Security Defects in Biometric- based IdentificationSystemsand Mechanisms torectify them

Rapider responses are required from ATM banking suppliers to security requests. The current network environment has made it easier for hackers to engage in illicit activity. One of the most pressing issues confronting modern IT teams is network security. There is no doubt that ATM financial transactions should be protected against security risks by several different layers, but service providers should also incorporate security implications in their service offerings. And a lot concerning security flaws, credit card or identity card fraud, and methods for thieves and crackers to get their hands on any logical pin numbers or passwords. Identification, used in the current architecture to permit a system's access, is analogous to a username. Since passwords can be hijacked or misplaced, it is crucial to utilize authentication to confirm that the intended user is truly who their claim to be. The most recent solutions to security and privacy issues are biometric identification and authentication systems. Face recognition is the study of characteristics of a person being that can be used to recognize them. These physical characteristics include, among other things, fingerprints, faces, hand shapes, voices. This study surveys the various security system authentication methods. Top-level safety in authentication is provided by this facial biometric with QR code verification.

The robustness of talking-face identity verification (IV) systems is best evaluated by monitoring their behavior under impostor attacks. We propose a scenario where the impostor uses a still face picture and a sample of speech of the genuine client to transform his/her speech and visual appearance into that of the target client. We propose MixTrans , an original text-independent technique for voice transformation in the cepstral domain, which allows a transformed audio signal to be estimated and reconstructed in the temporal domain. We also propose a face transformation technique that allows a frontal face image of a client to be animated, using principal warps to deform defined MPEG-4 facial feature points based on determined facial animation parameters. The robustness of the talking-face IV system is evaluated under these attacks. Results on the BANCA talking-face database clearly show that such attacks represent a serious challenge and a security threat to IV systems.

As cyber attacks become more frequent at all levels, the commercial aviation industry is gearing up to respond accordingly. Commercial Aviation and Cyber Security: A Critical Intersection is a timely contribution to those responsible for keeping aircraft and infrastructure safe. It covers areas of vital interest such as aircraft communications, next-gen air transportation systems, the impact of the Internet of Things (IoT), regulations, the efforts being developed by the Federal Aviation Administration (FAA), and other regulatory bodies. The book also collects important information on the best practices already adopted by other industries such as utilities, defense and the National Highway Traffic Safety Administration in the US. It equally addresses risk management, response plans to cyber attacks, managing supply chains and their cyber- security flaws, personnel training, and the sharing of information among industry players. Commercial Aviation and Cyber Security: A Critical Intersection looks at possible future scenarios and how to respond to ever-growing cyber threats, how standards development will help combat this issue, listing the recommendations proposed by international agencies.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

ROS is a set of computer operating system framework designed for robot software development, and Red Door, a lightweight software firewall that serves the ROS, is intended to strengthen its security. ROS has many flaws in security, such as clear text transmission of data, no authentication mechanism, etc. Red Door can achieve identity verification and access control policy with a small performance loss, all without modifying the ROS source code, to ensure the availability and authentication of ROS applications to the greatest extent.

In this work, we study the uncertainty aspects in bio-metric identification and authentication systems. The uncertainty lies in two aspects, i.e., the probability mass function (p.m.f.) of users' biometric information and the observation channel. For the p.m.f. of users' biometrics, it is assumed that the users are from different groups and the distribution of each group is determined by the state. For the observation channel, it is assumed that there is a set of channels and the actual observation channel is from this set.

In order to separate a host's identity from its location on the Internet, the Host Identity Protocol (HIP) was developed by the Internet Engineering Task Force as a mobility management solution. HIP provides a solid basis to enable secured mobility and multihoming features. Several extensions and proposals have been introduced in recent publications to improve the micro‐mobility features of HIP. Moreover, many other publications have dealt with the efficiency of Network Mobility (NEMO) management with HIP. However, the HIP‐based micro‐mobility management solutions adapted to NEMO scenario do not cover all security aspects requirements and still suffer from security flaws. Therefore, in this paper, a number of potential threats in the typical HIP with Rendez Vous Server are identified. A new secure and efficient scheme for network mobility management is also proposed to overcome the outlined ones. The proposed solution ensures strong authentication between network entities, reduces Denial of Service attacks, secures against Domain Name Server spoofing, reply, and eavesdropping attacks, and ensures end‐to‐end confidentiality and integrity protection. To analyze the security properties of the proposed scheme, we have performed automated formal specification and evaluation with the help of both the Automated Validation of Internet Security Protocols and Applications and the Security Protocol Animator, which have proved that authentication and confidentiality goals are achieved. Hence, the scheme is effective when an intruder is present. Copyright © 2014 John Wiley & Sons, Ltd.

The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recognition, categorizing gait recognition methodsinto template-based and non-template-based approaches. Throughout their research, they identify the key issues and challenges that researchers face in this domain, along with the prevailing trends in human gait recognition within biometric identification systems.Additionally, the paper introduces a method for person identification based on gait, utilizing the Histogram of Oriented Gradients and the Sum Variance Haralick texture features. It involves transforming input video into a series of images depicting the gait silhouette, creating a Gait Energy Image (GEI) by combining these gait silhouettes throughout a gait cycle, and translating the GEI into the Gait Gradient Magnitude Image (GGMI). The subsequent step involves extracting recommended gait characteristics from the GGMIs of participants included in a dataset.To preprocess the collected characteristics, Principal Component Analysis (PCA) is applied, reducing the dimensions that may negatively impact classification robustness, thereby enhancing overall performance. In the final step, a K-Nearest Neighbors (KNN) classifier is employed to categorize the characteristics obtained from a specific dataset.The proposed novel feature vector in the paper demonstrates increased reliability and effectively captures spatial variations in gait patterns. Notably, it reduces the dimensionality of the feature vector from 3780×1 to 63×1, resulting in decreased computational complexity in the gait recognition system. Experimental evaluations on the CASIA A and CASIA B datasets reveal that the proposed approach outperforms other HOG-based methods in most scenarios, with the exception of situations involving frontal images.

Agriculture Technology (AgTech) integrates various technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, the Internet of Things (IoT), cloud and edge computing are applied to provide capabilities for collecting, transmitting, storing, processing, and analyzing agricultural data. Large amounts of data are gathered from multiple sources and processed in real time, enabling more effective decision-making. Data can be used to automate processes, resulting in savings on farm labor. To obtain the benefits of AgTech, data trustworthiness is essential and cyber resilience is required. Security flaws may result in farming equipment and processes being interrupted or inoperable, with significant revenue and capital losses. It is important for farmers to identify and respond to cyber incidents. Several existing works have attempted to classify AgTech technologies. However, most existing classifications are based on a limited set of characteristics and only focus on communication technologies in use or provide generic adversarial scenarios. There are real risks to data trustworthiness that impact on digital agriculture, and many of these risks are yet to be addressed. It is critical to understand the building blocks and possible components to be considered in a future security framework and their capabilities for satisfying the data confidentiality, quality, authenticity, and integrity requirements. This paper proposes a taxonomy to enable a consistent means of classifying the technologies that form the backbone of AgTech and outline the building blocks of data trustworthiness. This taxonomy consists of seven main criteria that recognize the technologies and systems (used to enable the movement of data at different stages) in terms of how it operates, its features, its benefits, and its limitations. Understanding the taxonomy enables effective implementation of anomaly detection and cryptographic methods to ensure data trustworthiness. Efficient AgTech with no security represents a serious risk to long-term sustainable smart farming and food security. Further, this paper outlines the integration of post-quantum cryptography with AgTech. The usefulness of the proposed taxonomy is demonstrated using two different types of case study related to the design and implementation of a modular security framework in real-world scenarios. The paper also identifies and discusses other important factors that impact data trustworthiness.

There is a pressing need for baseline demographic and health-related data to plan, implement and evaluate health interventions in developing countries, and to monitor progress towards international development goals. However, mobile pastoralists, i.e. people who depend on a livestock production system and follow their herds as they move, remain marginalized from rural development plans and interventions. The fact that mobile people are hard to reach and stay in contact with is a plausible reason why they are underrepresented in national censuses and/or alternative sequential sample survey systems. We present a proof-of-concept of monitoring highly mobile, pastoral people by recording demographic and health-related data from 933 women and 2020 children and establishing a biometric identification system (BIS) based on the registration and identification of digital fingerprints. Although only 22 women, representing 2.4% of the total registered women, were encountered twice in the four survey rounds, the approach implemented is shown to be feasible. The BIS described here is linked to a geographical information system to facilitate the creation of the first health and demographic surveillance system in a mobile, pastoralist setting. Our ultimate goal is to implement and monitor interventions with the "one health" concept, thus integrating and improving human, animal and ecosystem health.

The Model Context Protocol (MCP) is an emerging open standard that defines a unified, bi-directional communication and dynamic discovery protocol between AI models and external tools or resources, aiming to enhance interoperability and reduce fragmentation across diverse systems. This paper conducts a systematic study of MCP from both architectural and security perspectives. We first define the full lifecycle of an MCP server, comprising four phases (creation, deployment, operation, and maintenance), further decomposed into 16 key activities that capture its functional evolution. Building on this lifecycle analysis, we construct a comprehensive threat taxonomy that categorizes security and privacy risks across four major attacker types: malicious developers, external attackers, malicious users, and security flaws, encompassing 16 distinct threat scenarios. To validate these risks, we develop and analyze real-world case studies that demonstrate concrete attack surfaces and vulnerability manifestations within MCP implementations. Based on these findings, the paper proposes a set of fine-grained, actionable security safeguards tailored to each lifecycle phase and threat category, offering practical guidance for secure MCP adoption. We also analyze the current MCP landscape, covering industry adoption, integration patterns, and supporting tools, to identify its technological strengths as well as existing limitations that constrain broader deployment. Finally, we outline future research and development directions aimed at strengthening MCP’s standardization, trust boundaries, and sustainable growth within the evolving ecosystem of tool-augmented AI systems. All collected data and implementation examples are publicly available at https://github.com/security-pride/MCP_Landscape .

In the current paper a generalized net model of the biometric identification is presented. The modeled process contains combination of the steps included in unimodal, multibiometric and hybrid identification systems. The constructed generalized net model can be used for analyzing and simulating the processes included in biometric identification systems. It can be helpful in procedures of managing and optimizing the biometric identification processes. In the second part of the paper a MATLAB implementation of the biometric fingerprint identification is presented.

This paper discusses a system to determine the identification request for a given person already exists. The check is performed by comparing Applicant attributes (except for signature) against the registration database. For biometric information searches, a Biometric Identification System (BIS), which is an Automated Fingerprint Identification System (AFIS) Database, is searched against the captured biometric data on the application record. If a matched record already exists or a number of partially matched records exist, then application processing is routed to the “Resolve Duplicate” process. The Fraud Investigation application is built on Privacy Identification and Management Solutions (PRIMS) framework which is based on Identification and Credentialing solutions. The solutions are structured to capture biographic and biometric data in real-time with immediate checks on a person's identity, and produce e-ID cards and passports using this framework. The PRIMS framework supports identity management such as personnel enrolment and application processing, identity verification, secure document production and card inventory control, issuance and post-issuance management, authentication/authorization at point-of-service. The PRIMS framework is built with a flexible, component-based, service-oriented architecture. The PRIMS framework integrates a number of functions required to manage end-to-end life cycle of registered persons and secure documents. PRIMS provides a highly configurable workflow and business rules engine and allows for significant variability to support unique client requirements in the areas of enrolment processing, different technologies for secure documents, and different solutions and uses of biometrics.

Cybersecurity has become increasingly important for organisations. At present, there are several software development companies that employ strategies to eliminate code-based security risks. It is estimated that 95% of businesses use web applications that are the most vulnerable among all other types of applications. Application security company Veracode checked out 759,445 applications over a year using different security scans and found that 74% of these apps had at least one security flaw. It is important to note that Veracode also pointed out that 69% of the applications had at least one vulnerability from the Open Worldwide Application Security Project’s Top Ten list. This research is divided into two parts. First, two web applications were developed using ASP.NET and Node.js respectively, using security best practices. This was followed by analysing the security of these applications for some of the OWASP’s Top 10 vulnerabilities using cybersecurity software and tests. The outcomes are then reported, which provides an understanding of the vulnerabilities of the applications developed using ASP.NET and Node.js, in spite of using security best practices. The conclusions of the report emphasise the importance of incorporating cybersecurity practices at the earlier stages of the software development lifecycle (SDLC) and providing sufficient awareness about cybersecurity vulnerabilities to the developers.

In the last decade, we have seen the emergence of the Bitcoin crypto-currency, where the Blockchain technology builds trust transaction after transaction through a validation process. Academics focused their research on security flaws or improvement, but we identify a lack of literature on how the validation of transaction and block proceeds. In fact, understanding the validation will help the conception of new blockchain features and allow users to recognize all security risks. This paper aims at a deep comprehension and detailed analysis of the validation in the Bitcoin protocol.