Authentication method for the keyboard usage based on fuzzy logic

The release of the latest iPhone device by Apple, named the iPhone 5s which incorporates a fingerprint-based biometric scanner, was met with a lot of criticism from the security and privacy community. It was soon demonstrated that the biometric reader on this new iPhone is just as vulnerable to spoofing attacks as devised by researchers such as Matsumoto et al. 2002. It is an excepted fact that making use of biometrics for effective security during the identification and authentication process is not recommended Woodward and Orleans, 2004. People leave latent biometric prints of their fingerprints on everything they touch. Biometric technology is vexed with this problem - a biometric characteristic is not essentially covert, as people deposit their biometric characteristics in various ways in the environment they interact with. This paper proposes an approach to allow a person to use a smart device such as the iPhone 5s, for secure biometric authentication over a networked environment for secure electronic transactions. The paper illustrates that a smart device can be considered as a 'smart token', to address the security concerns associated with biometric technology.

Biometric cryptosystems are widely studied recently, which bind a cryptographic key with the biometric characteristic of a user in such a way that the key cannot be recovered without a successful biometric authentication. By now, the key is generated from extra random number generators, which makes the system more complex. A novel method is proposed, which generates the key from biometric characteristics directly, and makes the biometric cryptosystems much cheaper, simpler and more convenient. To make the keys secure enough, the TD-ERCS is adopted and revised in the proposed algorithm. Experiments show that the algorithm is sensitive to biometric characteristics, free of collision, and the generated keys seem also random. It is believed that such a algorithm can be practically used for various biometric cryptosystems.

In this study, a fuzzy logic model was constituted by using the Fuzzy Logic (FL) method, which is one of the traditional artificial intelligence (AI) methods, in order to estimate the shear strength of reinforced concrete (RC) beams with limited ductility. In this model, beam width(bw), beam height(h), characteristic concrete compressive strength(fck), transverse reinforcement diameter(T), the number of arms bearing the shear force of the transverse reinforcement(n) and transverse reinforcement spacing(s) were taken into account as variable parameters. The model developed by using the problem data containing the solutions of shear force strength of 2640 beams with different cross-section properties were tested with 480 beam solutions different from these data. In the tests of the developed FL model, maximum percentage error, minimum percentage error, average percentage error and correlation coefficient values were obtained as 3.604, -0.091, 1.514 and R2=0.999678. By applying the fuzzy inverse logic method (FIL), which was recently developed by the author of this study, on the FL model, which is seen to have been developed quite sensitively from the test results, a total of 521 designs were obtained for 15 different RC beams with limited ductility subjected to shear. In order to check the accuracy of these designs, after shear strengths were obtained by conventional computations for these designs, % error and correlation coefficients were computed between the obtained strength values and the shear force values taken into account for the design. The promising results show that the FIL method can be used in the design of RC beams under shear force and even in other scientific studies such as design, optimization and control.

Solar Home System is one of the technologies for utilizing solar power at home. To optimize the performance of PLTS, it is necessary to regulate the use of electrical energy. In this research, an effort is made to control the load using fuzzy logic to regulate the power consumption used by the load so that energy can be utilized effectively. The fuzzy logic method works based on the input given so that the desired results can be as expected. To test the effectiveness of the fuzzy logic method, this study was tested with two types of loads: lighting and fan loads. For lighting loads, it uses two light sensor inputs, and for air conditioning, it uses two temperature sensor inputs and a PIR sensor. The test results show that in the experiment of setting the light load using fuzzy logic, the average power usage at the load is 11.31 watts. In contrast, without fuzzy logic, the average power usage value for the load is 14.29 watts. In the fan load control experiment using fuzzy logic, the energy consumption setting was obtained according to the room temperature input and the number of people in the room. The test results received power usage without using fuzzy logic, where the average fan power usage value is 4.32 Watts, while without fuzzy logic, the average power usage value is 2.97 Watts. For one sensor input, the average power usage value of the average fan is 3.02 Watts; without fuzzy logic, the average power usage value is 2.93 Watts for two sensor inputs.

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. For several decades, user authentication was based on the “something the user knows”, known also as knowledge-based user authentication. Recent studies state that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. In this context, we, firstly, present an overview of user authentication on mobile devices and discuss risk-based user authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Afterwards, a set of novelty detection algorithms for risk estimation is tested and evaluated to identify the most appropriate ones for risk-based user authentication on mobile devices.

Because the authentication method based username-password has the disadvantage of easy disclosure and low reliability, and also the excess password management degrades the user experience tremendously, the user is eager to get rid of the bond of the password in order to seek a new way of authentication. Therefore, the multifactor biometrics-based user authentication wins the favor of people with advantages of simplicity, convenience and high reliability, especially in the mobile payment environment. Unfortunately, in the existing scheme, biometric information is stored on the server side. As thus, once the server is hacked by attackers to cause the leakage of the fingerprint information, it will take a deadly threat to the user privacy. Aim at the security problem due to the fingerprint information in the mobile payment environment, we propose a novel multifactor two-server authentication scheme under mobile computing (MTSAS). In the MTSAS, it divides the authentication method and authentication means, in the meanwhile, the user's biometric characteristics cannot leave the user device. And also, MTSAS chooses the different authentication factors depending on the privacy level of the authentication, and then provides the authentication based on the different security levels. BAN logic's result proves that MTSAS has achieved the purpose of authentication, and meets the security requirements. In comparison with other schemes, the analysis shows that the proposed scheme MTSAS not only has the reasonable computational efficiency, but also keeps the superior communication cost.

<p>Biometrics associated with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Commonly used biometric features are facial features, fingerprints, voice, facial thermo grams, iris, posture/gait, palm print, hand geometry etc. Compared with other biometric characteristics iris is the most stable and hence the most reliable biometric characteristic over the period of a lifetime. This proposed work provides comparative study of various filters of Wavelet Transforms in terms of size and PSNR of images<em>.</em></p>

In personal computers, data is input through devices such as keyboards and mice, and various services are received from the internet. To provide these online services, secure user authentication methods are essential. Knowledge-based authentication methods, such as PINs or passwords, have been widely implemented in most services due to their ease of implementation. However, security threats such as brute-force attacks, phishing attacks, and keyboard data attacks that intercept sensitive user information have emerged. To counter these security threats, image-based authentication methods using mouse input were introduced. However, vulnerabilities arose when functions like GetCursorPos() or WM_INPUT messages were used, allowing mouse input data to be intercepted, thereby undermining image-based authentication. To defend against these attacks, counter-defence methods were developed to generate fake mouse data, protecting actual mouse data. With the advent of these defence methods, there has been a demand for attack methods to classify fake and real mouse data. Recently, machine learning-based methods have been employed on the attacker’s side to classify real mouse data, effectively distinguishing fake from real mouse data and compromising the security of image-based authentication methods. Therefore, this paper proposes a defence technology to safely protect mouse data from theft attacks using machine learning, specifically leveraging Generative Adversarial Networks (GANs). To achieve the goal of this defence technology, the distribution of fake mouse data generated using GANs was analyzed, verifying the feasibility of mouse defence methods. In summary, a system incorporating the defence technology was constructed, and a dataset containing both fake and real mouse data was created. Based on the constructed environment, the performance of the mouse data defence technology was evaluated. The results showed that it reduced performance by up to 37% in the dataset with the highest performance of existing machine learning-based attack methods. This study concludes that the proposed mouse data defence technology effectively addresses vulnerabilities and security threats related to user authentication information in various services relying on image-based authentication methods.

Continuous authentication is a promising method for protecting computer systems in the event of compromise of primary authentication factors, such as passwords or tokens. Systems employing continuous authentication that rely on biometrics may not be restricted to a single biometric characteristic; rather, they can simultaneously utilize multiple characteristics and subsequently arrive at a conclusive decision based on their collective analysis outcomes. One of the significant challenges researchers encounter when investigating effective fusion in decision-making is the lack of data. At present, data generation primarily involves the creation of feature vectors or attack simulation. This paper introduces a method for directly generating distances derived from a Siamese neural network, utilizing the probability density function of an existing distribution. Through statistical analysis, we successfully generated 5000 samples that correspond to the initial distribution, which were then employed to discover the threshold values at which FAR and FRR were less than 1%. The methods developed can be further applied to identify the most efficient strategies for integrating the results of continuous authentication in systems that incorporate multiple biometric characteristics.

Biometric characteristics allow identifying an individual based on who they are. This paper focuses on using the of a subject in electronic identity documents. The International Civil Aviation Organization (ICAO) has endorsed the adoption of the as the globally interoperable biometric characteristic. Soon afterward, the International Standard Organization (ISO) proposed the ISO/IEC 19794-5 standard in accordance with ICAO directives. In this paper, a new approach for the evaluation of three ISO/ICAO requirements (unnatural skin tone, across the and flash reflection on skin) is presented. The results achieved by the proposed methods have overcome almost all the works in the literature. For the shadows across the face requirement, the proposed method obtained a result 41% better than the best result in the literature. The proposed method is able to keep the rejection rate equal to zero even when it does not achieve the best equal error rate.

Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum‐rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance‐based face recognition algorithms has been done at each level.

In this paper, the fingerprint authentication is obtained by using fuzzy logic system, which is based on bell shaped intuitionistic fuzzy number. The minutiae points extracted from fingerprint images which are treated as a fuzzy number. The important of the proposed work is to find out the two identical fingerprint images based on minutiae points, from these selects the optimal set and reduce the error points between the images using fuzzy logic system. The fuzzy system provides an adequate description of the proposed algorithm. The uniqueness of this process is consisting of a smaller number of calculations better than previous methods.

Using multimodalities, the field of biometric is receiving widespread acceptance for efficient communication between users in social network. Hence, suitable designing of authentication and access control is highly required to perform analysis of system in social network domain. In this work, a Proximal Spatial Vector and Affinity Coefficient (PSV-AC) framework is designed with the objective of ensuring authentic communication between users in social network using face and fingerprint. Initially, multimodal user biometric features with proximal feature pixel stored in spatial vector are gene encoded to obtain biometric identity keys. User authenticity is authorized by decoding gene encoded biometric features using biometric identity keys. User Affinity Coefficient is calculated via antecedent instances shared between different social users. The social network user’s affinity coefficient is ordered in a matrix. Matrix ranking is then performed to identify whether authentic user social communication happened in network. Experiments have been carried out using BioSecure datasets to measure the performance in terms of number of social users, social network authentication time, access control time, multimodal biometric feature template size, and true positive rate.

The subject access control systems is information and communication networks. The purpose is to develop a user authentication method for information and communication systems as an access control system using a zero watermarking algorithm. Objectivs: develop a user authentication method based on zero watermarking, test the proposed method, provide suggestions and recommendations for the further application of the authentication method. To accomplish the defined tasks, the following methods computer simulation of the proposed method to study its characteristics; empirical methods – applying the method and observing the transformation results; tatistical methods – calculating indicators such as Pearson correlation coefficients, peak signal-to-noise ratio (PSNR), and Spearman’s rank correlation coefficients. Results achieved: novel user authentication method based on a unique zero watermarking algorithm was proposed for the first time within an information and communication system. Suggestions for further application of the method were formulated, and the method was successfully tested. Conclusions. During the study, an authentication method based on a zero watermarking algorithm was proposed, designed to be used as an access control system at industrial enterprises. The method was tested, and the test results confirm its operability and effectiveness. This method has the potential to reduce the cost of two-factor authentication when deploying authentication systems in large enterprises.

User authentication is an important technology to ensure the legal use of smartphones. With the development of smartphones, More and more authentication methods are appearing. Compared with traditional password authentication and face authentication, behavioral biometric authentication can keep authenticating the user's identity during use and does not require the user's collaboration in the authentication process. This paper summarizes the structure of smartphone authentication systems and the processing of behavioral biometric authentication and investigates user authentication approaches based on behavioral biometrics, including touchscreen-based authentication, motion-based authentication, and voice-based authentication. The research work of user authentication based on different behavioral biometrics is described and compared in detail. Finally, the paper discusses the advantages, challenges, and future trends of behavioral biometrics authentication.