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Abstract CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.

This paper defines new classes of algorithms for securing and sharing visual information. Algorithms offering data protection against unauthorised access are cryptographic protocols for data sharing and splitting. These protocols ensure the division of information among a trusted group of secret holders, with every protocol participant being allocated a specified number of shares in the executed algorithm. Proposing and defining new solutions in the field of cryptographic algorithms for data sharing constitutes the main topic of this paper. This paper discusses a new class of algorithms for secret sharing with the use of linguistic formalisms dedicated to the processes of meaning interpretation and linguistic data sharing. Linguistic threshold schemes serve the processes of data protection in distributed systems; they are also used to distribute the shared secret parts in an optimum way, and to perform the meaning analysis and interpretation of various data sets. Semantic analysis as an element of the impact assessment of the meaning of the interpreted and analysed data will make it possible to take into consideration a much wider aspect of description and interpretation of the analysed phenomenon or data set; it will also enable the assessment of the core of the characterised sets in respect to other information with related meaning. The proposed protocols enhance the security of shared data, and allow the generation of any number of secret shares, which is greater than traditional secret sharing methods.

In recent years, the number of personal accounts assigned to one business user has been constantly growing. There could be as many as 191 individual login credentials used by an average employee, according to a 2017 study. The most recurrent problems associated with this situation faced by users are the strength of passwords and ability to recall them. Researchers have proven that “users are aware of what constitutes a secure password but may forgo these security measures in terms of more convenient passwords, largely depending on account type”. Reusing the same password across multiple platforms or creating one with dictionary words has also been proved to be a common practice amongst many. In this paper, a novel password-reminder scheme will be presented. The goal was that the user creates a CAPTCHA-like image with a hidden meaning, that only he or she can decode. The image must be in some way related to that individual’s memory or her/his unique knowledge or experience. With this image, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a number. If the image is selected properly and strong association with a person’s visual memory has been linked to it, the chances of recalling a lengthy password he/she created should not present a problem.

A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory information system (LIS), pharmaceutical management information system (PMIS), radiology information system (RIS), and nursing information system (NIS). A dynamic resource allocation table is critical for optimizing the performance to the mobile system, including the doctors, nurses, or other relevant health workers. We have designed a smart dynamic resource allocation model by using the C4.5 algorithm and cumulative distribution for optimizing the weight of resource allocated for the five major attributes in a cooperation communications system. Weka is used in this study. The class of concept is the performance of the app, optimal or suboptimal. Three generations of optimization of the weight in accordance with the optimizing rate are shown.

Although many security systems with biometric information have appeared, they only have been used the static bio-information, e.g., a fingerprint, ris, and so on. However, because these values are permanent, the attackers can modify and abuse that. To overcome this problem, many researchers would like to use dynamic bio-information, e.g., electrocardiograms (ECG), in security systems. In this case, a sensor in the system must measure the dynamic bio-information instead. The difficulty is that usually, the measured data is different whenever it measures. Therefore if the data is applied to existing algorithms, the results will not be matched and the user will be rejected to pass. This is because an unstable base point, which are Q and S values in the ECG, is used to calculate. To solve this, it suggests an adjusted (Qi * Si) algorithm that defines a specific distance from the location of R-peak to obtain the Qi and Si values. The algorithm can use balanced input data to determine the features, thereby enabling a highly accurate dynamic biometric system.

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user’s personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a ‘password reminder’ already embedded into the login scheme.

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

This paper demonstrates that AI methods, in particular linguistic mechanisms of semanticmeaning reasoning can be applied to the development of intelligent IT systems. They enablealso conducting an in-depth meaning analysis in the presented DDS information systems.This paper presents also IT mechanisms of object meaning description on selected examplesof spinal cord image analysis. The procedures for such semantic reasoning are based on themodel of cognitive resonance. They have been applied to the task of meaning interpretationof a selected type of central nervous system diagnostic images, as an intelligent analysismodule in IT systems. The application presented in this paper is of research character and itserves the preparation of efficient lesion detection methods applied to a dataset originatingfrom magnetic and resonance examinations of the spinal cord structures.