Poker Show VR Free Download [cheat]

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Hedda Tillmon

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Aug 20, 2024, 11:26:43 PM8/20/24
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A nave cheater would win at an incredibly fast rate, and these cheats are caught very quickly usually, and if not caught quickly they are easy to detect through a quick scan through their hand histories.

The more difficult problem occurs when the cheater exhibits intelligence, bluffing in spots they are bound to be called in, calling river bets with the worst hands, the basic premise is that they lose pots on purpose to disguise their ability to see other players cards, and they win at a reasonably realistic rate.

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It's an interesting problem also because it is current, and has real application in bettering the world if someone finds a creative solution, as there is a good chance genuine players will have funds refunded to them when identified cheaters are discovered.

Statistical analysis is a joke for identifying poker cheats
I realize the question allows there to be millions of hands worth of history available to the system. I'm sure there are players with hand histories this large, hell, I've probably played this many online hands. But I've also been playing online for over 10 years. Thats not a small amount of time, and it is my understanding that two conflicting things are true when it comes to identifying online poker cheaters: it needs to happen in a small amount of time, and like any good thief, an online poker cheat is going to take his stash elsewhere immediately after the taking.

There was a great example of the variance in poker in this paper which was generated by matching an always raise player versus an always call player (page 13 of the PDF). Over the course of 100,000 hands, wayyyy more than I think most people would be willing to play against someone who could see their cards, the always call player won on average .026 small blinds per hand. I know this does not sound like much, but assuming stakes of $5-10, that comes out to $6,500. Maybe someone can help me find the link, but the measured professional win rate is less not too much larger than this. Please note, NEITHER of these players was cheating, and the statistically expected difference over this number of hands is significantly less than what actually transpired.

What online poker players need to understand
Poker is gambling. It is a game of skill, because some players are able to elicit more information from their opponents than their opponents are able to gather, and that extra information is often as useful as seeing other peoples cards. Even players who are better players than their typical opponents, will end up long term losers. If you do not understand this, you're just searching for witches with statistics in the arbitrarily small number of hands you'll be playing against any opponent.

What can be done?
Keeping in mind the question states that cheaters are able to see the other players cards, you don't need statistical analysis to identify them. There are only three ways in which that is possible.

First is that the server is sending the information intentionally to clients which is an obvious security issue and should not be implemented (IMO, even for moderators). If a site was found allowing this to happen, it is the player's responsibility to move their funds elsewhere, or refuse to play on the site until that terrible design decision is rectified. It should also be the responsibility of the sites to inform their players of the exact steps that take place during hands played on the site so they have that to make their decision on when choosing a site in the first place. Security by obscurity is unpermitable. As for catching the thieves, this information should be sitting in log files on their servers, which should be regularly audited for this type of behavior.

Second is that the user has hacked the poker server and they would know about that in hurry, or else once it is exposed, it is again players responsibility to determine where to play. In this case, the cheater can be prosecuted in most countries.

Lastly, it is possible the dealing algorithm has been cracked. This one was a major problem in the past with companies that used naive methods to deal hands, but most of the major shops solved this problem by taking random inputs from players logged into their system as well as using entropy generating hardware to seed their random number generator. Thats not to say it cannot be cracked however. If this is the case, the only option is for the company to engineer a new random number generator.

A better question is "how is cheating even possible ?". There is no need what so ever to send the opponent's hands over the wire until at showdown. If that data isn't sent to the client, then how could they cheat ?

But I would like to know at which online poker provider the cheating is possible. Because I can't imagine a way how to do this, if the poker software is coded properly. If I was asked to program an online poker software, The users wouldn't be able to see the opponents cards, because there is no way he could get this information. And this is how I would do this.

The only way the users could cheat here is, you get together with other players, or impersonate multiple players with different accounts and accessing IPs, and open another channel to communicate between the players. This way the group has a big advantage because they know more than their own cards, but there's still no way they can see other cards. And because it's now a group that is cheating it is even more harder to detect it, because they can share their earnings with multiple players, and this group could even have a player that looses more than (s)he gains and still win overall.

You could however narrow the candidates who you think might be cheating, by looking at the users who over your time period benefited overall. This will remove the vast majority of users, allowing you to focus your resources better. (This of course will include users who are skilled at Poker.).

Once you've done that, you can compare the history of play from while the cheat was possible to the history afterwards or before, and see if the users success decreases or increases.That should give you a list of users who you need to investigate more carefully, possibly by analyzing specific games.

For all of you expressing disbelief that this is even possible: the community on the poker forums linked in OP were similarly awestruck, but the site in question has confirmed that such a security vulnerability was present. Quite simply, the site was using very basic and insecure crypto to transmit hole card data to its players. Theoretically, it would have been possible for anyone aware of this to intercept transmissions from the site to a specific victim (eg. by being physically nearby and intercepting wireless data), and to cheat that player using the intercepted knowledge.

Oh, and also some of you seem to be assuming we're talking about a hypothetical scenario, and/or play-money poker; we're not. The site is real, the vulnerability was real, the investigation is really happening (see link in OP), and the games under investigation are real-money games with normal buyins of $200 and above.

I'm by no means a data-mining expert, and my grasp of statistical analysis of large data sets is pretty weak as well (and I'm not very good at poker, even though I love it) so take everything I say here with a grain of salt.

Weed out the junk data. You are going to only really care about players that fit into two categories: (1) players who win more hands than they lose, (2) players who win more money than they lose. Who cares about a cheater who loses a lot? Heh.

With this paired down list of players to actually analyze, I would take a look at their style of play. Assuming you have a lot of historical data, I would build a player skill profile and attempt to normalize their betting strategy. As a poor poker player, I normally will back up weaker cards that no decent player would back simply because they feel good. For example, any time I am dealt a face card with another low card (2, 3, 4, 5), if they're suited, I'll often ALWAYS call any bets made by other players before the turn, even though this strategy is not very successful. Pre-turn raises above the Big Blind often indicate a player has a pocket pair, yet my love of playing won't let me fold a suited hand pre-flop.

So for me, your analysis of my play would say that me matching aggressive calls pre-flop when I have anything suited would be normal. But a different player who only occasionally calls large pre-flop bets would be an indication that something might be out of whack.

I don't know what sort of system you'd need to build to make a profile of different users styles of play, but I imagine you could use some computer learning algorithms to "learn" a person's style of play with pretty decent accuracy.

Anyway, those are the things I've thought of. Now actually implementing them, I have no idea where to even begin so I'm afraid I can't be of much help there. This is a very interesting academic problem though, so please do us a favor and keep us informed of what you end up going with. If you want to take this conversation offline, feel free to email me at [email protected].

This MIGHT be indicative of the player knowing his starting KINGS (pretty good) is not as good as someone elses pocket ACES .. however that's assuming he makes the decision pre-flop and not post flop.. depends really..

To be perfectly honest, I'd doubt very much that the players who could see opponents hands were random. There must be some sort of cross over in the code that generates the card view that was selecting some users but not others. I would recommend running tests on this code and trying to find a trend in the "viewers" and "non-viewers". If you find a strong trend, then the trend could be applied to the actual dataset too see which users, or which hands or which whatever was generating the code fault.

The answer to your question is simple. There is no way to detect that type of cheater with just hand histories. You need the information that is not public in order to correlate multiple characteristic's to find a suspected cheater.

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