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

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

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

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

Electronic Letters on Computer Vision and Image Analysis 5(3):84-95, 2005 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

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts

Simulation platform for cyber-security and vulnerability analysis of critical infrastructures

The progressive advances in information and communication technology have lend modern critical infrastructures to become more and more complex and interconnected, and in continuous evolution. The increasing complex interrelation among such critical systems creates new security vulnerabilities, which can be exploited by malicious users to compromise sensible data and other systems also very far from the impact zone. Identifying and analyzing these complex interactions represent a challenge to the evaluation of the real vulnerability of each critical system. On the other hand, the evaluation of this complex and large-scale systems requires expensive and sophisticated modeling practices, simulation tools, and experimentation infrastructure. Therefore, we present a hybrid and distributed simulation platform for cyber-security analysis of largescale critical infrastructure systems. It enables testers to assemble complex and distributed experimental scenarios in the cloud, by integrating different simulated environments, on which perform sophisticated vulnerability analysis, by exploiting penetration testing and monitoring facilities

Netflow-Based Malware Detection and Data Visualisation System

Part 7: Various Aspects of Computer SecurityInternational audienceThis paper presents a system for network traffic visualisation and anomalies detection by means of data mining and machine learning techniques. First, this work describes and analyses existing solutions in the field of network anomalies detection in order to identify adapted techniques in that area. Afterwards, the system architecture and the adapted tools and libraries are presented. Particularly, two different anomalies detection methods are proposed.The key experiments and analysis focus on performance evaluation of the proposed algorithms. In particular, different setups are considered in order to evaluate such aspects as detection effectiveness and computational complexity.The obtained results are promising and show that the proposed system can be considered as a useful tool for the network administrator