Attacking Windows Hello for Business: Is It What We Were Promised?

The increasing reliance on web applications across domains such as e-commerce, banking, healthcare, and enterprise systems has significantly amplified the importance of secure and reliable authentication mechanisms. Traditional password-based authentication methods are increasingly vulnerable to modern cyber threats, including phishing, brute force attacks, and credential stuffing, thereby necessitating the adoption of more advanced and robust authentication strategies. This study presents a comprehensive comparative analysis of modern authentication mechanisms in web applications, including Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), and biometric-based authentication systems. The research evaluates these mechanisms based on critical parameters such as security strength, usability, scalability, performance, and implementation complexity. It further examines architectural models, token-based frameworks, and federated identity systems that support secure user authentication in distributed environments. Additionally, the study reviews common vulnerabilities, threat mitigation techniques, and real-world deployment scenarios to assess the effectiveness of each approach. The findings highlight a growing shift toward hybrid authentication models that integrate multiple techniques to achieve enhanced security and user experience. However, challenges remain in balancing security with usability, ensuring interoperability across platforms, and maintaining privacy in decentralized systems. The paper concludes by identifying future research directions focused on passwordless authentication, zero-trust security models, and adaptive authentication systems for next-generation web applications.Keywords : Authentication mechanisms, Web application security, Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), Biometric authentication, Passwordless authentication, Zero Trust security, Cybersecurity

Context. An issue of correct configuration of honeypots is still opened, especially it is about honeypots that simulate wireless networksas their clients are mobile and zone of control is not limited. Wrong configuration of honeypot may become its usage disinterested insideautomated system especially it is applicable to honeypots for IEEE 802.11 wireless networks. Honeypot with open (no authentication)method or with low security may be suspicious for experienced attacker otherwise, it become easy prey for attackers whose goal is just accessto Internet. On the other hand, usage of honeypot with strong security level make no sense as well, as this model will become unconquerablefor attackers. Most protected access points use authentication method WPA2, usage of which may assure attacker that he/she attacks legitimate system.Objective. The goal of the researching work is to develop diagnostic model for honeypots in IEEE 802.11 wireless networks, which isconditionally secured by authentication method WPA/WPA2. Proposed model can help to assess possibility to leverage known WPA vulnerabilities by attacker on access point with given configuration.Method. An evaluation method of attacker’s qualification and its technical set of equipment in way of WPA/WPA2 encryption keyselection for wireless honeypot is offered. Implementation of this method allows to reach load reduction on honeypot what will provide anillusion of system authenticity for attacker. Method of distributed brute force attack on authentication method WPA/WPA2 that providesdiagnostic of Wi-Fi honeypot for encryption key resistance is offered. A Comparison between hardware virtualization and OS-level virtualizationis provided under the identical conditions in scope of WPA2 handshake brute force task.Results. Optimal conditions for providing brute force attack in virtual environment are obtained, what can give possibility to quicklyassess security level honeypot. This information can be used to understand how qualified attacker should be.Conclusions. A method of key perseverance assessment for authentication method WPA/WPA2 in IEEE 802.11 wireless network isproposed, for interaction with attacker with needed qualification level and computing resources. A method of IEEE 802.11 wireless networkssecurity assessment using Analytics Hierarchy Process got further development. The scalable environment for honeypots assessment providing is offered. The method of wordlist generation and rotation that are delivered to assessment system is proposed, what can help to exclude key reduplication what in its turn will help to speedup of assessment results.

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.

The surging demand for inexpensive and scalable IT infrastructures has led to the widespread adoption of Cloud computing architectures. These architectures have therefore reached their momentum due to inherent capacity of simplification in IT infrastructure building and maintenance, by making related costs easily accountable and paid on a pay-per-use basis. Cloud providers strive to host as many service providers as possible to increase their economical income and, toward that goal, exploit virtualization techniques to enable the provisioning of multiple virtual machines (VMs), possibly belonging to different service providers, on the same host. At the same time, virtualization technologies enable runtime VM migration that is very useful to dynamically manage Cloud resources. Leveraging these features, data center management infrastructures can allocate running VMs on as few hosts as possible, so to reduce total power consumption by switching off not required servers. This chapter presents and discusses management infrastructures for power-efficient Cloud architectures. Power efficiency relates to the amount of power required to run a particular workload on the Cloud and pushes toward greedy consolidation of VMs. However, because Cloud providers offer Service-Level Agreements (SLAs) that need to be enforced to prevent unacceptable runtime performance, the design and the implementation of a management infrastructure for power-efficient Cloud architectures are extremely complex tasks and have to deal with heterogeneous aspects, e.g., SLA representation and enforcement, runtime reconfigurations, and workload prediction. This chapter aims at presenting the current state of the art of power-efficient management infrastructure for Cloud, by carefully considering main realization issues, design guidelines, and design choices. In addition, after an in-depth presentation of related works in this area, it presents some novel experimental results to better stress the complexities introduced by power-efficient management infrastructure for Cloud.

Demand for remote work has surged as the COVID-19 epidemic has spread around the world. As one of the main implementations of desktop virtualization, Virtual Desktop Infrastructure (VDI) is popular and widely used in corporate remote work. A VDI user can connect to and use a virtual machine in a remote data center by logging in with a username and password using any device anywhere with Internet access. VDI has mobile convenience but is at risk of password leakage and insider threat. Traditional authentication methods, such as password and PIN, cannot withstand these threats. This work presents a keystroke-based continuous user authentication based on the Bidirectional Long Short-Term Memory (Bi-LSTM) network and embedding mechanism in deep learning to defend against such risks. It verifies the current user's identity based on the user's typing behavior continuously and non-invasively. We implement it on SPICE VDI and evaluate its performance and deployment feasibility on a public keystroke dataset - the Clarkson II dataset, which collected in uncontrolled and natural settings. The results show that it achieves state-of-art performance. It detects intruders with 8.28% of EER when only using 30 keystrokes and 0.85% of EER when using 990 keystrokes.

The primary motivation for enterprises to adopt virtualization technologies is to create a more agile and dynamic IT infrastructure -- with server consolidation, high resource utilization, the ability to quickly add and adjust capacity on demand -- while lowering total cost of ownership and responding more effectively to changing business conditions. However, effective management of virtualized IT environments introduces new and unique requirements, such as dynamically resizing and migrating virtual machines (VMs) in response to changing application demands. Such capacity management methods should work in conjunction with the underlying resource management mechanisms. In general, resource multiplexing and scheduling among virtual machines is poorly understood. CPU scheduling for virtual machines, for instance, has largely been borrowed from the process scheduling research in operating systems. However, it is not clear whether a straight-forward port of process schedulers to VM schedulers would perform just as well. We use the open source Xen virtual machine monitor to perform a comparative evaluation of three different CPU schedulers for virtual machines. We analyze the impact of the choice of scheduler and its parameters on application performance, and discuss challenges in estimating the application resource requirements in virtualized environments.

Sensory data is a main kind of data resources against the background of Internet of Things, it puts forward higher requirements on the underlying IT infrastructure in the aspects of data scale, growth rate and demand of application processing. With the IaaS(Infrastructure as a Service)-layer cloud services whose main character is virtualization, the goal of pooling, managing and flexible using of the IT infrastructure resources can be achieved, meanwhile the IaaS-layer cloud services is beneficial for meeting the need of sensory-data-hosting or sensory-data-application-hosting. But when directly applying those existing solutions to sensory-data-hosting, the phenomenon of load tilt and unbalace may appear, resulting in the less-than ideal condition of emerging hot spots or cold spots. This paper puts forward a sensory-data-hosting oriented scheduling strategy on virtual machine, aim at solving the problem of existing solutions can not schedule dynamically according to the changing needs of sensory-data-hosting, also we have designed and implemented the scheduling system as well as the scheduling algorithm based on the greedy algorithm. Preliminary sensory data-hosting cases have shown that the strategy proposed in this paper can be used to realize business-sensitive scheduling on virtual machine, and the goal of load balancing and optimized energy saving can also be achieved.

Cloud computing has revolutionized modern IT infrastructure, with virtualization technologies playing a crucial role in efficient resource utilization and deployment. Docker containers and Virtual Machines (VMs) are two dominant virtualization approaches, each presenting unique security implications. While Docker enhances agility, scalability, and resource efficiency, its shared kernel model introduces security risks, such as kernel exploits, container escape attacks, and privilege escalation vulnerabilities. In contrast, VMs offer robust isolation, mitigating risks through complete OS separation but at the cost of increased resource consumption and management complexity. This study presents a comprehensive analysis of security concerns associated with Docker and VMs, highlighting their attack vectors, vulnerabilities, and best practices for mitigating risks. Security architecture considerations, including hypervisor hardening, namespace isolation, and role-based access control, are explored. Additionally, hybrid approaches, such as running Docker inside VMs, are discussed as viable solutions to balance performance and security. Findings from this research emphasize the importance of tailored security strategies based on operational requirements. High-security industries, such as healthcare and financial services, may favor VMs due to their strong isolation properties, while development and CI/CD pipelines benefit from Docker's efficiency. Security automation, AI-driven anomaly detection, and confidential computing are identified as key areas for future advancements in securing virtualized workloads. By understanding the risks and implementing robust security frameworks, organizations can optimize both security and performance in their cloud infrastructure.

Today, Cloud Computing become one of the most promising an emerging technology which will has a significant impact on IT Infrastructure. Still, Cloud Computing is infancy. In the current cloud computing environment there is numerous of application, consist of millions of module, these application serve from large quantity of users and user request becomes dynamic. So there must be provision that all resources are dynamically made available to satisfy the needs of requesting a user in a situation where the resource contention fierce is occurs. In this paper, we propose scheduling heuristic to allocate resource and schedule request for cloud system. An aim of proposed scheduling heuristic is to schedule request on Virtual Machine (VM) based on agreed Service Level Agreements (SLA) and creating new VM on physical resources based on availabilities of resources which considered priority based VM allocation, Load Balancing and Preemption Mechanism. And the experimental results of proposed heuristic helps to optimize performance and at the same time reduce the possibilities of SLA violations.

Cloud Computing is a crucial computational paradigm for modern companies because it can discharge them from managing their ever growing IT infrastructure. Dynamically offering a plenty of computational resources, the cloud can also simplify the execution of CPU-intensive applications. Modern data centers for cloud computing are facing the challenge of a growing complexity due to the increasing number of users and their augmenting resource requests. A lot of efforts are now concentrated on providing the cloud infrastructure with autonomic behavior, so that it can take decisions about virtual machine (VM) management across the datacenter's nodes without human intervention. While the major part of these solutions is intrinsically centralized and suffers of scalability and reliability problems, we investigate the possibility to provide the cloud with a decentralized self-organizing behavior. To this purpose we present a novel VM migration policy suitable for a distributed environment, where hosts can exchange status information with each other according to a predefined protocol. The main goal of the policy is to balance the computational load on datacenter's physical hosts by conveniently moving virtual machines (VMs). We tested the policy performance by means of an ad hoc built simulator.

IT infrastructures have been widely deployed in datacentres by cloud service providers for Infrastructure as a Service (IaaS) with Virtual Machines (VMs). With the rapid development of cloud-based tools and techniques, IaaS is changing the current cloud infrastructure to meet the customer demand. In this paper, an efficient management model is presented and evaluated using our unique Trans-Atlantic high-speed optical fibre network connecting three datacentres located in Coleraine (Northern Ireland), Dublin (Ireland) and Halifax (Canada). Our work highlights the design and implementation of a management system that can dynamically create VMs upon request, process live migration and other services over the high-speed inter-networking Datacentres (DCs). The goal is to provide an efficient and intelligent on-demand management system for virtualization that can make decisions about the migration of VMs and get better utilisation of the network.

INFN CNAF is the National Computing Center, located in Bologna, Italy, of the Italian National Institute for Nuclear Physics (INFN). INFN CNAF, also called the INFN Tier-1, provides computing and storage facilities to the International High-Energy Physics community and to several multi-disciplinary experiments. Currently, the INFN Tier-1 supports more than twenty different collaborations; in this context, optimization of the usage of computing resources is essential. This is one of the main drivers behind the development of a software called WNoDeS (Worker Nodes on Demand Service).WNoDeS, developed at INFN CNAF and deployed on the INFN Tier-1 production infrastructure, is a solution to virtualize computing resources and to make them available through local, Grid or Cloud interfaces. It is designed to be fully integrated with a Local Resource Management System; it is therefore inherently scalable and permits full integration with existing scheduling, policing, monitoring, accounting and security workflows. WNoDeS dynamically instantiates Virtual Machines (VMs) on-demand, i.e. only when the need arises; these VMs can be tailored and used for purposes like batch job execution, interactive analysis or service instantiation. WNoDeS supports interaction with user requests through traditional batch or Grid jobs and also via the Open Cloud Computing Interface standard, making it possible to allocate compute, storage and network resources on a pay-as-you-go basis. User authentication is supported via several authentication methods, while authorization policies are handled via gLite Argus. WNoDeS is an ambitious solution aimed at virtualizing cluster resources in medium or large scale computing centers, with up to several thousands of Virtual Machines up and running at any given time.In this paper, we descrive the WNoDeS architecture.

User authentication is a fundamental component of cybersecurity. Traditional password-based systems have long been the standard for verifying identity in digital environments, but increasing threats such as phishing, brute-force attacks, and password reuse have exposed their vulnerabilities. Biometric authentication using unique physiological or behavioural characteristics like fingerprints, facial recognition, or iris scans has emerged as a promising alternative. This paper presents a comparative study between password and biometric authentication systems focusing on their security, usability, reliability, and performance. A data-driven analysis using secondary data and user-based experiments highlights the differences in accuracy, authentication time, and user satisfaction. Results show that biometric systems outperform password authentication in terms of security and user convenience but face challenges related to privacy, cost, and environmental reliability. The paper concludes that hybrid models combining biometrics and passwords can balance usability and security for modern applications.

Business continuity is valuable to IT management in both small and large organizations. As faults, errors, and even disasters perfectly exist, instantaneous service fail over must be furnished to computer systems in order to preserve business continuity. However, current solutions concentrate either on volatile memory and CPU context or on persistent disk storage, instantaneous fail over can hardly be achieved as expected. To remedy this, we design a novel framework that encapsulates computer system in a virtual machine, and henceforth frequently replicates its state to a backup physical host. In case the primary virtual machine crashes, the virtual machine replica at the backup host can instantaneously take over the failed virtual machine to service unfinished and new tasks without user's perception. We design and implement a prototype system, Paratus, to demonstrate this argument. Experimental results show that seamless service recovery is accomplished within seconds at the cost of no consistency check, restart, or reconfiguration required. Meanwhile performance degradation is hardly visible to users.

"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"