Maowei Yang

Bio: Maowei Yang is an academic researcher from Sichuan University. The author has contributed to research in topics: Password & Dictionary attack. The author has an hindex of 3, co-authored 3 publications receiving 106 citations.

Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems

Abstract: Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.

Security Analyses of Click-based Graphical Passwords via Image Point Memorability

Abstract: We propose a novel concept and a model of image point memorability (IPM) for analyzing click-based graphical passwords that have been studied extensively in both the security and HCI communities. In our model, each point in an image is associated with a numeric index that indicates the point's memorability level. This index can be approximated either by automatic computer vision algorithms or via human assistance. Using our model, we can rank-order image points by their relative memorability with a decent accuracy. We show that the IPM model has both defensive and offensive applications. On the one hand, we apply the model to generate high-quality graphical honeywords. This is the first work on honeywords for graphical passwords, whereas all previous methods are only for generating text honeywords and thus inapplicable. On the other hand, we use the IPM model to develop the first successful dictionary attacks on Persuasive Cued Click Points (PCCP), which is the state-of-the-art click-based graphical password scheme and robust to all prior dictionary attacks. We show that the probability distribution of PCCP passwords is seriously biased when it is examined with the lens of the IPM model. Although PCCP was designed to generate random passwords, its effective password space as we measured can be as small as 30.58 bits, which is substantially weaker than its theoretical and commonly believed strength (43 bits). The IPM model is applicable to all click-based graphical password schemes, and our analyses can be extended to other graphical passwords as well.

Security implications of password discretization for click-based graphical passwords

Abstract: Discretization is a standard technique used in click-based graphical passwords for tolerating input variance so that approximately correct passwords are accepted by the system. In this paper, we show for the first time that two representative discretization schemes leak a significant amount of password information, undermining the security of such graphical passwords. We exploit such information leakage for successful dictionary attacks on Persuasive Cued Click Points (PCCP), which is to date the most secure click-based graphical password scheme and was considered to be resistant to such attacks. In our experiments, our purely automated attack successfully guessed 69.2% of the passwords when Centered Discretization was used to implement PCCP, and 39.4% of the passwords when Robust Discretization was used. Each attack dictionary we used was of approximately 235 entries, whereas the full password space was of 243 entries. For Centered Discretization, our attack still successfully guessed 50% of the passwords when the dictionary size was reduced to approximately 230 entries. Our attack is also applicable to common implementations of other click-based graphical password systems such as PassPoints and Cued Click Points -- both have been extensively studied in the research communities.

Zipf’s Law in Passwords

Abstract: Despite three decades of intensive research efforts, it remains an open question as to what is the underlying distribution of user-generated passwords. In this paper, we make a substantial step forward toward understanding this foundational question. By introducing a number of computational statistical techniques and based on 14 large-scale data sets, which consist of 113.3 million real-world passwords, we, for the first time, propose two Zipf-like models (i.e., PDF-Zipf and CDF-Zipf) to characterize the distribution of passwords. More specifically, our PDF-Zipf model can well fit the popular passwords and obtain a coefficient of determination larger than 0.97; our CDF-Zipf model can well fit the entire password data set, with the maximum cumulative distribution function (CDF) deviation between the empirical distribution and the fitted theoretical model being 0.49%~4.59% (on an average 1.85%). With the concrete knowledge of password distributions, we suggest a new metric for measuring the strength of password data sets. Extensive experimental results show the effectiveness and general applicability of the proposed Zipf-like models and security metric.

Intercrossed Access Controls for Secure Financial Services on Multimedia Big Data in Cloud Systems

Abstract: The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm. We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.

Adaptive artificial immune networks for mitigating DoS flooding attacks

Abstract: Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD’99, CAIDA’07 and CAIDA’08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

A Security Analysis of Honeywords.

Abstract: Honeywords are decoy passwords associated with each user account, and they contribute a promising approach to detecting password leakage. This approach was first proposed by Juels and Rivest at CCS’13, and has been covered by hundreds of medias and also adopted in various research domains. The idea of honeywords looks deceptively simple, but it is a deep and sophisticated challenge to automatically generate honeywords that are hard to differentiate from real passwords. In JuelsRivest’s work, four main honeyword-generation methods are suggested but only justified by heuristic security arguments. In this work, we for the first time develop a series of practical experiments using 10 large-scale datasets, a total of 104 million real-world passwords, to quantitatively evaluate the security that these four methods can provide. Our results reveal that they all fail to provide the expected security: real passwords can be distinguished with a success rate of 29.29%∼32.62% by our basic trawling-guessing attacker, but not the expected 5%, with just one guess (when each user account is associated with 19 honeywords as recommended). This figure reaches 34.21%∼49.02% under the advanced trawling-guessing attackers who make use of various state-of-the-art probabilistic password models. We further evaluate the security of Juels-Rivest’s methods under a targeted-guessing attacker who can exploit the victim’ personal information, and the results are even more alarming: 56.81%∼67.98%. Overall, our work resolves three open problems in honeyword research, as defined by Juels and Rivest.