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It lists a risk class called "legal liability", of which it quotes the categories "gambling", "racism and hate", "adult material" etc. SuperDave wrote: We're still running IE6. And while it works on my laptop fairly nicely, the iphone experience is not as easy to use of course. This methodology has the advantage of reducing two thirds of the time-consuming work required to prelabel all the URLs. They're giant, flying pinatas Registered: Aug 25, Posts: Close X. Hence a major contribution of this work was in evaluating the performance of the filters without having to manually label all URLs in the test set Positive pregnancy tests after iui pornographic or not, by using voting among filters Breasts websense taking Breasts websense decisions as absolute labels. ChloroFiend wrote: I work InfoSec and cannot access sites classified as 'hacking' sites. Start Free Trial.
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Huge boobs milf and French catwalk models babe threesome. Austin Business Journal. MsCleopatra Retrieved August 31, Online 23 min Savingcams - Hormone supplements and Breasts websense can also lead to breast pain. Estrogen and progesterone can actually increase the size and number of ducts and milk Breasts websense in your breasts. Searches Related to "breasts". Although many causes of breast pain and sensitivity can be treated at home, you should see your Brewsts if you begin experiencing severe symptoms. If you suspect that you have a cyst, see your doctor.
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- Forcepoint , previously known as Websense or Raytheon Websense , is an Austin -based company owned by U.
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A couple of jobs ago, we got internet access installed on computers for work purposes. Stuff was blocked by what I think was websense, to the point where most things I would check out would be blocked -- including Ars, which was categorized "Web Chat. I get infuriated when for example I have a few minutes free and want to read an article on a gaming site blocked.
Not that there'd be playable games there, but maybe checking on a release date for something coming up. Or if I want to watch World Cup games? Good to go all day long. Youtube where something might actually be relevant to a work-related discussion?
That same video on Google Video is good to go, though, and one of the "related videos" on the right will have a still of a vagina being vigorously rubbed. I will say that the only consistency I've seen with the stuff at work besides the obvious porn is that webmail is totally blocked. And I'm OK with that. I'm more productive in the average day than most, and my job is getting done. We're still running IE6.
Not only do I not have to deal with WebSense at work but there's an actual policy stating that I cannot be blocked because the internet plays a vital role in the performance of my job. For a while we had Google News blocked as a virus propagation site.
I didn't really care, but I found it hilarious. Also, back when Gmail was blocked, you could still access it via iGoogle.
SuperDave wrote: We're still running IE6. I point. I laugh. I'm usually the proxy admin, forced to set policy dictated by management. It usually makes sense, but one place had the "sports" category blocked. So we couldn't even read the sports news. That changed when a new football mad manager arrived. Unfortunately "games" is nearly always blocked. And because of the way most products categorise that means any kind of games news sites or even Penny-Arcade are not accessable either.
I started refusing to supply usage reports for queries that were obviously not based on any kind of employee performance complaint, and advising people not to use the internet or email at all outside of business needs. Glad I don't work there anymore. At my last job it was installed by default and I didn't have admin access to update it.
They were hiring me as a web developer. I wept. I'm not longer there. IE6 being standard across all desktops is still not that unusual. There's an application that is very widely used throughout the mining industry that isn't supported on anything else.
My employer's corporate website doesn't render properly in IE6. The point is moot, though - there's so little bandwidth available, I can start IE on a computer, boot my Droid from completely dead, and be posting from it at Ars before any graphics at all have appeared on the computer screen.
Did just that a few days ago. Previous company's Websense blocked The Onion as "adult content". A few of us ganged up and ordered the dead-tree edition and had it delivered to us at work. We also sent a copy to the Websense admins.
Not sure if they got the joke or not. Online, anonymous proxies are often workable solutions for over-zealous filtering. I remembr when websense was blocking a site that had a negative review of websense. Or my favorite, blocking Cisco. Petruchio wrote: bluloo wrote: Online, anonymous proxies are often workable solutions for over-zealous filtering. So is not wearing pants.
Or so I've heard. I thought you worked in a secured facility though? Or at least I recall something about a security clearance I don't think I've encountered web filtering since middle school by high school, the admins had realized it was pointless and caused more problems than it solved.
I think I would go crazy if I couldn't browse or listen to Pandora or whatever. AlphaMeridian wrote: Petruchio wrote: bluloo wrote: Online, anonymous proxies are often workable solutions for over-zealous filtering.
Websense blocks everything here. I'm shocked that I can access Ars. I can't even visit yahoo. I work InfoSec and cannot access sites classified as 'hacking' sites. Gmail via HTTP is websense blocked ChloroFiend wrote: I work InfoSec and cannot access sites classified as 'hacking' sites. It's not unusual at GeneriCorp XYZ, sure, but at a company that does software development for the web? That's fucked. I should mention that this was last year.
Mine didn't, nor did any policy specifically forbid their use. Hell, the facility IT manager would let the women he thought were hot stream mp3s from their PCs for others to share. He even installed SW for them at one time or another until corporate specifically banned such things. For some time, we had unlimited net access. For a short time afterward, while new policy was being formed, we had none.
After that period, we had broadly filtered access at this time a proxy would have been useful for both work and non-work related sites. Quote: We have a policy barring the use of personal webmail. I just tested this. It works. I'm laughing. Mortus wrote: Quote: We have a policy barring the use of personal webmail. Unless you worked at the software house that built the damn app that wasn't supported on anything but IE6! I have only worked places were we had a notice web browsing was monitored, but never filtered.
And there was no evidence anyone was really monitoring it, either. I would also try to find ways around it. Just yesterday I was watching an Onion video only to discover it contained breasts so I don't know if this one is entirely inaccurate. That's just dumb, then. What's the point of putting a filter in flace if you're not going to block proxies? Mortus wrote: I just tested this. Yeah, it's stupid. Websense is stupid. But it's less stupid than anything else that has similar functionality.
I work from home. Nothing is blocked. Obligatory "Internet Explorer cannot display the webpage" counterpoint. We have IE 7. I'm actually surprised we have 7, considering the archaic technology that this company uses. One of my job responsibilities is managing Websense for about 20, employees. At my last company, I worked desktop support and got blocked constantly by websense when trying to research error messages and things online.
We couldnt download printer drivers from HP as well as all kinds of executable files. Out network guy didnt mind as long as we kept the systems off network so we didnt introduce viruses or anything onto the network. That and if the desktops got a virus or something, we would be the ones to fix it anyways, so it was in our best interest to not cause problems.
We have Barracuda. I hate Baraccuda.
A study in JAMA found that Forcepoint had the best-performing web-filtering products in terms of blocking pornography while allowing health information. FRESH 2. April 29, The contents and products in this web site are protected by U. Big Boobs 2 min Ivory-black - They can appear in one breast or both.
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Eyas S. The Internet is becoming a significant source of all types of information to all people. This has made Internet censorship a major and controversial issue. While many people believe that the use of content filtering products is against free speech, there are others, especially parents and librarians, who are concerned about the negative effects of Internet pornography on minors.
Many libraries and schools are mandating filtered access to the Internet. The performance evaluation is based on a log of several tens of thousands of uniform resource locators URLs , which has been collected from an Internet service provider ISP. This ISP provides unfiltered Internet access to several thousands of customers. Several performance measures have been investigated to compare the performance of these products.
These measures include the blocking rate of pornographic materials and the false alarm rate the blocking rate of nonpornographic material.
Furthermore, the method we propose to evaluate the filters has the added advantage of being practical. One of the biggest complaints people have about the Internet concerns the proliferation of pornography. To guard minors and conservative communities from pornography, many products appeared in the market with the goal of filtering Internet access and hence restricting access to pornographic sites.
Another important application for Internet filtering is resource management, where an organization wishes to ensure that its Internet connection is properly used for legitimate business activities during office hours, so nonbusiness sites are blocked.
The definition of the problem is as follows: Assuming that an organization has its own definition of what is suitable and what is not, the organization must find the filter that best satisfies its needs in allowing access to the maximum number of sites it deems suitable, while at the same time blocking the minimum number of sites it deems unsuitable.
This work attempts to measure the effectiveness of several commercial filters in blocking access to pornography. Filter effectiveness is measured as minimizing the blocking of suitable sites and maximizing the blocking of unsuitable sites. It is important to note that this work focuses on filtering Web-based traffic which is in fact the vast majority of Internet traffic. It is believed that the results of this work would benefit organizations e.
The contribution of this work is not only in assessing filter effectiveness but also in outlining a practical procedure by which to test filtering software.
The procedure can be applied to new filters, for different filtering objectives, or with different suitability standards in mind. Usually, to assess a detection task the decisions of the detector are compared to the actual identities of the objects to detect.
In our case, the objects to detect are URLs of pornographic Web objects. This means that a set of Web pages is needed, with each page being prelabeled as pornographic or not. Hence a major contribution of this work was in evaluating the performance of the filters without having to manually label all URLs in the test set as pornographic or not, by using voting among filters and taking unanimous decisions as absolute labels. The work relies on the principles of statistical decision theory in evaluating the filters.
The major components of this work are the following:. Filtering software blocks content in two primary ways: blocking by URL and blocking by the content of retrieved pages. As an alternative approach to the black list, some filtering software uses a "white list. This is intended mainly for school students or closed communities.
Content-based blocking is widely criticized for its ineffectiveness. A block on the word "breast" might block pages about breast cancer. Address-based blocking is preferred since it is less prone to errors. Moreover, sites in different languages are hard to detect. Newer image-based content filters are emerging; they have yet to gain widespread acceptance.
However, address-based blocking is more expensive because of the overhead incurred in the frequent updating of the black list. Filtering software can also be classified based on its location within the network into two classes: client based and server based.
In client-based filtering software, the filter resides at the client side. It interacts with browsers installed on the client machine to employ filtering functionality while a person surfs the Internet. Because it is installed on the client machine, it is considered to be voluntary. The client can choose to uninstall it.
In server-based filters, on the other hand, the filter is installed on a server within a network. It is managed by the network administrator; therefore, filtering can be forced upon all network clients. These filters are widely used in corporations and large organizations.
The filter can be a plug-in to a known Web proxy e. In this paper, the performance of six filtering products was evaluated. All selected filters use the black list technique. Furthermore, all filters except N2H2 are server based. At the time of the experiment, N2H2 Inc. The first step in testing the filters was to construct a sufficient and representative test set of Web pages or URLs that adequately mimics the target user population. The target user population in this case is assumed to be the casual home user accessing the Internet through a dial-up connection to a public ISP.
For that the test set was chosen to be a large set of 54, page requests URLs from actual users, registered in the proxy log of an ISP. The data was collected during a hour period during the summer of At that particular ISP, Internet access was provided through a proxy that cached frequently requested pages.
However, the proxy did not block access to any sites. As a side effect of using the proxy, a log was automatically produced that specified for each user request the destination URL the address of the page that was requested among lots of other detailed information. It should be noted that a URL addresses a Web object and not a whole page, so pages with multiple objects such as images would have multiple URLs in the test set.
Two data sets were prepared: 1 the original set with all URLs and 2 the distinct set , which is the data set after removing all duplicate URLs URLs that were requested more than once during the data collection period and keeping only distinct URLs.
Note that the second test set is a proper subset of the first and hence does not require any extra effort in performing the experiment. Only the analysis stage is affected. Error statistics on the original set are more indicative of the user populations because misdetections or false alarms in a URL requested multiple times will be reflected in the final error rate. On the contrary, the distinct set lists each URL only once, and hence an error will be counted only once.
First we will describe the experiments performed on the original set and analyze the results. In the next sections, we will address the distinct set.
In this step all URLs in the data set were run through each of the filters to determine each filter's particular decision about each URL. The test machine was a Sun Altra 10 that runs Solaris 2. Netscape proxy server version 3. The experiment was done between December and January The result of running all the filters on the data set is summarized in the following table.
The total number of URLs tested was 54, Table 2 shows the number of URLs blocked by each filtering product. Figure 2 shows the percentage of total URLs that were blocked by each filtering product. As can be seen from the table, the filters agree to a certain extent on the number of URLs that are blocked from among the total test set. As can be seen from the table, the number of blocked URLs for all filtering products fall within a small interval except that of I-Gear. The next step was to use the filter decisions as a basis for labeling each URL as pornographic or not.
The method used here was conceptually simple but saved a lot of effort practically. The method was to trust the unanimous decisions reached by the filters.
So, all URLs with unanimous "block" decisions were considered to be pornographic, while all URLs with unanimous "retrieve" decisions were considered to be nonpornographic.
The remaining URLs with different decisions were labeled manually. All URLs with different filter decisions were manually checked and labeled to be pornographic or not based on the usual U.
Table 3 summarizes the labels. The first two rows show the number of URLs unanimously blocked and retrieved, respectively; the third row shows the URLs with different decisions, which were manually labeled.
The final two rows show the summary of labels of all URLs in the database, where the pornographic row includes sites unanimously agreed to by the filters plus the URLs deemed pornographic from the manual check from among the URLs with different filter decisions.
From the table we see that two thirds of the URLs were unanimously labeled, while one third of the URLs were manually labeled, as all filters agreed to most of the URLs.
The last step in this evaluation is to analyze the error rates of each filter. In any detection task two possible types of error by filters are possible. The misdetection rate for each filter was calculated by calculating the conditional probability that a URL was labeled pornographic but was not blocked by the filter.
The false alarm rate for each filter was calculated by calculating the conditional probability that a URL was labeled nonpornographic but was blocked by the filter. The probability of pornographic URL but not blocked by filter is calculated as the number of pornographic URLs that were not blocked by the filter divided by the total number of URLs in the set.
The other probabilities are calculated similarly. In calculating the error rate of each filter, we give equal weight to each of the two types of errors. Table 4 shows the errors of each filter. As can be seen from the table, all products have error rates that are close to one another, except one. In this experiment, SurfWatch turned out to be the filter with the lowest error rate, but it was closely trailed by SmartFilter, CyberPatrol, and N2H2. Figure 1 shows the results graphically. Figure 1.
Error rates for the original set. Similar processing and analysis was done on the distinct set. It is important to note that the most resource-consuming tasks in the experiment running the filters on the URLs and manually labeling URLs did not need to be repeated for this data set.
The filter decisions were taken from the trial runs on the original data set.