Feasibility Study: Computing Confidence Interval (CI) for IBR Data Using Bootstrapping Technique
- Chindipha, Stones D, Irwin, Barry V W
- Authors: Chindipha, Stones D , Irwin, Barry V W
- Date: 2021
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427665 , vital:72454 , https://www.researchgate.net/profile/Barry-Ir-win/publication/358895311_Feasibility_Study_Computing_Confidence_Inter-val_CI_for_IBR_Data_Using_Bootstrapping_Technique/links/621bdc469947d339eb6e578b/Feasibility-Study-Computing-Confidence-Interval-CI-for-IBR-Data-Using-Bootstrapping-Technique.pdf
- Description: Statistical bootstrapping has been used in different fields over the years since it was introduced as a technique that one can use to simulate data. In this study, parametric and nonparametric bootstrapping techniques were used to create samples of different compositions from the baseline data. The bootstrap distribution of a point estimator of a population parameter has been used in the past to produce a bootstrapped confidence interval (CI) for the parameter’s true value, if the parameter is written as a function of the population’s distribution. Population parameters are estimated with many point estimators. The study used mean as the population parameter of interest from which bootstrap samples were created. This research was more interested in the CI side of bootstrapping and it is this aspect that this paper focused on. This is the case because the study wanted to offer a certain degree of assurance and reliability of IBR data to users who may not have access to a larger ’lens’ of a network telescope to allow them to monitor security threats in their network. The primary interest in the dataset were source and destination IP (DSTIP) addresses, thus the study selected different size pools of DSTIP addresses to simulate bootstrap samples.
- Full Text:
- Date Issued: 2021
- Authors: Chindipha, Stones D , Irwin, Barry V W
- Date: 2021
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427665 , vital:72454 , https://www.researchgate.net/profile/Barry-Ir-win/publication/358895311_Feasibility_Study_Computing_Confidence_Inter-val_CI_for_IBR_Data_Using_Bootstrapping_Technique/links/621bdc469947d339eb6e578b/Feasibility-Study-Computing-Confidence-Interval-CI-for-IBR-Data-Using-Bootstrapping-Technique.pdf
- Description: Statistical bootstrapping has been used in different fields over the years since it was introduced as a technique that one can use to simulate data. In this study, parametric and nonparametric bootstrapping techniques were used to create samples of different compositions from the baseline data. The bootstrap distribution of a point estimator of a population parameter has been used in the past to produce a bootstrapped confidence interval (CI) for the parameter’s true value, if the parameter is written as a function of the population’s distribution. Population parameters are estimated with many point estimators. The study used mean as the population parameter of interest from which bootstrap samples were created. This research was more interested in the CI side of bootstrapping and it is this aspect that this paper focused on. This is the case because the study wanted to offer a certain degree of assurance and reliability of IBR data to users who may not have access to a larger ’lens’ of a network telescope to allow them to monitor security threats in their network. The primary interest in the dataset were source and destination IP (DSTIP) addresses, thus the study selected different size pools of DSTIP addresses to simulate bootstrap samples.
- Full Text:
- Date Issued: 2021
An Evaluation of Text Mining Techniques in Sampling of Network Ports from IBR Traffic
- Chindipha, Stones D, Irwin, Barry V W, Herbert, Alan
- Authors: Chindipha, Stones D , Irwin, Barry V W , Herbert, Alan
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427630 , vital:72452 , https://www.researchgate.net/profile/Stones-Chindi-pha/publication/335910179_An_Evaluation_of_Text_Mining_Techniques_in_Sampling_of_Network_Ports_from_IBR_Traffic/links/5d833084458515cbd1985a38/An-Evaluation-of-Text-Mining-Techniques-in-Sampling-of-Network-Ports-from-IBR-Traffic.pdf
- Description: Information retrieval (IR) has had techniques that have been used to gauge the extent to which certain keywords can be retrieved from a document. These techniques have been used to measure similarities in duplicated images, native language identification, optimize algorithms, among others. With this notion, this study proposes the use of four of the Information Retrieval Techniques (IRT/IR) to gauge the implications of sampling a/24 IPv4 ports into smaller subnet equivalents. Using IR, this paper shows how the ports found in a/24 IPv4 net-block relate to those found in the smaller subnet equivalents. Using Internet Background Radiation (IBR) data that was collected from Rhodes University, the study found compelling evidence of the viability of using such techniques in sampling datasets. Essentially, being able to identify the variation that comes with sampling the baseline dataset. It shows how the various samples are similar to the baseline dataset. The correlation observed in the scores proves how viable these techniques are to quantifying variations in the sampling of IBR data. In this way, one can identify which subnet equivalent best represents the unique ports found in the baseline dataset (IPv4 net-block dataset).
- Full Text:
- Date Issued: 2019
- Authors: Chindipha, Stones D , Irwin, Barry V W , Herbert, Alan
- Date: 2019
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427630 , vital:72452 , https://www.researchgate.net/profile/Stones-Chindi-pha/publication/335910179_An_Evaluation_of_Text_Mining_Techniques_in_Sampling_of_Network_Ports_from_IBR_Traffic/links/5d833084458515cbd1985a38/An-Evaluation-of-Text-Mining-Techniques-in-Sampling-of-Network-Ports-from-IBR-Traffic.pdf
- Description: Information retrieval (IR) has had techniques that have been used to gauge the extent to which certain keywords can be retrieved from a document. These techniques have been used to measure similarities in duplicated images, native language identification, optimize algorithms, among others. With this notion, this study proposes the use of four of the Information Retrieval Techniques (IRT/IR) to gauge the implications of sampling a/24 IPv4 ports into smaller subnet equivalents. Using IR, this paper shows how the ports found in a/24 IPv4 net-block relate to those found in the smaller subnet equivalents. Using Internet Background Radiation (IBR) data that was collected from Rhodes University, the study found compelling evidence of the viability of using such techniques in sampling datasets. Essentially, being able to identify the variation that comes with sampling the baseline dataset. It shows how the various samples are similar to the baseline dataset. The correlation observed in the scores proves how viable these techniques are to quantifying variations in the sampling of IBR data. In this way, one can identify which subnet equivalent best represents the unique ports found in the baseline dataset (IPv4 net-block dataset).
- Full Text:
- Date Issued: 2019
Effectiveness of Sampling a Small Sized Network Telescope in Internet Background Radiation Data Collection
- Chindipha, Stones D, Irwin, Barry V W, Herbert, Alan
- Authors: Chindipha, Stones D , Irwin, Barry V W , Herbert, Alan
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427646 , vital:72453 , https://www.researchgate.net/profile/Barry-Ir-win/publication/327624431_Effectiveness_of_Sampling_a_Small_Sized_Net-work_Telescope_in_Internet_Background_Radiation_Data_Collection/links/5b9a5067299bf14ad4d793a1/Effectiveness-of-Sampling-a-Small-Sized-Network-Telescope-in-Internet-Background-Radiation-Data-Collection.pdf
- Description: What is known today as the modern Internet has long relied on the existence of, and use of, IPv4 addresses. However, due to the rapid growth of the Internet of Things (IoT), and limited address space within IPv4, acquiring large IPv4 subnetworks is becoming increasingly difficult. The exhaustion of the IPv4 address space has made it near impossible for organizations to gain access to large blocks of IP space. This is of great concern particularly in the security space which often relies on acquiring large network blocks for performing a technique called Internet Background Radiation (IBR) monitoring. This technique monitors IPv4 addresses which have no services running on them. In practice, no traffic should ever arrive at such an IPv4 address, and so is marked as an anomaly, and thus recorded and analyzed. This research aims to address the problem brought forth by IPv4 address space exhaustion in relation to IBR monitoring. This study’s intent is to identify the smallest subnet that best represents the attributes found in the/24 IPv4 address. This is done by determining how well a subset of the monitored original subnetwork represents the information gathered by the original subnetwork. Determining the best method of selecting a subset of IPv4 addresses from a subnetwork will enable IBR research to continue in the best way possible in an ever restricting research space.
- Full Text:
- Date Issued: 2018
- Authors: Chindipha, Stones D , Irwin, Barry V W , Herbert, Alan
- Date: 2018
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/427646 , vital:72453 , https://www.researchgate.net/profile/Barry-Ir-win/publication/327624431_Effectiveness_of_Sampling_a_Small_Sized_Net-work_Telescope_in_Internet_Background_Radiation_Data_Collection/links/5b9a5067299bf14ad4d793a1/Effectiveness-of-Sampling-a-Small-Sized-Network-Telescope-in-Internet-Background-Radiation-Data-Collection.pdf
- Description: What is known today as the modern Internet has long relied on the existence of, and use of, IPv4 addresses. However, due to the rapid growth of the Internet of Things (IoT), and limited address space within IPv4, acquiring large IPv4 subnetworks is becoming increasingly difficult. The exhaustion of the IPv4 address space has made it near impossible for organizations to gain access to large blocks of IP space. This is of great concern particularly in the security space which often relies on acquiring large network blocks for performing a technique called Internet Background Radiation (IBR) monitoring. This technique monitors IPv4 addresses which have no services running on them. In practice, no traffic should ever arrive at such an IPv4 address, and so is marked as an anomaly, and thus recorded and analyzed. This research aims to address the problem brought forth by IPv4 address space exhaustion in relation to IBR monitoring. This study’s intent is to identify the smallest subnet that best represents the attributes found in the/24 IPv4 address. This is done by determining how well a subset of the monitored original subnetwork represents the information gathered by the original subnetwork. Determining the best method of selecting a subset of IPv4 addresses from a subnetwork will enable IBR research to continue in the best way possible in an ever restricting research space.
- Full Text:
- Date Issued: 2018
- «
- ‹
- 1
- ›
- »