An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics

Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201

Automatic Detection of Malware-Generated Domains with Recurrent Neural Models

Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning approach based on recurrent neural networks is able to detect domain names generated by DGAs with high precision. The neural models are estimated on a large training set of domains generated by various malwares. Experimental results show that this data-driven approach can detect malware-generated domain names with a F_1 score of 0.971. To put it differently, the model can automatically detect 93 % of malware-generated domain names for a false positive rate of 1:100.Comment: Submitted to NISK 201

Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence

Cyber threat intelligence is the provision of evidence-based knowledge about existing or emerging threats. Benefits of threat intelligence include increased situational awareness and efficiency in security operations and improved prevention, detection, and response capabilities. To process, analyze, and correlate vast amounts of threat information and derive highly contextual intelligence that can be shared and consumed in meaningful times requires utilizing machine-understandable knowledge representation formats that embed the industry-required expressivity and are unambiguous. To a large extend, this is achieved by technologies like ontologies, interoperability schemas, and taxonomies. This research evaluates existing cyber-threat-intelligence-relevant ontologies, sharing standards, and taxonomies for the purpose of measuring their high-level conceptual expressivity with regards to the who, what, why, where, when, and how elements of an adversarial attack in addition to courses of action and technical indicators. The results confirmed that little emphasis has been given to developing a comprehensive cyber threat intelligence ontology with existing efforts not being thoroughly designed, non-interoperable and ambiguous, and lacking semantic reasoning capability

Reviewing BPMN as a Modeling Notation for CACAO Security Playbooks

As cyber systems become increasingly complex and cybersecurity threats become more prominent, defenders must prepare, coordinate, automate, document, and share their response methodologies to the extent possible. The CACAO standard was developed to satisfy the above requirements, providing a common machine-readable framework and schema for documenting cybersecurity operations processes, including defensive tradecraft and tactics, techniques, and procedures. Although this approach is compelling, a remaining limitation is that CACAO provides no native modeling notation for graphically representing playbooks, which is crucial for simplifying their creation, modification, and understanding. In contrast, the industry is familiar with BPMN, a standards-based modeling notation for business processes that has also found its place in representing cybersecurity processes. This research examines BPMN and CACAO and explores the feasibility of using the BPMN modeling notation to represent CACAO security playbooks graphically. The results indicate that mapping CACAO and BPMN is attainable at an abstract level; however, conversion from one encoding to another introduces a degree of complexity due to the multiple ways CACAO constructs can be represented in BPMN and the extensions required in BPMN to support CACAO fully

The Impact of Quantum Computing on Present Cryptography

The aim of this paper is to elucidate the implications of quantum computing in present cryptography and to introduce the reader to basic post-quantum algorithms. In particular the reader can delve into the following subjects: present cryptographic schemes (symmetric and asymmetric), differences between quantum and classical computing, challenges in quantum computing, quantum algorithms (Shor's and Grover's), public key encryption schemes affected, symmetric schemes affected, the impact on hash functions, and post quantum cryptography. Specifically, the section of Post-Quantum Cryptography deals with different quantum key distribution methods and mathematicalbased solutions, such as the BB84 protocol, lattice-based cryptography, multivariate-based cryptography, hash-based signatures and code-based cryptography.Comment: 10 pages, 1 figure, 3 tables, journal article - IJACS

Profiling student smokers:a behavioral approach

The aim of the present study is to construct a coherent profile of student smokers in Greece, based on their behavioral and demographic characteristics. In this context, we collected data by administrating an anonymous self-completed questionnaire, which was answered by students of University and Technological Educational Institute (T.E.I.) of Patras. The final sample consists of 1,190 student smokers. For the purposes of the present study, principal component analysis was utilized to explore and detect the demographic and behavioral profiles of Greek student smokers. The factor solution identified 5 demographic factors and 14 behavioral factors. All factors were labeled, interpreted and discussed in the light of existing knowledge in order to understand better the consumer behavior of student smokers