11,027 research outputs found
Closing the loop of SIEM analysis to Secure Critical Infrastructures
Critical Infrastructure Protection is one of the main challenges of last
years. Security Information and Event Management (SIEM) systems are widely used
for coping with this challenge. However, they currently present several
limitations that have to be overcome. In this paper we propose an enhanced SIEM
system in which we have introduced novel components to i) enable multiple layer
data analysis; ii) resolve conflicts among security policies, and discover
unauthorized data paths in such a way to be able to reconfigure network
devices. Furthermore, the system is enriched by a Resilient Event Storage that
ensures integrity and unforgeability of events stored.Comment: EDCC-2014, BIG4CIP-2014, Security Information and Event Management,
Decision Support System, Hydroelectric Da
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Face recognition technologies for evidential evaluation of video traces
Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
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