Abstract
High data rate optical fiber links are usually deployed in core IP networks to transport bulky [...]

Abstract
Security attacks using USB interfaces and devices are becoming more advanced, which boost efforts to develop counter measures in order to protect systems and data. One of the most recent attacks using USB devices is the BadUSB attack, performed by spoofing the device’s firmware and allowing the attackers to execute a set of malicious actions, e.g. an USB storage device could be mounted as USB keyboard in order to inject malicious scripts into the system. This paper proposes a protection agent against BadUSB attack developed for Windows operative systems. It allows a user to check the class of an USB device ready to be mounted, though enabling the detection of a potential attack if the expected functionality of the device does not match with its class type. The results show that the proposed protection agent is capable of detecting potential intrusions by blocking the installation of the device, scanning the device for something that identifies it, searching for a description locally and finally warning the user about the device meaning that all devices must be approved by the user when plugged in if the system protection agent is running. Copyright

Abstract
The security standard ISO/IEC 27001 provides orientations to support organizations to set adequate best practices in information security management, specifying requirements that enable the appropriate selection and implementation of security controls. This standard assists organizations to protect their information assets, achieve their adequate levels of security and thus help them to succeed their business goals. Currently, an increasing number of Portuguese organizations seek to comply ISO/IEC 27001:2013 standard and obtain the respective certification. This paper presents the result of a research conducted in order to detail the main difficulties and limitations evidenced by Portuguese organizations while meeting the ISO/IEC 27001:2013 standard. Moreover, this paper provides discussion on the results obtained, to better understand the progress and status quo of this standard implementation. From the research conducted it can be seen that organizations are becoming heavily concerned with information security issues, mainly due it to the recent cybersecurity incidents occurred. Additionally, certification is recognized as an important instrument to give confidence and demonstrate to all organizational' customers, suppliers and stakeholders that information security components are verified and organized within the organization.

Abstract
This work presents the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward.

Abstract
The spread of biometric applications in mobile devices handled by untrained users opened the door to sources of noise in mobile iris recognition such as larger extent of rotation in the capture and more off-angle imagery not found so extensively in more constrained acquisition settings. As a result of the limitations of the methods in handling such large degrees of freedom there is often an increase in segmentation errors. In this work, a new near-infrared iris dataset captured with a mobile device is evaluated to analyse, in particular, the rotation observed in images and its impact on segmentation and biometric recognition accuracy. For this study a (manually annotated) ground truth segmentation was used which will be published in tandem with the paper. Similarly to most research challenges in biometrics and computer vision in general, deep learning techniques are proving to outperform classical methods in segmentation methods. The utilization of parameterized CNN-based iris segmentations in biometric recognition is a new but promising field. The results presented show how this CNN-based approach outperformed the segmentation traditional methods with respect to overall recognition accuracy for the dataset under investigation.

Abstract
This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results. © 2018 Gesellschaft fuer Informatik.