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Penggunaan Smartphone sudah menjadi kebutuhan dalam kehidupan sehari-hari. Mayoritas masyarakat sudah menggunakan smartphone baik untuk keperluan komunikasi maupun dalam mendukung pekerjaan. Di samping memberikan dampak positif, smartphone juga mempunyai dampak negatif terutama dari sisi radiasi. Dampak smartphone digunakan secara terus menerus dap...
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... Many keystroke dynamics-based authentication methods have used deep learning algorithms to make their authentication decisions. Studies such as [49][50][51][52][53][54] used RNNbased deep architectures. In [49], researchers tested many types of touch modalities, including keystroke on their effectiveness with various motion sensors. ...
... Many keystroke dynamics-based authentication methods have used deep learning algorithms to make their authentication decisions. Studies such as [49][50][51][52][53][54] used RNN-based deep architectures. In [49], researchers tested many types of touch modalities, including keystroke on their effectiveness with various motion sensors. ...
... Their model was able to achieve 9.2% EER in mobile authentication with a triplet loss function when balancing enrollment data and performance. In [53], weighted results from and RNN and a SVM were used to authenticate keystroke data. A small dataset consisting of motion sensor, touchscreen, and temporal data from 10 users was used to train the models. ...
Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.
... The implemented solution in [50] is referred to as secure, because parsing the wireless network traffic is implemented in a memory-safe language for biometric scans classification. It is scalable because wireless network can increase throughput with more processing cores and can be used in a distributed monitoring setup. ...
The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication.
... We all have different "living credentials" such as fingerprints, facial expressions, optical elements, voice tones, and behavioral attributes. User behavior is one way to represent the identity of an individual [14]. It identifies whether the current user is a valid one by collecting and training the features of the sample data in order to carry out authentication. ...
User authentication is one of the critical concerns of information security. Users tend to use strong textual passwords, but remembering complex passwords is hard as they often write them on a piece of paper or save it on their mobile phones. Textual passwords are slightly unprotected and are easily attackable. The attacks include a dictionary, shoulder surfing, and brute force. Graphical passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of use. This paper proposes a Process-based Pattern Authentication (PPA) system for the Internet of Things (IoT) devices that do not require a server to maintain a static password of the login user. The server stores user’s information, which they provide at the time of registration, i.e., the R-code and the symbol, but the P-code, i.e., the actual password, will change with every login attempt of users. In this scheme, users may draw a pattern on the basis of calculation from the P-code and R-code in the PPA pattern and can authenticate themselves using their touch dynamic behaviors through an Artificial Neural Network (ANN). The ANN is trained on the touch behaviors of legitimate users reporting superior performance over the existing methods. For experimental purposes, PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and usability. The experiments show that the system has an effect of 5.03% of the False Rejection Rate (FRR) and 4.36% of the False Acceptance Rate (FAR), respectively.
... An increasingly popular form of biometric authentication is through the recognition of mouse movements or keyboard-based behavioral patterns. Rapid User Mouse Behavior Authentication (RUMBA) [22] is a novel attempt to detect patterns in mouse movements using RNNs and the architecture of this model is represented in Figure 2. The researchers took this approach because monitoring physical characteristics requires access to extra hardware like specialized sensors. The paper also describes that data like mouse movement information is easy to collect and contains little privacy-sensitive information. ...
Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each pro-posed model.
... This sensor can further be implemented in the Wireless Body area network (WBAN), and can be integrated into Ehealthcare platforms, owing to the advancements in digitization. Some more related literature can be studied in the references [54][55][56][57][58]. ...
High Blood Pressure (HBP) over a long period of time leads to a medical condition known as Hypertension (HTN or HT), in which the arterial blood pressure is persistently inflated. In this research, a blood pressure biosensor has been implemented that monitors the different stages of hypertension by measuring the varying blood pressure values. Further, a system design has been proposed for the theranostics (combination of diagnosis and therapy) of hypertension. The proposed sensor takes blood pressure as the input, and gives the corresponding electrical signal variations at the output, so as to detect the exact stage of hypertension. The working principle of this BP sensor is based on the electrostatic transduction mechanism. The proposed system aims to design a low-cost, easily available, implantable lab-on-chip (LoC) platform for the continuous measurement of blood pressure by keeping track of the patient history, so as to prevent chronic hypertensive disorders. Further, such an implantable LoC theranostic platform is among the best possible solutions for the e-healthcare systems, owing to its potential in a wireless body area network (WBAN) using Internet of Things (IoT) and other similar technologies.
... An increasingly popular form of biometric authentication is through the recognition of mouse movements or keyboard-based behavioral patterns. Rapid User Mouse Behavior Authentication (RUMBA) [22] is a novel attempt to detect patterns in mouse movements using RNNs and the architecture of this model is represented in Figure 2. The researchers took this approach because monitoring physical characteristics requires access to extra hardware like specialized sensors. The paper also describes that data like mouse movement information is easy to collect and contains little privacy-sensitive information. ...
Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each proposed model.
... Nowadays, user authentication in smartphones became a trending topic in digital field. For that, Sun et al. (2019) have been designed an authentication frame in smartphones to protect the user secrets from un-trusted parties. Here, the sensed data of own users from different mobile gadgets were trained to the system then the features of sensed data of own users were extracted using recurrent model. ...
Authentication is the process of keeping the user’s personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.
... So, this functionality allows the application server to tell real taps from fake taps that make use of stored URLs in a browser or shared URLs. This is especially relevant for security applications where it is critical to rely on trusted taps at fixed locations (for example, when logging rounds of security personnel patrolling facilities, coupled with authentication methods, such as simple fingerprint biometrics or combined with other advanced behavior-based biometrics) [37]. ...
Radio Frequency IDentification (RFID) and related technologies such as Near Field Communication (NFC) are becoming essential in industrial contexts thanks to their ability to perform contactless data exchange, either device-to-device or tag-to-device. One of the three main operation modes of NFC, called read/write mode, makes use of the latter type of interaction. It is extensively used in business information systems that make use of NFC tags to provide the end-user with augmented information in one of several available NFC data exchange formats, such as plain text, simple URLs or enriched URLs. Using a wide variety of physical form factors, NFC-compatible tags (wireless transponders) are currently available in many locations with applications going from smart posters, contactless tokens, tap-and-go payments or transport ticketing to automated device configuration, patient identification at hospitals or inventory management within supply chains. Most of these applications handle sensitive processes or data. This paper proposes a complete security threat model for the read/write operation mode of NFC used in Next Generation Industrial IoT (Nx-IIoT) contexts. This model, based on a well-known methodology, STRIDE, allows developers and users to identify NFC applications vulnerabilities or weaknesses, analyze potential threats, propose risk management strategies, and design mitigation mechanisms to mention only some significant examples. 1 Introduction Near Field Communication (NFC) is a short-range half-duplex wireless communication technology relying on magnetic field induction that allows two or more devices to perform a data exchange when they are located at a maximum of few centimetres from each other [1],[2]. The main advantage of NFC over other wireless communication technologies is that NFC has been designed with a focus on the simplicity of operation, offering the user a "touch-and-go" experience. Transactions are initialized automatically, only by bringing an NFC device (for example, an NFC enabled smartphone) close to an NFC compliant transponder (a tag) or other NFC device [3]. NFC technology is based on the contactless smartcard standard ISO/IEC14443 and operates at a universally open and licensed 13.56MHz frequency. Some NFC devices can also communicate on the same frequency with vicinity RFID transponders based on the more recent ISO/IEC 15693 [4],[5]
The Internet of Things (IoT) in communication networks has gained major interest in recent decades. Accordingly, secure authentication of such individuals results in a major challenge due to the weakness in the authentication process. Hence, an effective Hybrid and Adaptive Cryptographic (HAC)-based secure authentication framework is designed in this research to perform an authentication process in IoT. The proposed approach uses cryptographic operations, like exclusive-or (Ex-or) operation, a hashing function, and hybrid encryption to accomplish the authentication process. However, the hybrid encryption function is carried out in two different ways: one depends on Advanced Encryption Standard (AES) as well as Elliptic Curve Cryptography (ECC), while other is based on Rivest Shamir Adleman (RSA) and AES. With a hybrid encryption function, security flaws can be effectively dealt through the cryptographic system. Moreover, the proposed approach provides high robustness with low complexity. The proposed HAC-based secure authentication approach obtained a minimum communication cost of 0.017sec, less computation time of 0.060sec, and minimum memory usage of 2.502MB, respectively.
