Abstract-Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.
Text-based password schemes have inherent security and usability problems, leading to the development of graphical password schemes. However, most of these alternate schemes are vulnerable to spyware attacks. We propose a new scheme, using CAPTCHA (Completely Automated Public Turing tests to tell Computers and Humans Apart) that retaining the advantages of graphical password schemes, while simultaneously raising the cost of adversaries by orders of magnitude. Furthermore, some primary experiments are conducted and the results indicate that the usability should be improved in the future work
Abstract-Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.
Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, most of these alternate schemes have their own disadvantages. For example, cued-recall graphical password schemes are vulnerable to shoulder-surfing and cannot prevent intersection analysis attack. A novel cued-recall graphical password scheme CBFG (Click Buttons according to Figures in Grids) is proposed in this paper. Inheriting the way of setting password in traditional cued-recall scheme, this scheme is also added the ideology of image identification. CBFG helps users tend to set their passwords more complex. Simultaneously, it has the capability against shoulder surfing attack and intersection analysis attack. Experiments illustrate that CBFG has better performance in usability, especially in security.
[Purpose/significance] By analyzing the emotional evolution of Weibo users after emergencies, we can find out the law and potential risks of public opinion evolution and provide instruction for the government to control and guide network public opinion. [Method/process] We proposed an analysis model of public opinion evolution based on Emotional Analysis and GBRT. With the help of Python, a web crawler was developed to collect Weibo comments. After that, a Naive Bayesian Classifier was used for emotional analysis. According to public emotion and the number of comments, we divided the evolution process into fever period, persistence period, incubation period and extinction period. Statistical and visualization methods were used to study the evolution characteristics of word cloud, emotional tendency and age groups. Finally, correlation analysis and GBRT were used to predict each individual’s emotions. [Results/conclusion] Taking the dangerous chemical explosion accident in Tianjin as an example, we can validate our model. Results shows that the model can reasonably divide the evolutionary stages, find out the law of public opinion evolution in different stages, and accurately predict users’ emotional tendency.
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