CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions

The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and data are strong defense systems against malicious bots. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication process to confirm that the user is a human hence, access is granted. This paper provides an in-depth survey on CAPTCHAs and focuses on two main things: (1) a detailed discussion on various CAPTCHA types along with their advantages, disadvantages, and design recommendations, and (2) an in-depth analysis of different CAPTCHA breaking techniques. The survey is based on over two hundred studies on the subject matter conducted since 2003 to date. The analysis reinforces the need to design more attack-resistant CAPTCHAs while keeping their usability intact. The paper also highlights the design challenges and open issues related to CAPTCHAs. Furthermore, it also provides useful recommendations for breaking CAPTCHAs

Privacy-preserving, User-centric VoIP CAPTCHA Challenges: an Integrated Solution in the SIP Environment

Purpose – This work aims to argue that it is possible to address discrimination issues that naturally arise in contemporary audio CAPTCHA challenges and potentially enhance the effectiveness of audio CAPTCHA systems by adapting the challenges to the user characteristics. Design/methodology/approach – A prototype has been designed, called PrivCAPTCHA, to offer privacy-preserving, user-centric CAPTCHA challenges. Anonymous credential proofs are integrated into the Session Initiation Protocol (SIP) protocol and the approach is evaluated in a real-world Voice over Internet Protocol (VoIP) environment. Findings – The results of this work indicate that it is possible to create VoIP CAPTCHA services offering privacy-preserving, user-centric challenges while maintaining sufficient efficiency. Research limitations/implications – The proposed approach was evaluated through an experimental implementation to demonstrate its feasibility. Additional features, such as appropriate user interfaces and efficiency optimisations, would be useful for a commercial product. Security measures to protect the system from attacks against the SIP protocol would be useful to counteract the effects of the introduced overhead. Future research could investigate the use of this approach on non-audio CAPTCHA services. Practical implications – PrivCAPTCHA is expected to achieve fairer, non-discriminating CAPTCHA services while protecting the user’s privacy. Adoption success relies upon the general need for employment of privacy-preserving practices in electronic interactions. Social implications – This approach is expected to enhance the quality of life of users, who will now receive CAPTCHA challenges closer to their characteristics. This applies especially to users with disabilities. Additionally, as a privacy-preserving service, this approach is expected to increase trust during the use of services that use it. Originality/value – To the best of authors’ knowledge, this is the first comprehensive proposal for privacy-preserving CAPTCHA challenge adaptation. The proposed system aims at providing an improved CAPTCHA service that is more appropriate for and trusted by human users

Enhancing Web Browsing Security

Web browsing has become an integral part of our lives, and we use browsers to perform many important activities almost everyday and everywhere. However, due to the vulnerabilities in Web browsers and Web applications and also due to Web users\u27 lack of security knowledge, browser-based attacks are rampant over the Internet and have caused substantial damage to both Web users and service providers. Enhancing Web browsing security is therefore of great need and importance.;This dissertation concentrates on enhancing the Web browsing security through exploring and experimenting with new approaches and software systems. Specifically, we have systematically studied four challenging Web browsing security problems: HTTP cookie management, phishing, insecure JavaScript practices, and browsing on untrusted public computers. We have proposed new approaches to address these problems, and built unique systems to validate our approaches.;To manage HTTP cookies, we have proposed an approach to automatically validate the usefulness of HTTP cookies at the client-side on behalf of users. By automatically removing useless cookies, our approach helps a user to strike an appropriate balance between maximizing usability and minimizing security risks. to protect against phishing attacks, we have proposed an approach to transparently feed a relatively large number of bogus credentials into a suspected phishing site. Using those bogus credentials, our approach conceals victims\u27 real credentials and enables a legitimate website to identify stolen credentials in a timely manner. to identify insecure JavaScript practices, we have proposed an execution-based measurement approach and performed a large-scale measurement study. Our work sheds light on the insecure JavaScript practices and especially reveals the severity and nature of insecure JavaScript inclusion and dynamic generation practices on the Web. to achieve secure and convenient Web browsing on untrusted public computers, we have proposed a simple approach that enables an extended browser on a mobile device and a regular browser on a public computer to collaboratively support a Web session. A user can securely perform sensitive interactions on the mobile device and conveniently perform other browsing interactions on the public computer

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks

Mothers\u27 Adaptation to Caring for a New Baby

To date, most research on parents\u27 adjustment after adding a new baby to their family unit has focused on mothers\u27 initial transition to parenthood. This past research has examined changes in mothers\u27 marital satisfaction and perceived well-being across the transition, and has compared their prenatal expectations to their postnatal experiences. This project assessed first-time and experienced mothers\u27 stress and satisfaction associated with parenting, their adjustment to competing demands, and their perceived well-being longitudinally before and after the birth of a baby. Additionally, how maternal and child-related variables influenced the trajectory of mothers\u27 postnatal adaptation was assessed. These variables included mothers\u27 age, their education level, their prenatal expectations and postnatal experiences concerning shared infant care, their satisfaction with the division of infant caregiving, and their perceptions of their infant\u27s temperament. Mothers (N = 136) completed an online survey during their third trimester and additional online surveys when their baby was approximately 2, 4, 6, and 8 weeks old.;First-time mothers prenatally expected a more equal division of infant caregiving between themselves and their partners than did experienced mothers. Both first-time and experienced mothers reported less assistance from their partners than they had prenatally expected. Additionally, they experienced almost twice as many violated expectations than met expectations. Growth curve modeling revealed that a cubic function of time best fit the trajectory of mothers\u27 postnatal parenting satisfaction. Mothers reported less parenting satisfaction at 4 weeks, compared to 2 and 6 weeks, and reported stability in their satisfaction between 6 and 8 weeks. A quadratic function of time best fit the trajectories of mothers\u27 postnatal parenting stress and adjustment to the demands of their baby. Mothers reported more stress and difficulty adjusting to their baby\u27s demands at 4 and 6 weeks, compared to 2 and 8 weeks. A linear function of time best fit the trajectories of mothers\u27 adjustment to home demands, generalized state anxiety, and depressive symptoms. Mothers reported less difficulty meeting home demands, less generalized anxiety, and fewer depressive symptoms across the postnatal period. Mothers\u27 violated expectations were associated with level differences in all aspects of mothers\u27 postnatal adaptation except their adjustment to home demands. Specifically, more violated expectations, in number or in magnitude, were associated with poorer postnatal adaptation. Mothers\u27 violated expectations were not associated with the slope of mothers\u27 postnatal adaptation trajectories. Exploratory models revealed that other maternal and child-related variables also impacted the level and slope of mothers\u27 postnatal adaptation.;Overall, first-time and experienced mothers were more similar than different in regards to their postnatal adaptation. This study suggests that prior findings concerning adults\u27 initial transition to parenthood may also apply to adults during each addition of a new baby into the family unit. Additionally, mothers who reported less of a mismatch between their expectations and experiences concerning shared infant care had fewer issues adapting the postnatal period. Thus, methods to increase the assistance mothers receive from their partner should be sought. Limitations of this study and suggestions for future research are also discussed

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

The role of effort in security and privacy behaviours online

As more and more aspects of users’ lives go online, they can interact with each other, access services and purchase goods with unprecedented convenience and speed. However, this also means that users’ devices and data become more vulnerable to attacks. As security is often added to tools and services as an after-thought, it tends to be poorly integrated into the processes and part of the effort of securing is often offloaded onto the user. Users are goal-driven and they go online to get things done, protecting their security and privacy might therefore not be a priority. The six studies described in this dissertation examine the role of effort in users’ security and privacy behaviours online. First, two security studies use authentication diaries to examine the user effort required for authentication to organisational and online banking systems respectively. Second, two further studies are laboratory evaluations of proposed mechanisms for authentication and verification. Third, two privacy studies examine the role of effort in users’ information disclosure in webforms and evaluate a possible solution that could help users manage how much they disclose. All studies illustrate the different coping strategies users develop to manage their effort. They show that demanding too much effort can affect productivity, cause frustration and undermine the security these mechanisms were meant to offer. The work stresses the importance of conducting methodologically robust user evaluations of both proposed and deployed mechanisms in order to improve user satisfaction and their security and privacy

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection