Browsing by Author "Doğanaksoy, Ali"

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Caesar Yarışmasına Katılan Kimlik Denetimini Sağlayan Algoritmaların Kriptanalizi
(2017) Sulak, Fatih; Doğanaksoy, Ali
Günümüzde akıllı telefonlardan bankamatik kartlarına, internet alışverişinden kartlı geçiş sistemlerine kadar birçok yerde kimlik denetimi kullanılmaktadır. Yeterli güvenliğin sağlanması için kriptografik algoritmaların kullanılması kaçınılmazdır. Uzun analiz süreçleri sonunda belirlenen standart şifreleme algoritmaları ise şifreleme ile kimlik denetimini aynı anda yapamamaktadır. Hem kimlik denetimi yapan hem de güvenli şifreleme yapan bazı algoritmalar tasarlanmıştır. Ancak az sayıdaki bu algoritmalar, ayrıntılı şekilde analiz edilmemiş olduklarından güvenilirliklerine şüpheyle yaklaşılmaktadır. Daha önce AES ve SHA-3 te yapıldığı gibi kimlik denetimini sağlayan şifreleme algoritmaları için uluslararası bir standardın getirilmesi ihtiyacı vardır. Bu nedenle 2014 yılı başında başlamış ve 2017 yılı sonunda bitecek olan CAESAR yarışması düzenlenmektedir. Tamamen açık ve şeffaf yapılan bu yarışma sonucunda dünyadaki milyonlarca kullanıcının etkin, verimli ve güvenli olarak haberleşmesinde standart olarak kullanacağı algoritmalar belirlenecektir. Özellikle bankacılık gibi internetin çok etkin kullanıldığı alanlarda analizleri iyi yapılmış kuvvetli algoritmaların kullanılması kaçınılmazdır. Bu projede yarışmadaki zayıf algoritmaların bulunması ve zayıflıklarının diğer akademisyenlerle paylaşılması hedeflenmiştir. Bu hedeflerin büyük kısmına ulaşılmıştır.
Citation - WoS: 1
Citation - Scopus: 2
LS-14 Test Suite for Long Sequences
(Hacettepe Univ, Fac Sci, 2024) Akcengiz, Ziya; Aslan, Melis; Doğanaksoy, Ali; Sulak, Fatih; Uguz, Muhiddin
Random number sequences are used in many branches of science. Because of many techni- cal reasons and their practicality, pseudo random sequences are usually employed in place of true number sequences. Whether a sequence generated through a deterministic process is a pseudo random, in other words, random-looking sequence or it contains certain pat- terns, can be determined with the help of statistics and mathematics. Although, in the literature there are many statistical randomness tests for this purpose, there is no much work on test suites specialized for long sequences, that is sequences of length 1,000,000 bits or more. Most of the randomness tests for long sequences use some mathematical ap- proximations to compute expected values of the random variables and hence their results contain some errors. Another approach to evaluate randomness criteria of long sequences is to partition the long sequence into a collection short sequences and evaluate the collec- tion for the ran- domness using statistical goodness of fit tests. The main advantage of this approach is, as the individual sequences are short, there is no need to use mathematical approximations. On the other hand when the second approach is preferred, partition the long sequence into a collection of fixed length subsequences and this approach causes a loss of information in some cases. Hence the idea of dynamic partition should be included to perform a more reliable test suite. In this paper, we propose three new tests, namely the entire R2 run, dynamic saturation point, and dynamic run tests. Moreover, we in- troduce a new test suite, called LS-14, consisting of 14 tests to evaluate randomness of long sequences. As LS-14 employs all three approaches: testing the entire long sequence, testing the collection of fixed length partitions of it, and finally, testing the collection obtained by the dynamic partitions of it, the proposed LS-14 test suit differs from all existing suites. Mutual comparisons of all 14 tests in the LS-14 suite, with each other are computed. Moreover, results obtained from the proposed test suite and NIST SP800-22 suite are compared. Examples of sequences with certain patterns which are not observed by NIST SP800-22 suite but detected by the proposed test suite are given.
Modifications of Knuth Randomness Tests for Integer and Binary Sequences
(2018) Koçak, Onur; Sulak, Fatih; Doğanaksoy, Ali; Uğuz, Muhiddin
Generating random numbers and random sequences that are in-distinguishable from truly random sequences is an important task for cryptog-raphy. To measure the randomness, statistical randomness tests are applied tothe generated numbers and sequences. Knuth test suite is the one of the .rststatistical randomness suites. This suite, however, is mostly for real numbersequences and the parameters of the tests are not given explicitly.In this work, we review the tests in Knuth Test Suite. We give test para-meters in order for the tests to be applicable to integer and binary sequencesand make suggestions on the choice of these parameters. We clarify how theprobabilities used in the tests are calculated according to the parameters andprovide formulas to calculate the probabilities. Also, some tests, like Per-mutation Test and Max-of-t-test, are modi.ed so that the test can be usedto test integer sequences. Finally, we apply the suite on some widely usedcryptographic random number sources and present the results.
Mutual Correlation of Nist Statistical Randomness Tests and Comparison of Theirsensitivities on Transformed Sequences
(2017) Doğanaksoy, Ali; Sulak, Fatih; Uğuz, Muhiddin; Şeker, Okan; Akcengiz, Ziya
Random sequences are widely used in many cryptographic applications and hence their generation is oneof the main research areas in cryptography. Statistical randomness tests are introduced to detect the weaknesses ornonrandom characteristics that a sequence under consideration may have. In the literature, there exist various statisticalrandomness tests and test suites, de ned as a collection of tests. An efficient test suite should consist of a number ofuncorrelated statistical tests each of which measures randomness from another point of view. `Being uncorrelated\\' is nota well-de ned or well-understood concept in the literature. In this work, we apply Pearson\\'s correlation test to measurethe correlation between the tests.In addition, we de ne ve new methods for transforming a sequence. Our motivation is to detect those testswhose results are invariant under a certain transformation. To observe the correlation, we use two methods. One is thedirect correlation between the tests and the other is the correlation between the results of a test on the sequence andits transformed form. In light of the observations, we conclude that some of the tests are correlated with each other.Furthermore, we conclude that in designing a reliable and efficient suite we can avoid overpopulating the list of testfunctions by employing transformations together with a reasonable number of statistical test functions.
On the Independence of Statistical Randomness Tests Included in the Nist Test Suite
(2017) Sulak, Fatih; Uğuz, Muhiddin; Koçak, Onur; Doğanaksoy, Ali
Random numbers and random sequences are used to produce vital parts of cryptographic algorithms such as encryption keys and therefore the generation and evaluation of random sequences in terms of randomness are vital. Test suites consisting of a number of statistical randomness tests are used to detect the nonrandom characteristics of the sequences. Construction of a test suite is not an easy task. On one hand, the coverage of a suite should be wide; that is, it should compare the sequence under consideration from many different points of view with true random sequences. On the other hand, an overpopulated suite is expensive in terms of running time and computing power. Unfortunately, this trade-off is not addressed in detail in most of the suites in use. An efficient suite should avoid use of similar tests, while still containing sufficiently many. A single statistical test gives a measure for the randomness of the data. A collection of tests in a suite give a collection of measures. Obtaining a single value from this collection of measures is a difficult task and so far there is no conventional or strongly recommended method for this purpose. This work focuses on the evaluation of the randomness of data to give a uni ed result that considers all statistical information obtained from different tests in the suite. A natural starting point of research in this direction is to investigate correlations between test results and to study the independences of each from others. It is started with the concept of independence. As it is complicated enough to work even with one test function, theoretical investigation of dependence between many of them in terms of conditional probabilities is a much more difficult task. With this motivation, in this work it is tried to get some experimental results that may lead to theoretical results in future works. As experimental results may re ect properties of the data set under consideration, work is done on various types of large data sets hoping to get results that give clues about the theoretical results. For a collection of statistical randomness tests, the tests in the NIST test suite are considered. Tests in the NIST suite that can be applied to sequences shorter than 38,912 bits are analyzed. Based on the correlation of the tests at extreme values, the dependencies of the tests are found. Depending on the coverage of a test suite, a new concept, the coverage efficiency of a test suite, is de ned, and using this concept, the most efficient, the least efficient, and the optimal subsuites of the NIST suite are determined. Moreover, the marginal bene t of each test, which also helps one to understand the contribution of each individual test to the coverage efficiency of the NIST suite, is found. Furthermore, an efficient subsuite that contains ve statistical randomness tests is proposed.