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Enhancing security, privacy, and interoperability of biometric networks and protocols has been a challenge for many research works for many years. The several proposed approaches still need to integrate these three characteristics while showing security evidence for biometric applications. Therefore, this paper proposes a probabilistic scheme to encrypt biometric database indexes and a novel approach to interoperability among systems interchanging biometric characteristics, thus enhancing the IEEE Biometric Open Protocol Standard (BOPS). We highlight two meaningful improvements in our research when compared to related works. The first one comes from the proposed cryptographic techniques and network schemes. It implies a negligible probability for known attacks to be successful against the proposal, due to its semantic security evidence, as well as the difficulties that it imposes to the attacks, given the high complexity barriers that are unfeasible for the attacker to break in polynomial time, including the modified initialization vector and the nonce for the encryption algorithm. The second improvement comprises the new integrity and control procedures for biometric identification requests that boost the IEEE BOPS’ reliability and contribute to interoperability purposes. The security analysis, proofs, and results demonstrate that the new proposed biometric network is faultless regarding integrity and interoperability while preserving the anonymity of persons whose biometric data is exchanged in the network and stored in the related databases.

Distributed Denial of Service (DDoS) attacks are one of the most challenging security threats, since a single victim is attacked by several compromised malicious nodes. As a consequence, legitimate end users can be prevented to access network resources. This letter proposes a noise-robust multilayer perceptron (MLP) architecture for DDoS attack detection trained with corrupted data. In the proposed approach, the average value of the common features among dataset instances is iteratively filtered out by applying Higher Order Singular Value Decomposition (HOSVD) based techniques. The effectiveness of the proposed architecture is validated through comparison with state-of-the-art methods.

A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. Around 87% of the jurisprudence search systems use machine learning classification. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats.

Pandemic situations require analysis, rapid decision making by managers and constant monitoring of the effectiveness of collective health-related approaches. These works can be more efficient with the help of clearer and more representative views of the data, as well as with the application of other measures and projections of epidemiological nature to the information. However, performing such aggregations of data can become a major challenge in contexts with little or no integration between databases, or even when there is no technological core mature enough to feed and integrate technological advances in the workflow of health professionals. This paper aims to present the results of the meeting of project approaches such as the OSEMN framework, a software architecture based on Microservices and Data Science technologies, all tools aligned to make the environment of descriptive and predictive analysis of epidemic data (still dominated by manual processes) evolve towards a context of automation, reliability and application of machine learning, aiming at the organization and addition of value to the results of the data structuring. The project’s validation objects were the documents of the situation of the Covid-19 disease pandemic in the region of the city of Brasilia, Federal District, Capital of Brazil.

Among the most modern technologies used in air traffic surveillance systems, the ADS-B System is the one that stands out today. Such technology consists of a set of equipments and protocols designed to provide the means to determine the position of aircraft in flight from embedded navigation systems, as well as to periodically broadcast information of interest to other aircrafts en route and sensors on the ground within your range. However, serious security vulnerabilities lie at the heart of the ADS-B protocol, and the literature is unclear about the impact of exploitations in these breaches on the direct action of Air Traffic Controllers and aircraft pilots. This article aims to take an analytical look at the vulnerabilities present in the ADS-B system, not only when mapping attacks to that protocol, but also when seeking to identify, analyze, evaluate and classify the risks inherent to operational security through the GRSO method, focusing on the impact upon the Air Traffic Controllers decision- making process.

This study sought to provide an integrating model of the main contributions of the literature on CAPTCHAs with an impact in this field. With the expansion of internet access, there is an increasing need for a mechanism to protect websites from attacks, although there are situations where it is interesting to be able to automate some activities. This work consisted of identifying the most influential CAPTCHA-related academic works and trends in the field, which could serve as a metric on what approaches to take when developing new studies. Data such as main authors, current lines of research and more prolific research centers are arrived at using the Theory of the Consolidated Meta-analytic Approach. Inputting the keyword “captcha” in the Web of Science database, 539 records were found, from 2001 to 2020. The main classes retrieved are: (a) Captcha in Security Context (31.9%), (b) Usability in Captcha Design (28%), (c) Captcha Recognition by AI (21.8%), (d) Captcha Approaches and Novel Implementation Proposals (18.2%).

Novel schemes for attack detection are crucial to identify adaptive malicious traffic coming from sources that are quickly mobilized by attackers in high throughput communication networks. In this context, signal processing techniques have been applied to attack detection due to their capability to detect anomalies that are previously unknown, i.e. blind detection. This paper proposes a signal processing framework for the detection and identification of network attacks using concepts of model order selection (MOS), eigenvalues and similarity analysis. In order to validate the proposed framework, we consider network traffic datasets that contain malicious activity such as flood and port probing attacks. We propose to model the network traffic as a superposition of components, namely, user's operations (legitimate traffic), network service operation not related to the user (noise) and the malicious activity. The experiments performed in a real network and also using the DARPA 1998 public dataset show that the proposed blind detection approach achieves satisfactory levels of accuracy in terms of timely detection and identification of TCP/UDP ports under attack.

Drivers’ behavior in traffic is a determining factor for the rate of accidents on roads and highways. This paper presents the design of an intelligent IoT system capable of inferring and warning about road traffic risks and danger zones, based on data obtained from the vehicles and their drivers mobile phones, thus helping to avoid accidents and seeking to preserve the lives of the passengers. The proposed approach is to collect vehicle telemetry data and mobile phone sensors data through an IoT network and then to analyze the driver’s behavior while driving, along with data from the environment. The results of the inference serve to alert drivers about incidents in their trajectory as well as to provide feedback on how they are driving. The proposal is validated using a developed prototype to test its data collection and inference features in a small scale experiment.

Document and process digitalization is considered an essential factor for sustainable cities as it facilitates socio-economic interaction and contributes to climate-friendly urban environments and societies. Since digital documents are vulnerable to illegitimate modifications and forgery, there is a risk of these documents being rejected as untrustful. Therefore, to really benefit from digitalized documents and processes in sustainable urban environments and society, it is imperative to put in place means to generate trust toward these documents and processes. As computational trust and information security mutually contribute to building each other, this paper is devoted to building trust in digital documents by ensuring the chain of custody (CoC) of the produced and preserved documents. The CoC comprises a set of resources and means to ensure the properties of security required for digitalized documents and is a comprehensive concept allowing to integrate the security measures in the whole producer-to-consumer information flow, being explicitly informed to the user so that she/he can trust that available protections were put in place for their digital documents. As the CoC needs to be operative in diverse document production chains, our design considers the Industry Internet of Things (IIoT) paradigm to conceive an IIoT-Based Security Service for the documentary chain of custody – IoTSec2CoC, with a service model to identify and authorize all of its components by providing microservices for the integration of the fully distributed proactive and reactive security measures, to ensure the confidentiality, availability, and integrity of the flowing document units. This design constitutes a production chain that presents the plasticity adequate for smart sustainable cities. The proposal validation was carried out with an implemented prototype whose resilience tests show that the proposed new model increases security at all stages of the digital document processing CoC.

Distributed Denial of Service (DDoS) attacks are one of the most important security threats, since multiple compromised systems perform massive attacks over a victim, overwhelming its bandwidth and/or resources. Such attacks can be detected, for example, by using supervised machine learning based solutions previously trained on large DDoS attack datasets in order to automatically identify malicious patterns present in the incoming traffic. In addition, since large datasets show inherent multidimensional structures, tensor based detection techniques can outperform the matrix based counterparts. In this context, the development of a DDoS attack detection framework which exploits both machine learning and tensor based approaches is crucial. To face this challenge, this paper proposes a novel tensor based framework for DDoS attack detection using concepts of multiple denoising, tensor decomposition and machine learning supervised classification. Moreover, we also propose an extension of the recent Multiple Denoising algorithm such that the noise present in the dataset instances is more efficiently attenuated. Finally, we validate the effectiveness of our proposed framework through comparison with state-of-the-art low-rank approximation techniques as well as with related works. The proposed approach outperforms its competitor schemes in terms of accuracy, detection rate and false alarm rate.

With the widespread use of electronic devices such as laptops, smartphones and tablets, distance learning (DL) courses are increasingly being adopted by educational institutions. In order to conceive and execute such courses, different teaching models can be chosen. In this work, we overview the theoretical foundations related to the techniques of development of courses mediated by technologies from the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model. We propose the extended ADDIE (X-ADDIE) for the elaboration of DL courses. In the proposed X-ADDIE, besides the execution step, a new step is added, defined as pilot experiment. All the X-ADDIE steps are tracked by a project management tool. We evaluate the proposed X-ADDIE model by means of a qualitative approach.

The aim of this work is to propose the application of data mining techniques to detect behavior patterns in employee management databases. The desired knowledge concerns the transfer of people between organizational areas (internal turnover), in order to provide organizations a model for assessing the impact of these transfers on their people management policies. Therefore, this article describes a model that applies techniques of clustering and association rules for the discovery of knowledge on transfers of staff between units of a Brazilian financial organization. To allow the expression of this knowledge in a compact expressive way, a new dimension type is proposed for the multidimensional model. This way, the hypothesis is evaluated on a case study conducted in a Brazilian organization, with over four thousand units spread nationwide and over 95 thousand employees.