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An organized strategy to ensure the security of an organization is an information security management system. During various security crises, hazards, and breaches, this strategy aids an organization in maintaining the confidentiality, integrity, and accessibility of information. Organizations are getting ready to comply with information security management system criteria. Despite this, security concerns continue to plague ineffective controls, have poor connectivity, or cause a silo effect, which is a common cause. One of the causes is a low maturity model that is not synchronized with the organization’s business processes. For a higher level of maturity, it is best to evaluate the practices.
Different maturity models on information security and cyber security capacity, management processes, security controls, implementation level, and many more have already been developed by numerous international organizations, experts, and scholars. The present models, however, do not assess a particular organization's particular practices. The evaluation of the business process is frequently neglected because measurement requirements for models are typically more concentrated on examining specific elements. For this reason, it caused the maturity assessment to not be executed explicitly and broadly.
We developed an organizational information security maturity model, a combination of work of different maturity models currently existing. While making this model, we considered that any size or type of organization could use this model. The model considers the success elements of the information security management system when assessing the implementation's effectiveness. We employed a mixed-method strategy that included both qualitative and quantitative research. With the help of a questionnaire survey, we evaluated the previous research using a qualitative methodology. In the quantitative method, we'll figure out how mature the information security management system is now. The proposed model could be used to reduce security incidents by improving implementation gaps.
In den vergangenen Jahren wurden im Rahmen journalistischer Recherchen immer wieder große Datenmengen zu komplexen Firmenkonstruktionen und globalen Finanztransaktionen veröffentlicht, die dem Waschen illegaler Gelder und dem Verschleiern von Vermögenswerten dienten. Als Beispiele seien hierzu die „Panama Papers“ oder „Paradise Papers“ von 2016 genannt, mit denen zahlreiche Briefkastenfirmen und Finanzgeschäfte in Steueroasen gegenüber der Öffentlichkeit aufgedeckt wurden. Die Recherche der Journalisten ordnete den offenbar illegal erworbenen Vermögenswerten auch prominente Namen zu. Als Folge wurden unter anderem Strafverfahren wegen Steuerhinterziehung eingeleitet. Selbst Minister und Regierungschefs mussten zurücktreten. Auch die Europäische Union wurde in den letzten Jahren immer wieder von Geldwäsche-Skandalen erschüttert. Der mit Abstand umfangreichste Fall ereignete sich in der estnischen Filiale der Danske Bank, welche über Jahre massiv die Sorgfaltspflichten missachtet hatte und so in den Jahren 2007 bis 2015 rund 200 Milliarden Euro aus dubiosen russischen Quellen über die Konten der Bank geflossen sein sollen. Es handelt sich derzeit um den weltgrößten Geldwäscheskandal, der bisher aufgedeckt werden konnte. Auch die Deutsche Bank, die als Korrespondenzbank für das dänische Geldhaus tätig war, soll jahrelang verdächtige Transaktionen im Zusammenhang mit der Dankse Bank nicht offengelegt haben. Ferner hatte im Juli 2018 die Berliner Polizei und Staatsanwaltschaft 77 Immobilien einer kurdisch-libanesischen Großfamilie im Wert von zehn Millionen Euro beschlagnahmt. Durch den Kauf der Gebäude sollen illegale Gelder aus Raub und Drogenhandel gewaschen worden sein. Darüber hinaus hat erst im November 2019 das sog. Hawala-Banking in Deutschland Schlagzeilen gemacht. Mehr als 850 Polizeibeamte aus fünf Bundesländern gingen gegen eine vermutlich international agierende kriminelle Vereinigung vor. Die Behörden ermittelten 27 Beschuldigte, die im großen Stil Bargeld ins Ausland transferiert und so über das Hawala-Bankensystem mehr als 200 Millionen Euro aus illegalen Quellen gewaschen haben sollen.
Die Insolvenzzahlen in Deutschland sind das sechste Jahr in Folge rückläufig. Im Jahr 2016 gab es in Deutschland insgesamt 21.700 Unternehmensinsolvenzen. Dies ist der niedrigste Stand seit Einführung der Insolvenzordnung im Jahre 1999. Das weiter rückläufige Insolvenzgeschehen lässt sich auf das grundsätzlich sehr gute Konjunkturumfeld zurückführen. Die gute Binnenkonjunktur und Finanzierungssituation sorgen für steigende Umsätze und Erträge und verbessern die Stabilität der Unternehmen.1 Mit 30,3 % entfiel der zweitgrößte Anteil der Unternehmensinsolvenzen auf die Rechtsform der GmbH, am häufigsten von Insolvenzen betroffen waren mit 48,3 % nach wie vor die Kleingewerbetreibenden.2 Ca. 98 % aller Unternehmen in Deutschland sind kleine und mittlere Unternehmen (KMU).
Kleine Unternehmen haben weniger als zehn Mitarbeiter und einen Jahresumsatz oder eine Jahresbilanzsumme von höchstens zehn Millionen Euro. Mittlere Unternehmen hingegen verfügen über weniger als 250 Mitarbeiter und einen Jahresumsatz von höchstens 50 Millionen Euro oder eine Jahresbilanzsumme von höchstens 43 Millionen Euro. Diese Unternehmen sind überdurchschnittlich oft von Insolvenzen betroffen.3 Eine Auswertung des Statistischen Bundesamtes aus dem Jahr 2014 zeigt, dass die GmbH als gewählte Rechtsform bei den eingetragenen Betriebsgründungen mit 39,3 % weiterhin hoch im Kurs liegt und verdeutlicht somit deren Bedeutung im deutschen Wirtschaftsverkehr.4 Die GmbH ist die typische Gesellschaftsform für kleine und mittlere Unternehmen und wird als Unternehmensträger gewählt, wenn keiner der Beteiligten eine volle persönliche Haftung übernehmen möchte und wenn die Form einer Kapitalgesellschaft zwar gewollt, aber aufgrund der geringen Unternehmensgröße sowie der überschaubaren Gesellschafterzahl die AG nicht geeignet ist.5 Die folgenden Ausführungen beziehen sich daher auf Kapitalgesellschaften in der Rechtsform der GmbH und richten sich vor allem an die Geschäftsführer mittelständischer Unternehmen, in denen z. B. häufig eine fundierte Unternehmensplanung fehlt.
Diffracted waves carry high‐resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high‐accuracy and high‐resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.
Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.
Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.
The recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multiuser environments or parallel programming - making it quite challenging to maintain stable and secure Python workflows on a HPC system. In this paper, we analyze the key problems induced by the usage of Python on HPC clusters and sketch appropriate workarounds for efficiently maintaining multi-user Python software environments, securing and restricting resources of Python jobs and containing Python processes, while focusing on Deep Learning applications running on GPU clusters.
Due to the rapidly increasing storage consumption worldwide, as well as the expectation of continuous availability of information, the complexity of administration in today’s data centers is growing permanently. Integrated techniques for monitoring hard disks can increase the reliability of storage systems. However, these techniques often lack intelligent data analysis to perform predictive maintenance. To solve this problem, machine learning algorithms can be used to detect potential failures in advance and prevent them. In this paper, an unsupervised model for predicting hard disk failures based on Isolation Forest is proposed. Consequently, a method is presented that can deal with the highly imbalanced datasets, as the experiment on the Backblaze benchmark dataset demonstrates.
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.