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  • Distributing entropy to virtual machines.

    - by Louis
    Dear All, I'm interested in generating secret keys for SSL on virtual machines using true randomness. By true randomness I mean the same level of entropy that can be generated by UNIX's dev/random and entropy gathering daemon (EGD). Is there a "general knowledge" recipe to route entropy from the physical layer to the virtual machines via the hypervisor regardless of the Hypervisor/Guest OS combination? Example: suppose one "hypervises" with VMware VSphere and instantiates Windows Guest OS. Can this hypervisor collect entropy from its peripherals (like dev/random/ would) and distribute it to these guest Windows OS? When considering the big vendors (VMware, Hyper-V, Citrix, etc), do they have entropy pools that gather entropy that can easily be pushed to their respective virtual machines? Louis

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  • Does urandom share the same entropy of random?

    - by ???
    Does the entropy pool /dev/random used the same to /dev/urandom? I want to mknod /dev/random 1 9 to replace the slow random, I think the current entropy is randomly enough, if urandom is based on the same entropy, and all succeed random numbers are generated based on that entropy, I don't think there'll be any vulnerable.

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  • Server suddenly running out of entropy

    - by Creshal
    Since a reboot yesterday, one of our virtual servers (Debian Lenny, virtualized with Xen) is constantly running out of entropy, leading to timeouts etc. when trying to connect over SSH / TLS-enabled protocols. Is there any way to check which process(es) is(/are) eating up all the entropy? Edit: What I tried: Adding additional entropy sources: time_entropyd, rng-tools feeding urandom back into random, pseudorandom file accesses – netted about 1 MiB additional entropy per second, problems still persisted Checking for unusual activity via lsof, netstat and tcpdump – nothing. No noticeable load or anything Stopping daemons, restarting permanent sessions, rebooting the entire VM – no change in behaviour What in the end worked: Waiting. Since about yesterday noon, there are no connection problems anymore. Entropy is still somewhat low (128 Bytes peak), but TLS/SSH sessions have no noticeable delay anymore. I'm slowly switching our clients back to TLS (all five of them!), but I don't expect any change in behavior now.

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  • Gathering entropy in web apps to create (more) secure random numbers

    - by H M
    after several days of research and discussion i came up with this method to gather entropy from visitors (u can see the history of my research here) when a user visits i run this code: $entropy=sha1(microtime().$pepper.$_SERVER['REMOTE_ADDR'].$_SERVER['REMOTE_PORT']. $_SERVER['HTTP_USER_AGENT'].serialize($_POST).serialize($_GET).serialize($_COOKIE)); note: pepper is a per site/setup random string set by hand. then i execute the following (My)SQL query: $query="update `crypto` set `value`=sha1(concat(`value`, '$entropy')) where name='entropy'"; that means we combine the entropy of the visitor's request with the others' gathered already. that's all. then when we want to generate random numbers we combine the gathered entropy with the output: $query="select `value` from `crypto` where `name`='entropy'"; //... extract(unpack('Nrandom', pack('H*', sha1(mt_rand(0, 0x7FFFFFFF).$entropy.microtime())))); note: the last line is a part of a modified version of the crypt_rand function of the phpseclib. please tell me your opinion about the scheme and other ideas/info regarding entropy gathering/random number generation. ps: i know about randomness sources like /dev/urandom. this system is just an auxiliary system or (when we don't have (access to) these sources) a fallback scheme.

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  • Munin "Available entropy" when using address space layout randomization

    - by clawspoon
    Having just configured Munin for statistics logging on my gentoo server (hardened profile), I am noticing that my "Available entropy" is consitently in the 200-300 range. This seems way to low, so I checked it manually using the command $ cat /proc/sys/kernel/random/entropy_avail 3544 Odd. Consistently very low values in Munin and practically filled up when checking manually. After thinking about the problem for a while I came to the conclusion that the problem is probably that I'm using Adress Space Layout Randomization which is using the entropy when running commands/programs. Since Munin runs a whole slew of programs all the entropy is used up, and Munin then measures how much entropy there is, resulting in the low values. Does anyone have any experience with this? How can this be avoided?

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  • Munin "Available entropy" when using address space layout randomization

    - by clawspoon
    Having just configured munin for statistics logging on my gentoo server (hardened profile), I am noticing that my "Available entropy" is consitently in the 200-300 range. This seems way to low, so I checked it manually using the command $ cat /proc/sys/kernel/random/entropy_avail 3544 Odd. Consistently very low values in Munin and practically filled up when checking manually. After thinking about the problem for a while I came to the conclusion that the problem is probably that I'm using Adress Space Layout Randomization which is using the entropy when running commands/programs. Since Munin runs a whole slew of programs all the entropy is used up, and Munin then measures how much entropy there is, resulting in the low values. Does anyone have any experience with this? How can this be avoided?

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  • Feeding the kernels entropy source from other machines and/or increasing its maximum size

    - by David Spillett
    We have has a little trouble with a small box that acts as a VPN end-point and mail relay for our network, caused by the available entropy for /dev/random being too low (which causes TLS connection attempts by exim to fail). The machine doesn't do anything else, so the normal feed into the entropy pool (interrupt timings from things like disk access) is not enough. As a quick hack I've set a looping script that reads from /dev/hda at a couple of Mbyte/sec which keeps it topped up. Other than buying a hardware RNG, is there a clean way of piping data for entry from elsewhere, such as a copy of the data our file server uses for its entropy source? I've spotted several tips for using rng-tools to feed it from /dev/urandom on the same machine but that "feels dirty". Also, is it possible to increase the maximum pool size? It currently seems to max out at 3585.

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  • Interpreting Munin graphs showing available entropy and MySQL slow queries in sync

    - by user64204
    We're experiencing performance issues on our website, and after reviewing our munin graphs, the only metrics we've found in sync are Available entropy and MySQL slow queries, with the latter influenced by our number of logged in users: Based on the wikipedia entropy page, my understanding is that entropy is the amount of randomness (here measured in bytes) that the system can use for various tasks, mainly cryptography and functions that require random input. Since the peaks in available entropy and MySQL slow queries are occurring in sync and at regular interval, that the number of MySQL slow queries is proportional to our number of Drupal users whereas the peaks in available entropy seem to be much more constant and less proportional to these 2 metrics, we're thinking available entropy is the reflect of a root cause which, combined with the traffic to our website, is causing those slow queries (and not the opposite, slow queries influencing the entropy). Accordingly: Q: What underlying problem do you think could cause regular peaks in available entropy that could have an influence on MySQL's ability to process queries?

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  • Securely Storing Optional Entropy While Using DPAPI

    - by Changeling
    So I am trying to store the symmetric key using DPAPI. All is well and great, but what to do with the entropy? This answered question here really doesn't provide enough insight. It seems like a slippery slope - I could use the machine store to store the entropy but then what prevents someone from getting at that as well? Note: I am storing the current key using the User Scope. So my question is - what is the best way to store the entropy using DPAPI?

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  • Mutual Information / Entropy Calculation Help

    - by Fillip
    Hi, Hoping someone can give me some pointers with this entropy problem. Say X is chosen randomly from the uniform integer distribution 0-32 (inclusive). I calculate the entropy, H(X) = 32 bits, as each Xi has equal probability of occurring. Now, say the following pseudocode executes. int r = rand(0,1); // a random integer 0 or 1 r = r * 33 + X; How would I work out the mutual information between the two variables r and X? Mutual Information is defined as I(X; Y) = H(X) - H(X|Y) but I don't really understand how to apply the conditional entropy H(X|Y) to this problem. Thanks

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  • DPAPI + Entropy

    - by TWith2Sugars
    We have a WPF app that allows our users to download encrypted content and we want to provide the ability to decrypt this content off-line. The idea is to download the keys and store them using the DPAPI but I'm having trouble with the entropy parameter. Is there any way to generate an entropy to consistently use for the DPAPI functions with out hardcoding/storing them? Thanks Tony

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  • How do I compute the approximate entropy of a bit string?

    - by dreeves
    Is there a standard way to do this? Googling -- "approximate entropy" bits -- uncovers multiple academic papers but I'd like to just find a chunk of pseudocode defining the approximate entropy for a given bit string of arbitrary length. (In case this is easier said than done and it depends on the application, my application involves 16,320 bits of encrypted data (cyphertext). But encrypted as a puzzle and not meant to be impossible to crack. I thought I'd first check the entropy but couldn't easily find a good definition of such. So it seemed like a question that ought to be on StackOverflow! Ideas for where to begin with de-cyphering 16k random-seeming bits are also welcome...) See also this related question: http://stackoverflow.com/questions/510412/what-is-the-computer-science-definition-of-entropy

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  • Munin "Available entropy" when using adress space layout randomization

    - by clawspoon
    Having just configured munin for statistics logging on my gentoo server (hardened profile), I am noticing that my "Available entropy" is consitently in the 200-300 range. This seems way to low, so I checked it manually using the command $ cat /proc/sys/kernel/random/entropy_avail 3544 Odd. Consistently very low values in Munin and practically filled up when checking manually. After thinking about the problem for a while I came to the conclusion that the problem is probably that I'm using Adress Space Layout Randomization which is using the entropy when running commands/programs. Since Munin runs a whole slew of programs all the entropy is used up, and Munin then measures how much entropy there is, resulting in the low values. Does anyone have any experience with this? How can this be avoided?

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  • Scuttlebutt Reconciliation in the paper “Efficient Reconciliation and Flow Control for Anti-Entropy Protocols”

    - by soulmachine
    I am reading the paper "Efficient Reconciliation and Flow Control for Anti-Entropy Protocols"! , I couldn't clearly understand Section 3.2 "Scuttlebutt Reconciliation". Here I extract out some sentences from the paper, which especially confuse me. If gossip messages were unlimited in size, then the sets contains the exact differences,just like with precise reconciliation. Scuttlebutt requires that if a certain delta (r; k; v; n) is omitted, then all the deltas with higher version numbers for the same r should be omitted as well. Scuttlebutt satises the global invariant C(p;q) for any two processes p and q:

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  • Scuttlebutt Reconciliation from "Efficient Reconciliation and Flow Control for Anti-Entropy Protocols"

    - by Maus
    This question might be more suited to math.stackexchange.com, but here goes: Their Version Reconciliation takes two parts-- first the exchange of digests, and then an exchange of updates. I'll first paraphrase the paper's description of each step. To exchange digests, two peers send one another a set of pairs-- (peer, max_version) for each peer in the network, and then each one responds with a set of deltas. The deltas look like: (peer, key, value, version), for all tuples for which peer's state maps the key to the given value and version, and the version number is greater than the maximum version number peer has seen. This seems to require that each node remember the state of each other node, and the highest version number and ID each node has seen. Question Why must we iterate through all peers to exchange information between p and q?

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  • How can I estimate the entropy of a password?

    - by Wug
    Having read various resources about password strength I'm trying to create an algorithm that will provide a rough estimation of how much entropy a password has. I'm trying to create an algorithm that's as comprehensive as possible. At this point I only have pseudocode, but the algorithm covers the following: password length repeated characters patterns (logical) different character spaces (LC, UC, Numeric, Special, Extended) dictionary attacks It does NOT cover the following, and SHOULD cover it WELL (though not perfectly): ordering (passwords can be strictly ordered by output of this algorithm) patterns (spatial) Can anyone provide some insight on what this algorithm might be weak to? Specifically, can anyone think of situations where feeding a password to the algorithm would OVERESTIMATE its strength? Underestimations are less of an issue. The algorithm: // the password to test password = ? length = length(password) // unique character counts from password (duplicates discarded) uqlca = number of unique lowercase alphabetic characters in password uquca = number of uppercase alphabetic characters uqd = number of unique digits uqsp = number of unique special characters (anything with a key on the keyboard) uqxc = number of unique special special characters (alt codes, extended-ascii stuff) // algorithm parameters, total sizes of alphabet spaces Nlca = total possible number of lowercase letters (26) Nuca = total uppercase letters (26) Nd = total digits (10) Nsp = total special characters (32 or something) Nxc = total extended ascii characters that dont fit into other categorys (idk, 50?) // algorithm parameters, pw strength growth rates as percentages (per character) flca = entropy growth factor for lowercase letters (.25 is probably a good value) fuca = EGF for uppercase letters (.4 is probably good) fd = EGF for digits (.4 is probably good) fsp = EGF for special chars (.5 is probably good) fxc = EGF for extended ascii chars (.75 is probably good) // repetition factors. few unique letters == low factor, many unique == high rflca = (1 - (1 - flca) ^ uqlca) rfuca = (1 - (1 - fuca) ^ uquca) rfd = (1 - (1 - fd ) ^ uqd ) rfsp = (1 - (1 - fsp ) ^ uqsp ) rfxc = (1 - (1 - fxc ) ^ uqxc ) // digit strengths strength = ( rflca * Nlca + rfuca * Nuca + rfd * Nd + rfsp * Nsp + rfxc * Nxc ) ^ length entropybits = log_base_2(strength) A few inputs and their desired and actual entropy_bits outputs: INPUT DESIRED ACTUAL aaa very pathetic 8.1 aaaaaaaaa pathetic 24.7 abcdefghi weak 31.2 H0ley$Mol3y_ strong 72.2 s^fU¬5ü;y34G< wtf 88.9 [a^36]* pathetic 97.2 [a^20]A[a^15]* strong 146.8 xkcd1** medium 79.3 xkcd2** wtf 160.5 * these 2 passwords use shortened notation, where [a^N] expands to N a's. ** xkcd1 = "Tr0ub4dor&3", xkcd2 = "correct horse battery staple" The algorithm does realize (correctly) that increasing the alphabet size (even by one digit) vastly strengthens long passwords, as shown by the difference in entropy_bits for the 6th and 7th passwords, which both consist of 36 a's, but the second's 21st a is capitalized. However, they do not account for the fact that having a password of 36 a's is not a good idea, it's easily broken with a weak password cracker (and anyone who watches you type it will see it) and the algorithm doesn't reflect that. It does, however, reflect the fact that xkcd1 is a weak password compared to xkcd2, despite having greater complexity density (is this even a thing?). How can I improve this algorithm? Addendum 1 Dictionary attacks and pattern based attacks seem to be the big thing, so I'll take a stab at addressing those. I could perform a comprehensive search through the password for words from a word list and replace words with tokens unique to the words they represent. Word-tokens would then be treated as characters and have their own weight system, and would add their own weights to the password. I'd need a few new algorithm parameters (I'll call them lw, Nw ~= 2^11, fw ~= .5, and rfw) and I'd factor the weight into the password as I would any of the other weights. This word search could be specially modified to match both lowercase and uppercase letters as well as common character substitutions, like that of E with 3. If I didn't add extra weight to such matched words, the algorithm would underestimate their strength by a bit or two per word, which is OK. Otherwise, a general rule would be, for each non-perfect character match, give the word a bonus bit. I could then perform simple pattern checks, such as searches for runs of repeated characters and derivative tests (take the difference between each character), which would identify patterns such as 'aaaaa' and '12345', and replace each detected pattern with a pattern token, unique to the pattern and length. The algorithmic parameters (specifically, entropy per pattern) could be generated on the fly based on the pattern. At this point, I'd take the length of the password. Each word token and pattern token would count as one character; each token would replace the characters they symbolically represented. I made up some sort of pattern notation, but it includes the pattern length l, the pattern order o, and the base element b. This information could be used to compute some arbitrary weight for each pattern. I'd do something better in actual code. Modified Example: Password: 1234kitty$$$$$herpderp Tokenized: 1 2 3 4 k i t t y $ $ $ $ $ h e r p d e r p Words Filtered: 1 2 3 4 @W5783 $ $ $ $ $ @W9001 @W9002 Patterns Filtered: @P[l=4,o=1,b='1'] @W5783 @P[l=5,o=0,b='$'] @W9001 @W9002 Breakdown: 3 small, unique words and 2 patterns Entropy: about 45 bits, as per modified algorithm Password: correcthorsebatterystaple Tokenized: c o r r e c t h o r s e b a t t e r y s t a p l e Words Filtered: @W6783 @W7923 @W1535 @W2285 Breakdown: 4 small, unique words and no patterns Entropy: 43 bits, as per modified algorithm The exact semantics of how entropy is calculated from patterns is up for discussion. I was thinking something like: entropy(b) * l * (o + 1) // o will be either zero or one The modified algorithm would find flaws with and reduce the strength of each password in the original table, with the exception of s^fU¬5ü;y34G<, which contains no words or patterns.

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  • Information Gain and Entropy

    - by dhorn
    I recently read this question regarding information gain and entropy. I think I have a semi-decent grasp on the main idea, but I'm curious as what to do with situations such as follows: If we have a bag of 7 coins, 1 of which is heavier than the others, and 1 of which is lighter than the others, and we know the heavier coin + the lighter coin is the same as 2 normal coins, what is the information gain associated with picking two random coins and weighing them against each other? Our goal here is to identify the two odd coins. I've been thinking this problem over for a while, and can't frame it correctly in a decision tree, or any other way for that matter. Any help? EDIT: I understand the formula for entropy and the formula for information gain. What I don't understand is how to frame this problem in a decision tree format. EDIT 2: Here is where I'm at so far: Assuming we pick two coins and they both end up weighing the same, we can assume our new chances of picking H+L come out to 1/5 * 1/4 = 1/20 , easy enough. Assuming we pick two coins and the left side is heavier. There are three different cases where this can occur: HM: Which gives us 1/2 chance of picking H and a 1/4 chance of picking L: 1/8 HL: 1/2 chance of picking high, 1/1 chance of picking low: 1/1 ML: 1/2 chance of picking low, 1/4 chance of picking high: 1/8 However, the odds of us picking HM are 1/7 * 5/6 which is 5/42 The odds of us picking HL are 1/7 * 1/6 which is 1/42 And the odds of us picking ML are 1/7 * 5/6 which is 5/42 If we weight the overall probabilities with these odds, we are given: (1/8) * (5/42) + (1/1) * (1/42) + (1/8) * (5/42) = 3/56. The same holds true for option B. option A = 3/56 option B = 3/56 option C = 1/20 However, option C should be weighted heavier because there is a 5/7 * 4/6 chance to pick two mediums. So I'm assuming from here I weight THOSE odds. I am pretty sure I've messed up somewhere along the way, but I think I'm on the right path! EDIT 3: More stuff. Assuming the scale is unbalanced, the odds are (10/11) that only one of the coins is the H or L coin, and (1/11) that both coins are H/L Therefore we can conclude: (10 / 11) * (1/2 * 1/5) and (1 / 11) * (1/2) EDIT 4: Going to go ahead and say that it is a total 4/42 increase.

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  • Entropy using Decision Tree's

    - by Matt Clements
    Train a decision tree on the data represented by attributes A1, A2, A3 and outcome C described below: A1 A2 A3 C 1 0 1 0 0 1 1 1 0 0 1 0 For log2(1/3) = 1.6 and log2(2/3) = 0.6, answer the following questions: a) What is the value of entropy H for the given set of training example? b) What is the portion of the positive samples split by attribute A2? c) What is the value of information gain, G(A2), of attribute A2? d) What is IFTHEN rule(s) for the decision tree?

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  • are projects with high developer turn over rate really a bad thing?

    - by John
    I've inherited a lot of web projects that experienced high developer turn over rates. Sometimes these web projects are a horrible patchwork of band aid solutions. Other times they can be somewhat maintainable mozaics of half-done features each built with a different architectural style. Everytime I inherit these projects, I wish the previous developers could explain to me why things got so bad. What puzzles me is the reaction of the owners (either a manager, a middle man company, or a client). They seem to think, "Well, if you leave, I'll just find another developer." Or they think, "Oh, it costs that much money to refactor the system? I know another developer who can do it at half the price. I'll hire him if I can't afford you." I'm guessing that the high developer turn over rate is related to the owner's mentality of "If you think it's a bad idea to build this, I'll just find another (possibly cheaper) developer to do what I want". For the owners, the approach seems to work because their business is thriving. Unfortunately, it's no fun for the developers that go AWOL 3-4 months after working with poor code, strict timelines, and little feedback. So my question is the following: Are the following symptoms of a project really such a bad thing for business? high developer turn over rate poorly built technology - often a patchwork of different and inappropriately used architectural styles owners without a clear roadmap for their web project, and they request features on a whim I've seen numerous businesses prosper while experiencing the symptoms above. So as a programmer, even though my instincts tell me the above points are terrible, I'm forced to take a step back and ask, "are things really that bad in the grand scheme of things?" If not, I will re-evaluate my approach to these projects.

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  • How to generate a number in arbitrary range using random()={0..1} preserving uniformness and density?

    - by psihodelia
    Generate a random number in range [x..y] where x and y are any arbitrary floating point numbers. Use function random(), which returns a random floating point number in range [0..1] from P uniformly distributed numbers (call it "density"). Uniform distribution must be preserved and P must be scaled as well. I think, there is no easy solution for such problem. To simplify it a bit, I ask you how to generate a number in interval [-0.5 .. 0.5], then in [0 .. 2], then in [-2 .. 0], preserving uniformness and density? Thus, for [0 .. 2] it must generate a random number from P*2 uniformly distributed numbers. The obvious simple solution random() * (x - y) + y will generate not all possible numbers because of the lower density for all abs(x-y)>1.0 cases. Many possible values will be missed. Remember, that random() returns only a number from P possible numbers. Then, if you multiply such number by Q, it will give you only one of P possible values, scaled by Q, but you have to scale density P by Q as well.

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  • Parsing JPEG file format: Format of entropy-coded segment (ECS) segments?

    - by me2
    I'm having difficulty understanding the ITU-T T.81 spec for the JPEG file format. Hopefully someone else here has tried to parse JPEG files and/or knows about the details of this file format. The spec indiates that the ECS0 segment starts after the SOS segment but I can't find where in the spec it actually talks about the format of the ECS0 segment or how do detect its start. Simple JPEG implementations online are of limited help because they assume many things about the JPEGs they parse. Can anyone point me in the right direction?

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  • Parsing JPEG file format: Format of entropy-coded segments (ECS) ?

    - by me2
    I'm having difficulty understanding the ITU-T T.81 spec for the JPEG file format. Hopefully someone else here has tried to parse JPEG files and/or knows about the details of this file format. The spec indiates that the ECS0 segment starts after the SOS segment but I can't find where in the spec it actually talks about the format of the ECS0 segment or how do detect its start. Simple JPEG implementations online are of limited help because they assume many things about the JPEGs they parse. Can anyone point me in the right direction? FYI: The JPEG file format spec is here.

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