Ear Biometrics Based on Geometrical Feature Extraction

Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented

Graph Databases and E-commerce Cybersecurity - a Match Made in Heaven? The Innovative Technology to Enhance Cyberthreat Mitigation

This paper discusses the rationale behind applying state-of-the-art graph databases as one of the innovative ways of enhancing the artificial intelligence-powered cybersecurity of e-commerce service. Firstly, the graph theory and graph databases are introduced. Then, the paper argues why graph databases are a good fit for cybersecurity experts’ tasks and what the advantages of applying graph databases in cybersecurity are. Then, a number of available, existing tools which combine the graph database technology and cybersecurity are shown. The main contribution of the paper is a real-life scenario which has been presented of a tool designed by the authors, which employs the graph database technology and e-commerce cybersecurity, with the conclusions given thereafter

The IoT Threat Landscape vs. Machine Learning, a.k.a. Who Attacks IoT, Why Do They Do It, and How to Prevent It?

Internet-of-Things has been a widely used term, referring to the interconnected ecosystem, built of loosely connected devices, capable of accumulating, processing and transferring data through the heterogeneous network Recently, the IoT’s technical, economic and social importance has drastically increased. However, the IoT does not bring advantages only. According to recent studies, vast majority of IoT devices are prone to being attacked, hacked or intruded. If not secure enough, IoT may pose risk to the security of ordinary citizens, and whole industries alike. The paper aims at drawing the current threat landscape in relation to IoT, by examining the threat actors, their motivation and capabilities. Firstly, the specific security goals, context, elements and main challenges to IoT security are discussed. Then, the work collects the actors that pose the threat to IoT, as well as their motives for attacking IoT. The following part of the paper discusses the various attack taxonomies, and the state-of-the art of the IoT cybersecurity countermeasures and recommendations. Against this background, a novel intrusion detection tool is introduced, and its technical description is provided. When tested on data from a benchmark dataset, the method has already shown promise in performing its tasks

Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection

As computer and network technologies evolve, the complexity of cybersecurity has dramatically increased. Advanced cyber threats have led to current approaches to cyber-attack detection becoming ineffective. Many currently used computer systems and applications have never been deeply tested from a cybersecurity point of view and are an easy target for cyber criminals. The paradigm of security by design is still more of a wish than a reality, especially in the context of constantly evolving systems. On the other hand, protection technologies have also improved. Recently, Big Data technologies have given network administrators a wide spectrum of tools to combat cyber threats. In this paper, we present an innovative system for network traffic analysis and anomalies detection to utilise these tools. The systems architecture is based on a Big Data processing framework, data mining, and innovative machine learning techniques. So far, the proposed system implements pattern extraction strategies that leverage batch processing methods. As a use case we consider the problem of botnet detection by means of data in the form of NetFlows. Results are promising and show that the proposed system can be a useful tool to improve cybersecurity

Advanced services for critical infrastructures protection

In this paper an overview of the first results of FP7 CIPRNet project is presented. Particularly, we demonstrate CIPRNet services for critical infrastructure protection (CIP) stakeholders. The role of the proposed services is to support decisions in the CIP domain. Moreover, those services are expected to serve as the underpinnings for the European Infrastructures Simulation and Analysis Centre (EISAC) which, similarly to the US NISAC, should provide operational services on CIP, for the benefits of CI operators, stakeholders and the Public Authorities committed to CIP

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

International Conference on Image Processing and Communications

This book contains papers accepted for IP&C 2015, the International Conference on Image Processing and Communications, held at UTP University of Science and Technology, Bydgoszcz, Poland, September 9-11, 2015. This conference was the eighth edition in the IP&C series of annual conferences. This book and the conference have the aim to bring together researchers and scientists in the broad fields of image processing and communications, addressing recent advances in theory, methodology and applications. The book will be of interest to a large group of researchers, engineers and practitioners in image processing and communications