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55 wordsPermalink| Show parent| ReplyRe: Discussion Assignment Unit 3by Rumi Torii- Tuesday, 30 April 2019, 12:08 AM
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Hi Nimra,It would be interesting to pick different samples and compare them with other community. I was talking to my Indonesian friend this week and she mentioned that they are the shortest in the world. If you have an Indonesian in your community, then you might find them an outlier. Rumi52 wordsPermalink| Show parent| ReplyRe: Discussion Assignment Unit 3by Gary Warren- Sunday, 28 April 2019, 7:08 PMMy company is involved in analyzing malware and attacks by hackers against customer web sites. They take data from network firewalls and try to determine both the number of attempts pera period of time (hours, days, months, etc.) and severity of the attempt. Severity could include the type of attack (denial of service, password hacking, etc.) and any related impacts. When you consider the number of connections a public web site handles per day, the data sets can become very large.To keep this analysis simple, we can consider the population to be all of the computer servers hosting public facing e-Commerce web sites for a particular retailer within the southern United States (Target for example). The numeric measure could be the number of denial of service (DOS) attempts per server in an 8-hour period. Denial of service occurs when hackers attempt to overload a web server with so many requests that it stops functioning due to the workload (Security Tip (ST04-015). Of these servers, we could take a sample of 100 of them and determine the number of DOS attacks against them in the 8-hour time frame. This would be a sufficiently large data set to analyze. If, within this 100-server sample, our analysis indicated a small number of outliers (2 servers show high levels of attack attempts for example), we would investigate and determine if there is a valid reason or the data was misleading. There are a few things that could have occurred to create this scenario: Maybe the attacks were misdiagnosed and are actually false positives (we think there was an incident but there is not). No alert or notification would be needed here and the outliers could be ignored. In this case, the data could have been bad. Hardware issues, malfunctioning software, etc. could have created the outlier data. The servers in the sample instance are misconfigured allowing more incidents to occur. This could be considered an anomaly as it is outside of the norm but the sample data is valid. In this case, the outliers are an important indicator that some event has happened. These outliers could lead to additional investigation.
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Outliers are indicators that alert you to conditions in your data that are outside the normal ranges.They cannot be ignored of course but are not always a sign of errors. In most cases, they force additional analysis of your data.
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  • Winter '19
  • Rumi Torii

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