User Verification Research Articles

Obfuscation of Malicious Behaviors for Thwarting Masquerade Detection Systems Based on Locality Features.

In recent years, dynamic user verification has become one of the basic pillars for insider threat detection. From these threats, the research presented in this paper focuses on masquerader attacks, a category of insiders characterized by being intentionally conducted by persons outside the organization that somehow were able to impersonate legitimate users. Consequently, it is assumed that masqueraders are unaware of the protected environment within the targeted organization, so it is expected that they move in a more erratic manner than legitimate users along the compromised systems. This feature makes them susceptible to being discovered by dynamic user verification methods based on user profiling and anomaly-based intrusion detection. However, these approaches are susceptible to evasion through the imitation of the normal legitimate usage of the protected system (mimicry), which is being widely exploited by intruders. In order to contribute to their understanding, as well as anticipating their evolution, the conducted research focuses on the study of mimicry from the standpoint of an uncharted terrain: the masquerade detection based on analyzing locality traits. With this purpose, the problem is widely stated, and a pair of novel obfuscation methods are introduced: locality-based mimicry by action pruning and locality-based mimicry by noise generation. Their modus operandi, effectiveness, and impact are evaluated by a collection of well-known classifiers typically implemented for masquerade detection. The simplicity and effectiveness demonstrated suggest that they entail attack vectors that should be taken into consideration for the proper hardening of real organizations.

A New Enhanced Template Protection Algorithm on Iris Recognition

Over the past few years, biometric systems have become prominent in terms of verification of the user identity due to increased demand of security in the networked society. Iris recognition system is a novel technology for the verification of user which is considered as the most secure, reliable and stable technique. It is generally accepted in the areas with high security. Though, security is major concern in this field, a significant number of approaches have been proposed to secure iris biometrics, But still, there is a scope to improve these techniques. Thus, in this work, a novel model is proposed which employs a bitmask compression technique to secure the template obtained for iris by compressing its actual size. In addition; SVM is used for the classification process. Mean Square Error, Bit Error Rate, PSNR, and GAR are different parameters which are used for measuring the effectiveness of the proposed model. The simulation results are carried out in MATLAB software and the comparative results validated the efficacy of the novel model with respect to security, efficacy and accuracy.

A Multimodal Biometric User Verification System with Identical Twin using SVM 2

A biometric system recognizes unique features of a distinct found on the resolution and examining the behavioral and physical characteristics. Yet, the objective of existing search is to identify and verify the authorized person but it is inefficient due to noisy data, unacceptable false rates and identical twins. However, the identical twin has a high complexity owing to the closest genetic relationship with overall biometric recognition. Multimodal biometric system uses multiple sensors to conquer the detriment of single biometric system. Hence, this method uses Adoptive Median Filter for reducing image distortion and SVM2 classification to improve the false rate which ensure reliable and precise biometric recognition system. This propounds a multimodal biometric user verification system by integrating fingerprint; facial recognition and lip print images which produce a robust, reliable and accurate authentication result.

Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device

BackgroundUser interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device.MethodsThe evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method.ResultsPerformance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes.ConclusionsThe performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.

Emoji Helps! A Multi-modal Siamese Architecture for Tweet User Verification

In the current paper, we have proposed a new multi-modal authorship verification approach for social media texts. Authorship verification is a task of verifying whether an unknown text is written by a suspect or not. Use of social media like Facebook and Twitter is increasing day by day because of digitization. People have grown accustomed to regularly post or tweet about their everyday life, memorable incidences, random thoughts, opinions, and much more. Emojis are widely used in these tweets and posts. The writing style of a user can differ from others, since word choices, sentence structures, usage of punctuation symbols, and use of emoji can be different. We have applied a multi-modal Siamese-based framework for automatic extraction of features from the given texts and emojis. After the extraction of features, the extracted features are applied to a neural network–based architecture for binary classification. A multi-modal Twitter-based dataset is created for evaluating the performance of the proposed framework. We obtained an average accuracy of 61.56% with 78.08%, 61.50%, and 58.32% precision, recall, and f-measure values, respectively.

Signature Verification Using Critical Segments for Securing Mobile Transactions

The explosive usage of mobile devices enables conducting electronic transactions involving direct signature on such devices. Thus, user signature verification becomes critical to ensure the success deployment of online transactions such as approving legal documents and authenticating financial transactions. Existing approaches mainly focus on user verification targeting the unlocking of mobile devices or performing continuous verification based on a user's behavioral traits. Few studies provide efficient real-time user signature verification. In this work, we propose a critical segment based online signature verification system to secure mobile transactions on multi-touch mobile devices. Our system identifies and exploits the segments which remain invariant within a user's signature to capture the intrinsic signing behavior embedded in each user's signature. Our system extracts useful features from a user's signature that describe both the geometric layout of the signature as well as behavioral and physiological characteristics in the user's signing process. Given the input signatures for user enrollment, our system further designs a quality score to identify the problematic signature sets to achieve robust user signature profile construction. Moreover, we develop the signature normalization and interpolation methods to achieve robust signature verification in the presence of signature geometric distortions caused by different writing sizes, orientations and locations on touch screens. Our experimental evaluation of 25 subjects over six months time period shows that our system is highly accurate in provide signature verification and robust to signature forging attacks.

How Can Patients With Mobile Hips and Stiff Lumbar Spines Be Identified Prior to Total Hip Arthroplasty? A Prospective, Diagnostic Cohort Study

BackgroundPatients with reduced lumbar spine mobility are at higher risk of dislocation following total hip arthroplasty (THA). Therefore our study aimed to (1) define the optimal protocol for identifying patients with mobile hips and stiff lumbar spines and (2) determine clinical and standing radiographic parameters predicting these patients. MethodsA cohort of 113 patients with end-stage hip osteoarthritis awaiting THA was prospectively studied. Clinical data, patient-reported outcome measures, and spinopelvic radiographs were assessed with the patient in the standing, “relaxed-seated,” and “deep-flexed seated” position. A “hip user index” was calculated quantifying the percentage of sagittal hip movement compared to overall movement between the standing and deep-flexed seated position. ResultsRadiographs in the relaxed-seated position had an accuracy of 56% (95% confidence interval 46-65) to detect patients with stiff lumbar spines, compared to a detected rate of 100% in the deep-flexed seated position. A standing pelvic tilt of ≥19° was the only predictor for being a hip user with a sensitivity of 90% and specificity of 71% (area under the curve 0.83). Patients with a standing pelvic tilt ≥19° and an unbalanced spine with a flatback deformity had a 30× fold relative risk (95% confidence interval 4-226, P < .001) of being a hip user. ConclusionPatients awaiting THA and having combined high hip and reduced lumbar spine mobility can be screened for with lateral standing radiographs of the spinopelvic complex. Hip user verification should be done utilizing radiographs in the deep-flexed seated position due to a higher accuracy compared to relaxed-seated radiographs. Level of EvidenceLevel II, diagnostic study.

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition.

Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG), the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG), and electroencephalogram (EEG), enabling users to customize their own gesture codes. The performance of 16 static wrist and hand gestures was systematically investigated in two identity management modes: verification and identification. The results showed that for a single fixed gesture, using only 0.8-second data, the averaged equal error rate (EER) for verification was 3.5%, and the averaged rank-1 for identification was 90.3%, both comparable to the reported performance of ECG and EEG. The function of customizing gesture code could further improve the verification performance to 1.1% EER. This work demonstrated the potential and effectiveness of sEMG as a biometric trait in user verification and identification, beneficial for the design of future biometric systems.

Identity Verification in Virtual Education Using Biometric Analysis Based on Keystroke Dynamics

Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.

Gait identification using a new time-warped similarity metric based on smartphone inertial signals

The evolution of smart devices and ubiquitous computing have paved the way for more intelligent user verification options. Gait pattern recognition has gained prominence due to their noninvasive and seamless verification capabilities without requiring dedicated attention from the user. This paper proposes a new gait identification approach by proposing a new time-warped similarity metric for memory-based gait pattern analysis. The proposed metric is incorporated into a computationally-efficient template matching framework for gait identification. The proposed approach outperforms the conventional approaches by achieving a user recognition error rate of 7.7% and equal error rate of 11.2% based on samples from 50 subjects in two benchmark datasets.

Intelligent human-centred mobile authentication system based on palmprints

Biometrics, as an intelligent and secure authentication method, has recently become increasingly popular. Modern society uses fingerprints, iris and face recognition on a daily basis, even on a large scale; for example, in biometric passports. However, there are still other biometric traits that may provide sufficiently high accuracy but have not been widely implemented so far, e.g. palmprints. In this article, we propose a novel human-centred method of palmprint-based user verification. The proposed method is dedicated to the mobile devices and provides the accuracy reaching 94.5%. Moreover, the method is time-computing efficient and gives the response in less than 0.2 s. All the experiments described in the article were performed using the benchmark PolyU database and three widely available mobile phones.

Enhanced Security through Multi-Tier User Authentication in Wireless Sensor Networks

Paper Setup must be in A4 size with Margin: Top 0.7”, For Smart Home Appliance Devices (SHAD), a Wireless Sensor Network (WSN) utilized for monitoring the condition and controls of SHAD to supervise lighting, warming, security, and investigation. A user confirmation method is required that enables authenticated clients to access SHAD managements for securing SHAD. Providing authentication is a challenging task despite the limited properties of sensor nodes. This proposed Multi-Tier User Authentication Scheme (MTUS) a proficient and security upgraded secret validation with a key acceptance method by utilizing biometrics data as the confirmation factor. Thus, the proposed MTUS method achieves a high-security level by time consumptions, which have 0.454 m sec of improvement of user verification. MTUS comprises of tasks and less time consumptions by existing method of Denial-of-Service(DoS)-Resistant User Authentication (DRUA) systems. Thus, the proposed MTUS method is with Denial-of-Service(DoS)-Resistant User Authentication (DRUA) existing systems. The MTUS proposal increases the demonstration of the system by reducing SHAD traffic, guarding against DRUA assaults, and increasing the battery lifetime. Subsequently, the usefulness and execution of the whole system are improved.

Mobile Web-Based Information System for thesis’s Administration in University PGRI Yogyakarta

This research aims to develop an Information System thesis’s administration that efficient and effective supported by computerized administration system or on-line system. The subject of this research is Universitas PGRI Yogyakarta which have 13 study programs. Mobile web can use in the thesis’s administration system. It can facilitate the management of required letters and essay recap. This system can also browse the titles of the thesis that has been created so as to reduce duplicate titles of the thesis.This study concludes that the use of thesis administrative systems can facilitate and accelerate the management of the required letters. It also can sorting titles of the essay that has been taken so as to avoid duplication. Based on black box testing, it is found that the whole function has been run well, the functions tested include: (1) User Verification (2) Data Management (3) Information and reporting. 3) Based on the alpha test it can be seen that the system developed in usability is considered good, the assessment aspect includes: ease of use, ease of navigation, access speed, completeness of features and attractive appearance.

Location-Aware Mobile Cloud using Artificial Intelligence

Mobile cloud computing is a rapidly evolving technology these days and it faces major problems of load imbalance due to the high demand for mobile applications. There are many techniques to solve the problem, but the user can improve load by using a more optimized solution. This research deals with the VM allocation and migration by means of location awareness. The research paper also presents a user authentication and server load management system to reduce the overload of the server. A captcha based authentication mechanism is also presented for user verification. The concept of Feedback is also introduced for the mobile servers. This concept makes the selection of mobile server for the job list. ANN (Artificial neural network) is used for location awareness judgment. ANN is a machine learning approach, which is used to minimize the human effort and also minimize the processing time to allocate job to an accurate server with minimum SLA violation. MBFD (Mobile best fit decreasing) algorithm is used for the VM allocation and selection policy. This research has considered SLA (Service level agreement) violation and energy consumption to compute the performance of the work with an aim of reducing energy consumption with maximized resource efficiency. The proposed work model is also compared with the work presented by Xiong in the same area.

Prevention of Payment Card Frauds using Biometrics

Digitization era has paved the path for digital economy. The advent of plastic cards such as credit and debit cards facilitate many services and products anytime and anywhere but they suffer with fraudulent attacks. This paper proposes a novel approach for the prevention of payment card frauds using biometrics. The approach works with the existing payment card security system and takes the advantage of biometric recognition technology that serves with a unique ID and bears anindividual’s biometric data like Aadhaar. The integration of biometric verification system with the payment card security system not only strengthens the security of card users but also it ensures the physical presence of card holder at the point of sale. The system like Aadhaar may be successfully linked with the payment card processing system for supplementing identity verification of users and making the environment free from card frauds. The performance of the proposed payment card security system using biometrics shows that the probability of breaching user’s identity is significantly low and found to be in the range of − to − for face, fingerprint and iris recognition

Multifactor Authentication and Key Management Protocol for WSN-assisted IoT Communication

In this paper a novel multi-factor authentication protocol for IoT applications, relying on enhanced Rabinassisted elliptic curve cryptography, biometric features and time stamping methods, is developed. Furthermore, a fuzzy verification algorithm has been developed to perform receiverlevel user verification, making computation efficient in terms of computational overhead as well as latency. An NS2 simulation-based performance assessment has revealed that the multifactor authentication and key management models we have proposed are capable of not only avoiding security breaches, such as smart card loss (SCLA) and impersonation attacks, but can also ensure the provision of maximum possible QoS levels by offering higher packet delivery and minimum latency rates.

Securing keystroke dynamics from replay attacks

Keystroke dynamics is a viable behavioral biometric technique for identity verification based on users’ keyboard interaction traits. Keystroke dynamics can help prevent credentials from being abused in case of theft or leakage. But what happens if the keystroke events are eavesdropped and being replayed? Attackers that intercept keystroke dynamics authentication sessions of benign users can easily replay them from other sources unchanged or with minor changes and gain illegitimate privileges. Hence, even with its major security advantages, keystroke dynamics can still expose authentication mechanisms to replay attacks. Although replay attack is one of the oldest techniques to manipulate authentication systems, keystroke dynamics does not help preventing it. We suggest a new protocol for dynamics exchange based on choosing a subset of real and fake information snippets shared between the client and service providers to lure potential attackers. We evaluated our method on four state-of-the-art keystroke dynamics algorithms and three publicly available datasets and showed that we can dramatically reduce the possibility of replay attacks while preserving highly accurate user verification.

Synchronization Control Method of Information Flow in Coastal Logistics Supply Chain under Asymmetric Information

Wei, H., 2019. Synchronization control method of information flow in coastal logistics supply chain under asymmetric information. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1087–1092. Coconut Creek (Florida), ISSN 0749-0208.Traditional methods have poor security and cannot solve the imbalance of benefit distribution caused by information flow sharing, and their overall performance is poor. Therefore, a new synchronous control method of information flow in construction supply chain under asymmetric information is proposed. According to the needs of participants in the construction supply chain for various information flows, the information in the central database is divided into blocks. Each participant and different information blocks correspond to each other. Each participant is provided with different rights. The corresponding information is obtained through user verification, and the data flow security control is realized. By calculating Shapley value, a Shapley value model of benefit distribution in information flow of construction supply chain under asymmetric information is constructed. Aiming at the disadvantage of ignoring the influence of other factors on benefit distribution under asymmetric information, this model comprehensively analyzed the four external factors of participant's benefit distribution: input proportion, risk sharing coefficient, performance degree value and participant's contribution value, completed the improvement of Shapley model, obtained benefit distribution value, and gave the process of determining risk sharing coefficient and contribution value. The experimental results show that the proposed method can guarantee the security of information flow synchronization control, and the results of benefit allocation are more balanced.

Application of the CLSI EP15-A3 Guideline as an Alternative Troubleshooting Tool for Verification of Assay Precision

Abstract Introduction The CLSI EP15-A3 guideline “User Verification of Precision and Estimation of Bias” provides a simple experimental approach to estimate a method’s imprecision and bias. The objective is to determine if the laboratory precision performance of repeatability (SR) and within-laboratory imprecision (SWL) are in accordance to the manufacturer specification claims (MSCs). Objectives Evaluate the utility of the EP15-A3 protocol to verify method precision during a troubleshooting investigation and after major instrument maintenance, using a carcinoembryonic antigen (CEA) immunoassay as an example. Methods CEA was performed on the Beckman Coulter DxI (Beckman Coulter, Brea, CA). Quality control (QC) levels (L1: 2.89; L2: 21.10; L3: 39.10 ng/mL) (Bio-Rad Laboratories, Irvine, CA) were used. Each QC level was measured before and after instrument maintenance as follows: five replicates per run, one run per day, and during 5 days. Imprecision estimates (IEs) for SR (%CVR) and SWL (%CVWL) were calculated by one-way analysis of variance using Microsoft Excel Analyse-it software. Estimated imprecision was compared to MSC and desirable imprecision specifications based on biological variation (BV). Results A change in the analytical performance of CEA was detected by a decreased sigma-metric indicator. After a bias problem was ruled out, the observed %CVR for L1, L2, and L3 were 7.2%, 3.7%, and 4.8%, respectively. The %CVWL were 8.3%, 5.0%, and 5.5%, which exceeded the MSC of %CVWL~4.0% to 4.5%. After a laboratory investigation, major instrument maintenance was performed by the manufacturer. The %CVR and %CVWL estimates for L1, L2, and L3 after maintenance were 3.2%, 3.8%, 3.5% and 3.9%, 4.2%, 4.0%, respectively. After maintenance, the CEA performance was consistent with the MSC for each of the levels analyzed and within the BV impression goal of %CV ≤6.4. Conclusion CLSI EP15-A3 guideline is an alternative troubleshooting tool that can be used to investigate and verify method precision performance before and after significant instrument maintenance.

CORMORANT

People own and carry an increasing number of ubiquitous mobile devices, such as smartphones, tablets, and notebooks. Being small and mobile, those devices have a high propensity to become lost or stolen. Since mobile devices provide access to their owners' digital lives, strong authentication is vital to protect sensitive information and services against unauthorized access. However, at least one in three devices is unprotected, with inconvenience of traditional authentication being the paramount reason. We present the concept of CORMORANT, an approach to significantly reduce the manual burden of mobile user verification through risk-aware, multi-modal biometric, cross-device authentication. Transparent behavioral and physiological biometrics like gait, voice, face, and keystroke dynamics are used to continuously evaluate the user's identity without explicit interaction. The required level of confidence in the user's identity is dynamically adjusted based on the risk of unauthorized access derived from signals like location, time of day and nearby devices. Authentication results are shared securely with trusted devices to facilitate cross-device authentication for co-located devices. Conducting a large-scale agent-based simulation of 4 000 users based on more than 720 000 days of real-world device usage traces and 6.7 million simulated robberies and thefts sourced from police reports, we found the proposed approach is able to reduce the frequency of password entries required on smartphones by 97.82% whilst simultaneously reducing the risk of unauthorized access in the event of a crime by 97.72%, compared to conventional knowledge-based authentication.