been analyzed. Keywords—Power system frequency estimation, Smart Discrete Fourier Transform (SDFT) ,Sliding DFT.
Strategy of guessing exercises - Variations of drawing trees

In informatics we often have to tempt students to solve dierent problems. This is required partly during practice and partly during tests (moreover this is especially important in the preparation for informatics competitions). We give two dierent methods of creating dierent but still commensurate exercises: one of them is to start from a base problem, and make some changes on it in small steps; the other way is to look for variations of the base task solution. 1 [11]
Health Care Analysis Based On Fuzzy Logic Control System

An intelligent decision making system for approximate reasoning that can approximately handle the uncertainty of critical risk for human health using fuzzy logic control system implemented in tool for finding an analysis. In this paper involved to diagnosis the health risk which is related to Blood Pressure, Pulse rate and Kidney function. Level of blood pressure analysed with kidney function by Glomerular Filtration Rate (GFR). Under this concept, fuzzy logic control system proposed to represent the parameters which may cause the risk for human health and analysis by using rule base factor which was implemented in MAT Lab Tool.