scholarly journals Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

2017 ◽  
Author(s):  
A. Salama ◽  
R. Saatchi ◽  
D. Burke
Keyword(s):  
Regression Model ◽  
Network Traffic ◽  
Fuzzy System ◽  
Adaptive Sampling ◽  
Computer Network ◽  
Sampling Technique

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

scholarly journals Evaluation of Takagi-Sugeno-Kang fuzzy method in entropy-based detection of DDoS attacks

2018 ◽  
Vol 15 (1) ◽  
pp. 139-162 ◽  
Author(s):  
Miodrag Petkovic ◽  
Ilija Basicevic ◽  
Dragan Kukolj ◽  
Miroslav Popovic
Keyword(s):  
Network Traffic ◽  
Fuzzy System ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Sudden Change ◽  
Change Point Detection ◽  
Statistical Characteristics ◽  
Ddos Attacks ◽  
Detection Process ◽  
Takagi Sugeno

The detection of distributed denial of service (DDoS) attacks based on internet traffic anomalies is a method which is general in nature and can detect unknown or zero-day attacks. One of the statistical characteristics used for this purpose is network traffic entropy: a sudden change in entropy may indicate a DDoS attack. However, this approach often gives false positives, and this is the main obstacle to its wider deployment within network security equipment. In this paper, we propose a new, two-step method for detection of DDoS attacks. This method combines the approaches of network traffic entropy and the Takagi-Sugeno-Kang fuzzy system. In the first step, the detection process calculates the entropy distribution of the network packets. In the second step, the Takagi-Sugeno-Kang fuzzy system (TSK-FS) method is applied to these entropy values. The performance of the TSK-FS method is compared with that of the typically used approach, in which cumulative sum (CUSUM) change point detection is applied directly to entropy time series. The results show that the TSK-FS DDoS detector reaches enhanced sensitivity and robustness in the detection process, achieving a high true-positive detection rate and a very low false-positive rate. As it is based on entropy, this combined method retains its generality and is capable of detecting various types of attack.

Computer Network Traffic Analysis Using SNMP Protocol and LVQ Networks

2016 ◽  
pp. 215-219 ◽  
Author(s):  
Ivan Nunes da Silva ◽  
Danilo Hernane Spatti ◽  
Rogerio Andrade Flauzino ◽  
Luisa Helena Bartocci Liboni ◽  
Silas Franco dos Reis Alves
Keyword(s):  
Network Traffic ◽  
Computer Network ◽  
Traffic Analysis ◽  
Network Traffic Analysis ◽  
Snmp Protocol

A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

2021 ◽  
pp. 33-36
Author(s):  
Chandrima Maity ◽  
Debasish Sanyal ◽  
Arati Biswas ◽  
Sudarsan Saha
Keyword(s):  
Logistic Regression ◽  
Postpartum Depression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Depression Scale ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Background Information ◽  
Stepwise Logistic Regression ◽  
Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

Network Situational Awareness

2015 ◽  
pp. 334-349 ◽  
Author(s):  
Tom Fairfax ◽  
Christopher Laing ◽  
Paul Vickers
Keyword(s):  
Real Time ◽  
Network Traffic ◽  
Affective Computing ◽  
Situational Awareness ◽  
Computer Network ◽  
Emergent Properties ◽  
Self Organized ◽  
Traffic Volumes ◽  
Self Organized Criticality ◽  
Intelligent Information

This chapter treats computer networks as a cyber warfighting domain in which the maintenance of situational awareness is impaired by increasing traffic volumes and the lack of immediate sensory perception. Sonification (the use of non-speech audio for communicating information) is proposed as a viable means of monitoring a network in real time and a research agenda employing the sonification of a network's self-organized criticality within a context-aware affective computing scenario is given. The chapter views a computer network as a cyber battlespace with a particular operations spectrum and dynamics. Increasing network traffic volumes are interfering with the ability to present real-time intelligence about a network and so suggestions are made for how the context of a network might be used to help construct intelligent information infrastructures. Such a system would use affective computing principles to sonify emergent properties (such as self-organized criticality) of network traffic and behaviour to provide effective real-time situational awareness.

Network Traffic and Data

2011 ◽  
pp. 60-103
Author(s):  
Yu Wang
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Computer Network ◽  
Transmission Control Protocol ◽  
Processing Unit ◽  
Data Types ◽  
Specific Data ◽  
Related Data ◽  
Central Processing ◽  
Intrusion Detection And Prevention

In this chapter we will focus on examining computer network traffic and data. A computer network combines a set of computers and physically and logically connects them together to exchange information. Network traffic acquired from a network system provides information on data communications within the network and between networks or individual computers. The most common data types are log data, such as Kerberos logs, transmission control protocol/Internet protocol (TCP/IP) logs, Central processing unit (CPU) usage data, event logs, user command data, Internet visit data, operating system audit trail data, intrusion detection and prevention service (IDS/IPS) logs, Netflow1 data, and the simple network management protocol (SNMP) reporting data. Such information is unique and valuable for network security, specifically for intrusion detection and prevention. Although we have already presented some essential challenges in collecting such data in Chapter I, we will discuss traffic data, as well as other related data, in greater detail in this chapter. Specifically, we will describe system-specific and user-specific data types in Sections System- Specific Data and User-Specific Data, respectively, and provide detailed information on publicly available data in Section Publicly Available Data.

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