Internet Abuse in the workplace{2}


by Rafael F
Information Systems e-Business Management Journal Volume 6 page 419-439

This article focuses on a very important issue that has plagued the workplace for many years. It is referring to the misuse of the internet by employees. This misuse is referred to as Internet abuse in the workplace. Such abuse can lead to network congestion, security risks, and even sexual harassment (p. 420). It goes on to explain how several companies are implementing or have begun to implement filtering software that will help detect and hopefully stop this abuse. The essence of this paper is to introduce a study on Text Mining which is a new form of detecting websites that should be blocked. Traditional web blocking is limited to predefined words that place websites into certain categories. This can lead to blocking sites that really should not be blocked. Text mining however, treats each web page as a document and goes through each page and classifies it as potential abuse or not. It did not really explain how it determines what is abusive and what is not. What it does mention several times is that text mining is a complement to filtering software that is out on the market.

As a member of the IT department at work, part of my duties is to control the access to websites by the firewall. Many times there are sites that are blocked that really should not be and other times I find that access to certain sites have not been restricted. If the internet is not filtered properly, you will see that much abuse takes place every day. When I review reports on the firewall, I like to see what sites are being viewed the most and then determine if the site should be allowed or not. This process takes much time every month and if there are better techniques to solve the internet abuse issue then I am all ears to them.

Works Cited

Chou, Chen-huei, Atish Sinha, and Huimin Zhao. “A Text Mining Approach to Internet Abuse Detection.” Information Systems and eBusiness Management 6.4 (2008): 419-39. ABI/INFORM Complete. Web. 20 Nov. 2011.