PIN generation using EEG : a stability study

In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain–computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of this method is obviously its better fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e. to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable

Enhancing elderly mobility through IoT using textiles: a review

The aging of population worldwide and the increasing of average life expectancy of the world population become a social and economic problem for society. It's necessary to provide solutions which can maintain the independency of elderly and their mobility, along more time, avoiding the unnecessary perma-nence in hospitals and institutions for the elderly. The use of IoT using textiles is a very interesting approach because of the proximity of people to these materials. This work reviews the development on IoT using textiles to increase the mobility of elderly people and concludes that this is a field of growing interest. Although, there is few investigation, especially in what concerns studies focusing on the im-portance of improving the mobility of the elderly. There are studies that lead to the possibility of promoting this physical capability but have not been designed for that purpose, but we believe that approach of the problem of mobility of elderly people using the IoT based on clothes should be treated with specificity, because the consequences of this not happening, will affect not only the elderly but the whole society.info:eu-repo/semantics/publishedVersio

Internet of things for the hotel industry: a review

The Internet of Things (IoT) represents an opportunity for the hotel in-dustry to increase customer satisfaction while simultaneously reducing operational costs. This paper analyses the existing knowledge on this subject, through a re-view of the relevant publications indexed by Scopus and/or ISI Web of Science, concluding that, despite the existence of many relevant patents, registered in the past few years, the published research is very limited on this topic. The restriction to publication prior to the conclusion of the patent registration process may be a justification for this fact, and, if so, the near future will bring many novelties that will help the development of the hotel industry. It is also possible to conclude from this work that the potential of IoT is not yet well explored in the hotel indus-Try, once authors frequently theorize on the use of IoT for applications that could easily be of interest for the hotel industry, but fail to identify that opportunity as a major market.info:eu-repo/semantics/publishedVersio

Data mining a prostate cancer dataset using rough sets

Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%. In addition to the high classification accuracy of our system, the rough set approach also provides a rule-based inference mechanism for information extraction that is suitable for integration into a rule-based system. The generated rules relate directly to the attributes and their values and provide a direct mapping between them

Critical aspects In authentication graphic keys

In order to increase the number of possible keys (key’s space), some applications are using, as the user’s authentication secret, images instead of words, taking advantage of the several possibilities for each mouse click and of the fact that humans memorize images better then words. This paper presents the characterisation of the graphical keys chosen by almost 200 regular users of a website and the results show some important fact that must taken into account to maximize the security of the authentication process.(undefined

Developing a keystroke dynamics based agent using rough sets

Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 98%, specificity of 94% and an overall accuracy of 97% with respect to detecting intruders. In addition, our results indicate that typing speed and particular digraph combinations were the main determinants with respect to automated detection of system attacks

Enhancing login security through the use of keystroke input dynamics

Security is a critical component of most computer systems – especially those used in E-commerce activities over the Internet. Global access to information makes security a critical design issue in these systems. Deployment of sophisticated hardware based authentication systems is prohibitive in all but the most sensitive installations. What is required is a reliable, hardware independent and efficient security system. In this paper, we propose an extension to a keystroke dynamics based security system. We provide evidence that completely software based systems based on keystroke input dynamics can be as effective as expensive and cumbersome hardware based systems. Our system is a behavioral based system that captures the typing patterns of a user and uses that information, in addition to standard login/password security to provide a system that is user-friendly and very effective at detecting imposters. The results provide a means of dealing with enhanced security that is growing in demand in web-based applications such as E-commerce.(undefined

Generation of authentication strings from graphic keys

The traditional authentication system used in technological applications is the well-known and widely spread user/password pair. This technology as proved itself as well acceptable by the users and quite safe when used according to good security practices, this is: frequent change of the password; use of letters, number and symbols in the password; not revealing the password to others; not using the same password in more then one service; etc. But this is not what really happens, so we need to improve the protocol. Graphical secrets present lots of advantages and can increase the level of security without a significant change in the users habits. For that, we need to possess strong ways to convert them into strings that will fed the implemented passwords systems. In this paper we present a method to do so

Authenticating computer access based on keystroke dynamics using a probabilistic neural network

Comunicação apresentada na 2nd Annual International Conference on Global e-Security, Docklands, UK, 20 - 22 April 2006.Most computer systems are secured using a login id and password. When computers are connected to the internet, they become more vulnerable as more machines are available to attack them. In this paper, we present a novel method for protecting/enhancing login protection that can reduce the potential threat of internet connected computers. Our method is based on and enhancement to login id/password based on keystroke dynamics. We employ a novel authentication algorithm based on a probabilistic neural network. Our results indicate that we can achieve an equal error rate of less than 5%, comparable to what is achieved with hardware based solutions such as fingerprint scanners and facial recognition systems

Structured textual data monitoring based on a rough set classifier

Text is frequently stored in structures that are frequently complex and sometimes too large to be fully understood and/or apprehended. This problem has concerned the data mining community for many years as well as the information's community. Many algorithms have been proposed with the objective of obtaining better answers to the queries made and to obtain better queries that can respond to the questions that are in the users mind. Some of those algorithms are based on the relations between the concepts. But some of those relations are also dynamic and are, themselves, relevant information. This paper describes and adaptation of one of those methods, based on the Rough Sets theory, in order to detect changes in the existing relations between the stored concepts and, through that, to detect new relevant aspects of the data.- (undefined