An Adaptive Approach Towards the Selection of Multi-Factor Authentication

In today's digital age, the convenience of online financial transactions is accompanied by rising cybersecurity risks. The shift to digital platforms for conducting daily financial activities has significantly exposed customers to cyber threats. These threats often result from unauthorized access, phishing attacks, and fraud attempts by cybercriminals. Ensuring the security of these transactions is crucial to maintain users' trust and to protect sensitive financial data. This study proposes a comprehensive security framework to strengthen the safety of online financial transactions, using a combination of multi-factor authentication (MFA) and a machine learning-based fraud detection system, termed "E-cyberboost." The framework operates on two primary layers, each designed to address a different aspect of security: identity verification and real-time fraud detection. The first layer involves multi-factor authentication, where users must verify their identity. This layer typically combines something the user knows (like a password) with something they have (such as a one-time password or OTP sent to their mobile device) or something they are (such as fingerprint or facial recognition). MFA significantly reduces unauthorized access by ensuring that even if one authentication factor is compromised, additional verification is required to gain access. The second layer introduces an advanced machine-learning component. Named "E-cyberboost," this machine learning model actively monitors ongoing transactions, detecting patterns that may indicate fraudulent activity. If the system identifies any unusual or suspicious transaction behaviour, it immediately triggers a security response, such as additional user verification or temporarily blocking the transaction. This proactive approach allows the system to address potential fraud in real time, adapting to evolving threats by learning from previous incidents and continuously improving its detection accuracy. By combining the preventive measures of MFA with the responsive capabilities of machine learning, this framework provides robust security against both common and sophisticated cyber threats. The study discusses the methodology, implementation, and effectiveness of this layered approach in reducing the risk of unauthorized transactions and protecting users' financial information. The proposed framework aims to enhance trust in online financial platforms and ensure a safer digital transaction experience for users. Index Terms—Multifactor authentication, machine learning, fraud detection, E-cyberboost

Online service is an important driving force behind many of today’s Web 2.0 applications. For security and privacy concerns, authentication is required for all of services that involve online transactions. Authentication is the process of verifying a user’s identity when the user is requesting services from any secure Information System (IS). By far, the most popular authentication technique is a basic username–password based method that is commonly considered to be a weak technique of authentication. A more secure method is the multi–factor authentication that verifies not only username–password pair, but also requires a second or third unique physical or biological factor. The feasibility of multi–factor authentication is inhibited by its deployment intricacy, and by the cost of building, maintaining, and re-deploying the hardware needed by multi–factor authentication as well. The Internet online transactions require a more feasible and secure means of authentication. Toward the ends, we have developed a technique that is a form of two – factor authentication, called SofToken. The SofToken is very cost effective and can also greatly reduces the deployment complexity of the application, while keeping a comparable or even higher level of security to other currently available techniques. In addition, an enhancement that utilized RFID devices is also proposed to advance the portability of the SofToken application for enterprise intranet-extranet environments. This RFID enhancement on SofToken is designed especially for computer system access using two-factor authentication to improve the security measure.

Internet-of-Things (IoT) is growing network paradigm which enables mutual communication between the user and smart devices using the internet. The IoT devices are susceptible to the security threats, due to placement of restricted computational capabilities of the computing devices in IoT. The conventional encryption algorithm utilizes the high amount of resource block in it which increases the area and power. Moreover, Two Factor Authentication (TFA) scheme based authentication protocols does not have the efficiency to secure the data. Because the random number generated by the TFA is ideal for all IoT devices which are easy to hack by the unauthorized persons. In this Research paper, the Linear Feedback Shift Register (LFSR) based Reconfigurable Physical Unclonable Function (RPUF) is proposed to overcome the security issues caused in the IoT communication. The RPUF is designed based on the LFSR to generate the random number for every clock cycle. Normally, reconfigurable process helps to generate the different output values for every clock cycle. But, it failed to generate different outputs for same input values. Here, LFSR based RPUF helps to generated the different response values even the same challenge is given to the input side. The Lightweight TFA scheme is presented for IoT, where PUF has been considered as one of the major authentication factors. At last, Spartan 6 and Virtex 6 Field Programmable Gate Array (FPGA) performances are calculated for proposed TFA-RPUF-IoT and existing TFA-PUF-IoT protocols. In Spartan 6, TFA-RPUF-IoT protocol occupied 11 slices, 31 LUTs, 42 flip flops which are less compared to conventional TFA-PUF-IoT.

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.

This paper discusses the implementation of a blink detection method using 68 facial markers and the eye aspect ratio (EAR) to provide strong protection for access systems. It investigates the importance of 68 facial markers and explores how to use eye landmarks to calculate the eye aspect ratio. Access systems, which should have good security measures and be difficult to decipher, are typically safeguarded by passwords or multi-factor verification, such as passwords combined with facial recognition. However, these methods have inherent weaknesses, including the risk of shoulder surfing with passwords and the potential to be deceived by fake face images with facial recognition. To address these issues, a two-factor authentication method by using password with eye blink recognition is proposed as an effective solution for access control. By incorporating real-time eye blinking action, the system can avoid the use of fake images and prevent shoulder spoofing. To demonstrate the practical application of eye blink detection for enhanced two-factor authentication, a web application called "Eblink" is introduced. Functional tests have been conducted to validate the application's core features.

Industrial Internet of Things (IIoT) brings together computers, devices, advanced analytics, and people in industries, such as transportation, oil plant, and power grid that leads to major efficiency and productivity gains for almost any industrial procedures. Due to the interconnection of devices in IIoT, communication security has become a critical issue to address in many emerging industry standards that require the authentication and key exchange procedure to be done to guarantee the authorized machine access (e.g., from users) and secure the data transmission between machines. To overcome the shortcoming (i.e., low entropy) of the memorable password in user authentication, it is rightfully recommended by industry standards (such as IEC-62443 family) to use multifactor authentication (MFA) for higher security levels. Notably, latency is one of the main sources of inefficiency when a device is communicating with other machines on IIoT. To mitigate latency, a smooth projective hash function (SPHF) built from well-studied standard assumptions is used to achieve a lowinteractivity multifactor authenticated key exchange protocol (MFAKE) because SPHF allows each party to prove to the others that he knows the right authentication factor(s). In this article, we are, therefore, motivated to build a new MFAKE named “secure remote multifactor (SRMF)” to achieve the humaninvolved “machine-to-machine” secure communication in IIoT. That is, SRMF leverages multiple user-centric authentication factors (such as password, biometric fingerprints, and PIN), and it can synergistically support multifactor registration (MFR), MFA, and multifactor key exchange (MFKE). Furthermore, to prevent authentication factors stored at the server exposing to attackers, the password-harden service (i.e., Pythia-PRF and USENIX'15) inspires us to develop a multifactor hardening service (MFHS) utilizing an oblivious pseudorandom function (OPRF). The balanced security of the proposed protocol is proved under the model of Bellare-Pointcheval-Rogaway (EUROCRYPTO'00) along with theoretical and experimental evaluations.

Electric Vehicle Charging Stations (EVCS) deployment has been growing exponentially due to the fast integration of Electric Vehicles (EVs). These stations do not just handle the transfer of energy between the power grid and EVs, but also the delicate payment and user authentication procedures. Therefore, EVCS systems are becoming appealing to cyber attackers that want to take advantage of the existing vulnerabilities at grid interfaces and payment points. Privacy integrity and availability of such essential infrastructures are seriously threatened by data-leaking events, malware infection, ransomware and distributed denial-of-service (DDoS) attacks. “we have to have a substantial amount of control over the data, both on an information network and the physical operations themselves.” —these breaches can also result in theft or alteration of financial data, and mismanagement of charging operations, energy theft and potentially destabilization of the grid. This study prescribes a holistic cybersecurity architecture including solutions for both grid communication links, as well as payment processing subsystems at EVCS locations. The method combines multi- factor authentication, anti-malware, malware detection/prevention, and secure communication protocols for an end-to-end secure operation. To avoid unauthorized access or manipulation in the command of energy transfer, encrypted communication with mutual authentication is implemented on grid interfaces. Tokenization, two-factor authentication and process of identification and verification for advanced fraud detection are methods that protect the payment gateways and financial transactions. Malware is combated with constant system monitoring, signature-based scanning and AI-based anomaly detection that can detect zero-day threats by identifying when things are not operating as they should be. Also, the solution includes firmware integrity check to prevent malicious attacks and uses segmentation to separate the payment network from the grid control systems, allowing the two to be isolated from each other – thus reducing the likelihood of cross system compromise. The experimental results are obtained on a simulated EVCS network with real-world charging and payment transactions data for different cyberattack scenarios, such as man-in-the-middle, SQL injection, phishing-based credential theft, and malware injection. Experiments showed a 97.8% detection rate, a 90% of the payment fraud attempts prevented and a very low overhead (<50ms) to normal charging or payment operations. The proposed framework provides a mechanism to make the infrastructure of EVCS a layered defense system that can address operation and finance security issues together. The project increases trust in EV uptake, strengthens regulatory adherence to data protection laws, and erects a resilient shield against advancing cyber threats within an expanding smart mobility environment. This study illustrates that, in addition to safeguarding monetary transactions, active cybersecurity is necessary to ensure the security of the operation of power systems linked to EV charging systems.

In the era of digital transformation within the financial services sector, this review examines the crucial role of identity management. Safeguarding financial information and countering fraud are paramount concerns in an increasingly digital landscape. This study navigates the significance of identity management and its pivotal contribution to securing digital environments for financial institutions and clients alike. Exploring a spectrum of identity management methods, the review emphasizes biometric authentication for its precision, tokenization and encryption for securing transactions, and Multi - Factor Authentication (MFA) for enhanced security. Additionally, the study scrutinizes blockchain - based identity management for decentralized solutions. Within identity management frameworks, focus areas include OpenID Connect for simplicity and widespread adoption, Security Assertion Markup Language (SAML) for improved authentication, and OAuth for impact on user consent and data protection in financial transactions. This review contributes essential insights for industry practitioners, policymakers, and researchers. In a concise format, it outlines key methods and frameworks, addressing the urgent need for robust identity management solutions in the evolving landscape of financial services. Keywords: identity management, financial services, biometric authentication, blockchain, OpenID Connect, cybersecurity.

Stated preference surveys are increasingly conducted online using internet panel samples, where a fast-growing share of respondents answer on smartphones. These panel members range from novices to “professionals” in terms of previous survey experience. Little is known about the potential effects of smartphone responding and survey experience on the data quality of stated preference surveys. This paper uses a discrete choice experiment dataset on the Norwegian population’s willingness to pay to plant climate forests to explore how these two factors affect data quality. These data by type of response device, gathered using a probability-based internet panel, were combined with a unique dataset obtained from the survey company on respondents’ actual experience answering surveys on different types of devices. Our results show that differences in elicited preferences between smartphone and computer respondents are not caused by the device used, suggesting that initial concerns about smartphone responses may be exaggerated. Furthermore, more experience is associated with an increasing scale parameter (indicating lower error variance), but at a decreasing rate; and a higher propensity to choose the status quo (indicating possible simplifying strategies employed by respondents). Combined this suggest some optimal level of experience that is neither too high nor too low. We discuss the implications of our results for stated preference research and provide a few avenues for future research.

ZnO-SiO/sub 2/-Si surface acoustic wave (SAW) devices with polycrystalline ZnO films of high quality at a thickness h between 0.3 and 15 mu m were fabricated using the RF-magnetron bias sputtering technique. X-ray diffraction, rocking curves, and SEM microscopy were used to characterize the ZnO layers. The authors studied the propagation loss and the coupling coefficient of three different types of devices: Rayleigh wave devices with ZnO layers h/ lambda approximately=0.4 and h/ lambda approximately=0.04, and Sezawa wave devices. Measurements were performed in the frequency range between 80 and 600 MHz. It was observed that Rayleigh wave devices with a thin ZnO layer have a much lower frequency dependence of the propagation loss than the other two types of SAW devices. At 550 MHz, devices with thin ZnO layers exhibit a propagation loss of only 7 dB/cm, compared to 24 dB/cm for Rayleigh wave devices with a thick ZnO layer, and 14 dB/cm for the Sezawa wave devices. Phase velocity and coupling coefficient as a function of film thickness were examined and are compared with theoretical values.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In an era where digital assets are the lifeblood of organizations, protecting them from unauthorized access and internal threats has become a top priority. Modern Privileged Access Management (PAM) tools have emerged as indispensable solutions to tackle these challenges effectively. This paper aims to provide an in - depth exploration of the capabilities offered by state - of - the - art PAM tools. These tools empower organizations to identify, manage, and fortify privileged accounts across their IT infrastructure. Key features encompass automated credential vaulting, regular password rotation, and robust session management to mitigate the risks associated with credential theft and unauthorized access. Additionally, PAM solutions enforce granular access controls, facilitate multi - factor authentication, and harness advanced analytics to detect and respond to anomalous activities swiftly. The seamless integration of PAM tools with Identity and Access Management (IAM) systems not only streamlines user provisioning but also enhances compliance reporting, ensuring adherence to regulatory standards. By harnessing the power of modern PAM tools, organizations can bolster their cybersecurity posture, neutralize insider threats, and successfully navigate the ever - evolving threat landscape of today.

In an era of escalating cyber threats, safeguarding data integrity in enterprise software applications is critical for maintaining trust and operational stability. Designing robust cybersecurity measures is essential to protect sensitive information from unauthorized access, alteration, and loss. This review explores key strategies and methodologies for developing comprehensive cybersecurity frameworks tailored for enterprise software applications. Effective cybersecurity begins with a thorough risk assessment to identify potential vulnerabilities and threats specific to the enterprise's software environment. Implementing multilayered security measures, including encryption, access controls, and authentication protocols, is vital for mitigating risks. Encryption protects data in transit and at rest, ensuring that even if intercepted, the data remains unintelligible to unauthorized parties. Access controls and authentication mechanisms, such as multifactor authentication (MFA), enhance security by verifying the identity of users and restricting access based on roles and permissions. Regular security audits and vulnerability assessments play a crucial role in detecting and addressing potential weaknesses. These audits should be conducted both internally and externally to provide a comprehensive view of the security posture. Additionally, adopting secure coding practices and integrating security into the software development lifecycle (SDLC) help in identifying and mitigating vulnerabilities during the development phase. Incident response planning is another critical aspect of cybersecurity. Developing a well-defined incident response plan ensures that the enterprise can quickly and effectively address security breaches, minimizing potential damage and restoring data integrity. This includes establishing protocols for detecting, responding to, and recovering from cyber incidents. Educating and training employees about cybersecurity best practices is essential for maintaining a secure environment. Employees should be aware of common threats, such as phishing and social engineering attacks, and understand their role in safeguarding the enterprise’s data. In conclusion, designing effective cybersecurity measures for enterprise software applications requires a multifaceted approach that includes risk assessment, encryption, access controls, regular audits, secure coding practices, incident response planning, and employee training. By implementing these strategies, enterprises can enhance their defenses, protect data integrity, and ensure the resilience of their software applications against evolving cyber threats. Keywords: Designing, Cybersecurity, Data Integrity, Software Applications, Enterprise.

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.

Usage of technological devices is increasing and also increases the facility and the compatibility of the hardware. The internet can be accessed through many devices and the devices can be connected with other devices. Internet of Things (IoT) has increased highly in various types of device and complexity of the connection with the different type of devices also increases. This increases the demand for the Service Oriented Architecture (SOA) for the user. IoT-based SOA technique provides the effective information by obtaining the information about the user's devices and their locations. This technique requires the device to be connected to the several devices, which also increases problem in security. Several research has been carried on the IoT-based SOA method and provides better solutions for some problems. In this paper, research has been done on the IoT-based SOA technique research to analysis the major issues with this technique and their better solution provided for the system. The major problem with the technique is lightweight process is required for the embedded system.

Background: Incorrect use of inhaler devices is associated with unfavorable outcomes Aim: To evaluate device use errors in asthma and COPD patients treated in outpatient clinics of 3 tertiary hospitals and one private practice office in Greece Methods: Demographics and medical history were recorded. Patients demonstrated the use of their prescribed device and use was evaluated according to predefined steps Results: 607 patients (50.2%male, mean age 60.2years) performed 663 demonstrations (56 patients were using 2 different types of devices). 49.8% were treated for asthma and 50.2% for COPD. 79.5% were using DPIs. Errors were documented in 41.2%, were associated with the type of device, p Conclusion: Device use errors are common and associated with unfavorable outcomes. Trained patients were more likely to use the device correctly. Patients should be encouraged to demonstrate the use of their devices during medical visits in everyday clinical practice