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This study explored the relationship between successful guessing and latent ability in IRT models. A new IRT model was developed with a guessing function integrating probability of guessing an item correctly with the examinee's ability and the item parameters. The conventional 3PL IRT model was compared with the new 2PL-Guessing model on parameter estimation using the Monte Carlo method. SAS program was used to implement the data simulation and the maximum likelihood estimation. Compared with the traditional 3PL model, the new model should reflect: a) the maximum probability of guessing should not be more than 0.5, even for the highest ability examinees; b) different ability of examinees should have different probability of successful guessing, because a basic assumption for the new models is that higher ability examinees have a higher probability of successful guessing than lower ability examinees; c) smaller standard error in estimating parameters; and d) faster running time. The results illustrated that the new 2PL-Guessing model was superior to the 3PL model in all four aspects.

When given a multiple-choice test over unfamiliar material, students may score significantly above chance levels. This performance may be explained by prior knowledge of the material or by “test-wiseness,” determining the correct answer by using cues present in the test. Participants answered questions from an introductory psychology test-bank in two formats: a question stem with a single alternative and a traditional four alternative multiple-choice, reporting what sources of information they used to answer each question. For the single-alternative condition, participants had an accuracy of 42.2%, 17.2% higher than the base chance of 25%, with an average accuracy of 40.75% for the multiple-choice condition. Participants who stated they had previously learnt the material showed no significant difference in accuracy than those who stated they had guessed. These findings suggest that tests may have inflated scores which reflect test-wiseness and prior knowledge more than formal learning of the test materials.

This thesis examines some of the currently available programs for password guessing, in terms of designs and strengths. The programs Hashcat, OMEN, PassGAN, PCFG and PRINCE were tested for effectiveness, in a series of experiments similar to real-world attack scenarios. Those programs, as well as the program TarGuess, also had their design examined, in terms of the extent of how they use different important parameters. It was determined that most of the programs use different models to deal with password lists, in order to learn how new, similar, passwords should be generated. Hashcat, PCFG and PRINCE were found to be the most effective programs in the experiments, in terms of number of correct password guessed each second. Finally, a program for automated password guessing based on the results was built and implemented in the cyber range at the Swedish defence research agency.

Tidigare studier har visat att medelvärdet av en grupps gissningar på en faktabaserad fråga ofta blir närmre det rätta svaret än gissningarna var för sig. Det har visat sig att detta även kan appliceras på den enskilda individen genom att denne gör upprepade gissningar på samma fråga. Vidare har man sett att personer med lägre arbetsminnesspann gynnas mer av detta fenomen än personer med högre arbetsminnesspann. Syftet med vår studie är att undersöka huruvida dessa teorier kan appliceras på en grupp vuxna med utvecklingsstörning. I studien deltog 34 personer, 17 personer i försöksgruppen och 17 personer i en köns- och åldersmatchad kontrollgrupp. Ett gissningstest baserat på frågor om omvärldskunskap där svaren skall anges i siffror användes. Vidare utfördes tester för att undersöka mental ålder, språkförståelse samt två domäner av arbetsminne. Resultaten visar att både kontroll- och försöksgruppen gynnas av att ta medelvärdet av sina gissningar. Vi har dock sett att test av denna art är svåra att applicera på en grupp med utvecklingsstörning. Deras svar tycks inte spegla den förståelse de har för frågan och deras gissningar har en stor spridning både inom och över individerna. / Previous studies have shown that the average of guesses made by a group of people, on a question based on general knowledge, often is better than the single guess alone. This has been shown possible to apply on a single individual by producing multiple guesses. One study shows that individuals with low working memory spans benefit more from this phenomenon than individuals with a higher memory span. The aim of our study is to investigate whether these theories can be applied on a group of adults with an intellectual disability. In this study there were 34 participants, 17 subjects in a trial group and 17 subjects in a gender- and chronological age matched control group. A guessing test with questions based on world facts was used. We also conducted tests to investigate mental age, language comprehension and two different domains of working memory. The results show that both our trial group and control group benefits from taking the average of their guesses, although we have seen that this kind of test is difficult to apply on a group with intellectual disability. During the test situation individuals in the trial prove a certain understanding of the numerals, but the given guess does not always reflect this understanding. Also, their guesses are scattered, both within - and across individuals.

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No description available.

The problem of guessing a random string is studied. It arises in the analysis of the strength of secret-key cryptosystems against guessing attacks. Expected number of guesses, or more generally moments of the number of guesses needed to break the cryptosystem grow exponentially with the length of the string. This thesis studies the rate of exponential growth of these moments using the theory of large deviations. A closer elation between guessing and compression is first established. For systems with large key rates, it is shown that if the source’s sequence of so-called information spectrum random variables satisfies the large deviation property with a certain rate function, then the limiting guessing exponent exists and is a scalar multiple of the Legendre-Fenchel dual of the rate function. This is then used to rederive several prior results. The large deviations approach brings to light the relevance of information spectrum in determining guessing exponents. For systems with key-rate constraints, bounds are derived on the limiting guessing exponents for general sources. The obtained bounds are shown to be tight for stationary memoryless, Markov, and unifilar sources, thus recovering some known results. The bounds are obtained by establishing a close relationship between error exponents and correct decoding exponents for fixed rate source compression on the one hand and exponents for guessing moments on the other.

Online social networks are a natural part of the lives of today’s western youth. Media and information literacy is a central part of research in Library and Information science and until now there has always been more focus on these subjects in a learning context rather than in an every day life context. The aim of this study is to further examine how adolescent boys value and trust the information they receive via Twitter in their personal lives, and how this effects their view of their environment. The study was done with a qualitative method, where we conducted interviews with six boys who were all born in 1997. The results showed that they knew how to react critically to information on Twitter. Although none of the participants valued information on Twitter too seriously, they did feel that it kept them more updated than if they had not been using the social network.

Password security is one of the most critical aspects of IT security, as password-based authentication is still the primary authentication method. Unfortunately, our passwords are subject to different types of weaknesses and various types of password-guessing attacks. The first objective of this thesis is to provide a general perception of the trends in offline password-guessing tools, methods, and techniques. The study shows that the most cited tools are Hashcat, John the Ripper, Ordered Markov ENumerator (OMEN), and PassGan. Methods are increasingly evolving and becoming more sophisticated by emerging Deep Learning and Neural Networks. Unlike methods and tools, techniques are not subject to significant development, noting that dictionary and rule-based attacks are at the top of used techniques. The second objective of this thesis is to explore to what extent Swedish personal names are used in real-life passwords. Hence, an experiment is conducted for this purpose. The experiment results show that about 26% of Swedish users use their personal names when they create passwords, making them vulnerable to easy guessing by password-guessing tools. Furthermore, a simple analysis of the resulting password recovery file is performed in terms of password length and complexity. The resulting numbers show that more than half of guessed passwords are shorter than eight characters, indicating incompliance with the recommendations from standard organizations. In addition, results show a weak combination of letters, digits, and special characters, indicating that many Swedish users do not maintain sufficient diversity when composing their passwords. This means less password complexity, making passwords an easy target to guess. This study may serve as a quick reference to getting an overview of trends in the password-guessing field. On the other side, the resulting rate of Swedish personal names in Swedish password leaks may draw the attention of active social actors regarding information security to improve password security measures in Sweden. / Lösenordssäkerhet är en av de mest kritiska aspekterna av IT-säkerhet eftersom  lösenordsbaserad autentisering fortfarande är den viktigaste metoden för autentisering. Tyvärr är våra lösenord föremål för olika typer av svagheter och olika typer av lösenordsgissningsattacker. Det första syftet med detta arbete är att ge en allmän uppfattning om trenderna inom verktyg,metoder och tekniker angående offline lösenordsgissning. Studien visar att Hashcat, John the Ripper, Ordered Markov ENumerator OMEN och PassGan är de mest citerade verktygen. Medan metoderna alltmer utvecklas och blir mer sofistikerade genom framväxande “DeepLearning”, och “Neural Networks”. Till skillnad från metoder och verktyg är tekniker inte föremål för stor utveckling, och notera att “dictionary” attacker och “rule-based” attacker är överst bland använda tekniker. Det andra syftet är att utforska i vilken utsträckning svenska personnamn används i verkliga lösenord. Därför genomförs ett experiment för detta ändamål. Resultaten av experimentet visar att cirka 26 % av svenska användare använder sina personnamn när de skapar lösenord, vilket gör lösenord sårbara för enkel gissning med hjälp av lösenordsgissningsverktyg. Dessutom utförs en enkel analys av den resulterande lösenordsåterställningsfilen vad gäller lösenordslängd och komplexitet. De resulterande siffrorna visar att mer än hälften av de gissade lösenorden är kortare än åtta tecken, vilket är en indikation på att de inte följer rekommendationerna från standardorganisationer. Resultaten visar också en svag kombination av bokstäver, siffror och specialtecken vilket indikerar att många svenskar inte upprätthåller tillräcklig variation när de komponerar sina lösenord. Detta innebär mindre lösenordskomplexitet, vilket gör lösenord till ett mål för enkel gissning. Arbetet kan fungera som en snabbreferens för att få en överblick över trender inom lösenordsgissningsfältet. Å andra sidan kan den resulterande andelen svenska personnamn i  svenska lösenordsläckor uppmärksamma de aktiva aktörerna i samhället gällande informationssäkerhet för att förbättra lösenordssäkerhetsåtgärderna i Sverige.