Was ist das eigentlich? Cyberrisiken verständlich erklärt

Es wird viel über Cyberrisiken gesprochen. Oftmals fehlt aber das grundsätzliche Verständnis, was Cyberrisiken überhaupt sind. Ohne diese zu verstehen, lässt sich aber auch kein Versicherungsschutz gestalten.

Beinahe alle Aktivitäten des täglichen Lebens können heute über das Internet abgewickelt werden. Online-Shopping und Online-Banking sind im Alltag angekommen. Diese Entwicklung trifft längst nicht nur auf Privatleute, sondern auch auf Firmen zu. Das Schlagwort Industrie 4.0 verheißt bereits eine zunehmende Vernetzung diverser geschäftlicher Vorgänge über das Internet.

Anbieter von Cyberversicherungen für kleinere und mittelständische Unternehmen (KMU) haben Versicherungen die Erfahrung gemacht, dass trotz dieser eindeutigen Entwicklung Cyberrisiken immer noch unterschätzt werden, da sie als etwas Abstraktes wahrgenommen werden. Für KMU kann dies ein gefährlicher Trugschluss sein, da gerade hier Cyberattacken existenzbedrohende Ausmaße annehmen können. So wird noch häufig gefragt, was Cyberrisiken eigentlich sind. Diese Frage ist mehr als verständlich, denn ohne (Cyber-)Risiken bestünde auch kein Bedarf für eine (Cyber-)Versicherung.

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EX300 test Format | EX300 Course Contents | EX300 Course Outline | EX300 test Syllabus | EX300 test Objectives

The performance-based Red Hat Certified Engineer (RHCE) test for Red Hat Enterprise Linux 7 (EX300) tests to determine if your knowledge, skill, and ability meet those required of a senior system administrator responsible for Red Hat® Enterprise Linux® systems. Red Hat Certified System Administrator (RHCSA®) certification is required to earn RHCE® certification.

The test based on Red Hat Enterprise Linux 7 is available via on-site and individual exams until July 1, 2020.

An RHCE certification is earned by a Red Hat Certified System Administrator (RHCSA) who has demonstrated the knowledge, skill, and ability required of a senior system administrator responsible for Red Hat Enterprise Linux systems

Local and remote logins

Review methods for accessing the system and engaging Red Hat Support.

File system navigation

Copy, move, create, delete, link, and organize files while working from the Bash shell prompt.

Users and groups

Manage Linux users and groups and administer local password policies.

File permissions

Control access to files and directories using permissions and access control lists (ACLs).

SELinux permissions

Manage the SELinux behavior of a system to keep it secure in case of a network service compromise.

Process management

Evaluate and control processes running on a Red Hat Enterprise Linux system.

Updating software packages

Download, install, update, and manage software packages from Red Hat and yum package repositories.

Creating and mounting file systems

Create and manage disks, partitions, and filesystems from the command line.

Service management and boot troubleshooting

Control and monitor system daemons and troubleshoot the Red Hat Enterprise Linux boot process.

Network configuration

Configure basic IPv4 networking on Red Hat Enterprise Linux systems.

System logging and ntp

Locate and accurately interpret relevant system log files for troubleshooting purposes.

Logical volume management

Create and manage logical volumes from the command line.

Scheduled processes

Schedule tasks to automatically execute in the future.

Mounting network file systems

Use autofs and the command line to mount and unmount network storage with NFS and SMB.

Firewall configuration

Configure a basic firewall.

Virtualization and kickstart

Automate the installation of Red Hat Enterprise Linux on virtual machines with kernel-based virtual machine (KVM) and libvirt.

Managing IPv6 networking

Configure and troubleshoot basic IPv6 networking on Red Hat Enterprise Linux systems.

Configuring link aggregation and bridging

Configure and troubleshoot advanced network interface functionality including bonding, teaming, and local software bridges.

Controlling network port security

Permit and reject access to network services using advanced SELinux and firewalld filtering techniques.

Managing DNS for Servers

Set and verify correct DNS records for systems and configure secure-caching DNS.

Configuring E-mail Delivery

Relay all e-mail sent by the system to a SMTP gateway for central delivery.

Providing block-based storage

Provide and use networked iSCSI block devices as remote disks.

Providing file-based storage

Provide NFS exports and SMB file shares to specific systems and users.

Configuring MariaDB databases

Provide a MariaDB SQL database for use by programs and database administrators.

Providing Apache HTTPD Web Service

Configure Apache HTTPD to provide Transport Layer Security (TLS)-enabled websites and virtual hosts.

Writing Bash scripts

Write simple shell scripts using Bash.

Bash conditionals and control structures

Use Bash conditionals and other control structures to write more sophisticated shell commands and scripts.

Configuring the shell environment

Customize Bash startup and use environment variables, Bash aliases, and Bash functions.

Lab content summary

Managing and troubleshooting systemd services during the boot process

Network configuration and basic troubleshooting

Managing local storage, creating and using file systems

Firewall management with firewalld

Automating installation of Red Hat Enterprise Linux® using kickstart

Manage SELinux settings

Using NFS and Samba shared filesystems

iSCSI initiator and target configuration

Domain Name System (DNS) troubleshooting and caching name server

Providing Network File System (NFS) and Server Message Block (SMB) file servers

Apache HTTPD web server management

MariaDB SQL database configuration

Postfix Simple Mail Transfer Protocol (SMTP) nullclient for servers

Bash scripting for automation

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Redhat Engineer learn


Why people are still key to tech transformation

From the mass adoption of ChatGPT to generative AI infiltrating almost every industry (75% of business leaders rank generative AI as the top emerging technology that will impact business over the next 18 months), digital transformation has become inevitable for most organizations.

And while much of the recent conversation surrounding digital transformation has focused on the humans versus robots angle, businesses that ignore their human capital and purely target technology as a means to grow and prosper do so at their peril.

That’s according to Donal Spring, Principal Architect at Red Hat’s Open Innovation Labs. Speaking recently at the Dublin Tech Summit, Spring highlighted that although transformation is inevitable, it needs to be anchored by the people tasked with achieving progression and change, instead of solely focusing on deploying tech at scale to achieve a competitive advantage.

“To be pretty blunt, I’m not a big fan of the big ‘T’ word, the big transformation word. I feel like it means so much to so many people that it can kind of become a little bit toxic,” he said.

“Organizations have been on big underscore transformation projects, whether it’s agile transformation, or DevOps transformation, digital transformations, and really, they need to move it back to what it is we’re actually trying to achieve? And what measures are they setting around those things?”

Spring says that successful transformation is all about the people.

“Adopting some new piece of technology and expecting it to get 10x time to market because it says so on the box is a pretty unrealistic expectation. They need to think about the people and process side of any technology change, because you can’t have one without the other.”

Cross-functional teams that merge talent from various aspects of the business are also crucial, but arguably, talent is just one piece of the puzzle.

According to a recent report by McKinsey, the ability to develop and deploy a clear strategy instead of being reactionary is key. It highlights that focusing on specific areas, for example customer service or internal processes, is the best way to do this.

Either way, the good news for tech professionals is that their technical expertise and soft skills such as critical thinking and communication underpin the kind of digital transformation that can be quantified in terms of growth, profitability and innovation.

And if you’re looking for your next career challenge, the Venturebeat Job Board is the perfect place to start your search.

It features thousands of jobs in companies that are actively hiring, like the three below.

Senior Developer Technology Engineer — AI, NVIDIA, Santa Clara

NVIDIA is looking for a computer scientist to work in its Compute Developer Technology (Devtech) team as an AI Developer Technology Engineer. In this role you’ll study and develop cutting-edge techniques in deep learning, graphs, machine learning and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures. You will also work directly with key customers to understand the current and future problems they are solving and provide the best AI solutions using GPUs and collaborate closely with the architecture, research, libraries, tools and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms and programming models. View more details here.

Talent Transformation Strategist, Lead, Booz Allen Hamilton, McLean

Are you passionate about helping organizations manage talent effectively? In this Talent Transformation Strategist role you will use your expertise in management consulting to examine complex systems through an innovation perspective, and make recommendations for improving organizational strategy, structure, human capital and management practices. As a client engagement lead, you will often act as the day-to-day POC for the client, including providing management, administrative, and technical interface between the client and the Booz Allen team. You will also serve as career manager to staff, including providing direct manager guidance, support for career development and oversight to resolving concerns. See the full job description here.

Software Engineer Lead – Integration Engineer (Node JS & TypeScript), Capgemini, Atlanta

Capgemini is looking for an Integration Engineer with expertise in Node.js and TypeScript to join its growing team. As a global leader in in partnering with companies to transform and manage their business by harnessing the power of technology the ideal candidate will have at least eight years’ of Java/Spring Boot microservices development and design experience, over five years’ of Cosmos DB or NoSql experience, proficiency using Azure and five years’ experience of troubleshooting and Okta API security experience. Get more information about this role here.

For more great roles or to find your dream job, check out the Venturebeat Job Board today.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover their Briefings.

Red Hat CEO on AI moves and source code kerfuffle

At the Red Hat Summit earlier this year, Red Hat deepened its platform capabilities with OpenShift AI to address the needs of organisations that are set to add more artificial intelligence (AI) workloads into the mix of applications that run on OpenShift.

The move is a natural extension of the company’s goal to be the platform of choice for application developers and infrastructure operators to build and run applications in a distributed IT environment that spans public and private clouds, as well as at the edge of the network.

With OpenShift AI, Red Hat is providing a standardised foundation for creating production AI and machine learning models. It has also teamed up with IBM on Ansible Lightspeed, with Big Blue training its Watson Code Assistant to write Ansible automation playbooks.

Red Hat’s AI moves, however, were somewhat overshadowed by the reaction from the open source community over its decision to limit access to the source code of Red Hat Enterprise Linux (RHEL) to its customers. The decision, announced about a month after the summit, was aimed at preventing rebuilders from profiting from RHEL code without adding value to the software.

In an interview with Computer Weekly, Red Hat CEO Matt Hicks talks up the company’s efforts to support the use of generative AI across the hybrid cloud environment and the competitive landscape for machine learning operations (MLOps) tooling. He also weighs in on the RHEL source code kerfuffle, and how Red Hat is addressing community concerns over the decision.

Could you unpack some of the key announcements at the recent Red Hat Summit and what they mean for the company moving forward?

Hicks: I’ll start with AI and go backwards because I think it has become pretty clear that AI, by nature, is going to be a hybrid workload. You’re probably going to train models in large environments, and then you’re going to run those models as close to your users as you can. We’ve believed in open hybrid cloud for a long time and that’s an exciting workload that gets customers in the hybrid architecture mentality.

Most enterprise customers, because of things like ChatGPT, are trying to figure out the impact of AI on their business. It gets them to think about how they can do hybrid well, and the bulk of their summit announcements is about setting the foundation for hybrid, whether it’s for traditional apps, cloud-native apps or AI workloads.

We do this in a couple of different ways, starting with the secure supply chain work we’re doing. As you know, technology stacks are changing quickly, and so when you’re delivering a foundation, whether it’s on-premise, in public cloud or eventually towards the edge, understanding the provenance of that foundation and knowing that it’s secure is critical, especially as stuff moves out of your datacentre.

Someone can always go into CentOS and all the code is there to recompose, but their preference for Linux distributions is to add something novel or specialised to make a distribution better in ways that were not before versus reproducing their operating system as close as you can Matt Hicks, Red Hat

Service Interconnect is the second piece they announced. It makes it easier for applications to connect to components across the hybrid cloud through SSH [secure shell protocol] tunnels and VPNs [virtual private networks]. We’re really excited about that because they believe that AI is not going to exist by itself – it’ll be running next to applications which have to interconnect from training environments to where your business runs today.

The third piece is the developer hub. We’ve seen many enterprises that use OpenShift build their own portals to collect their assets and point their developers to where they should start, such as the images and services to use. That work is so common that if you have a secure foundation, and you build applications that span multiple locations, being able to publish and consume those to enable broader development teams is equally critical.

What about addressing some of the challenges with AI like explainability, particularly with large language models that have billions, even trillions, of parameters?

Hicks: There are two parts to that – there’s one part they do, and the other part depends on the model creator. I’ll talk about the work they did on Ansible Lightspeed with IBM to deliver domain specific AI generation capabilities where you can ask for a playbook, and we’ll generate it for you. While ChatGPT is very broad, this is very specific and suits Ansible really well.

And to your point, one of the things they highlighted was sourcing, specifically where the AI recommendations came from, because we’re in the business of open source. Licensing, copyright and trademark rules are important – you just can’t take any code you want and put it in any other code. They wanted to make sure they demonstrated what could be possible.

Now where this breaks down is actually across the two stacks. With OpenShift, they help to support a whole class of work in DevOps – source code management, peer review, publishing of code, tagging, knowing your release modules, pipelines and then publishing code. That’s what they do really well in OpenShift. They can take this whole collection of stuff and move code from laptops to production.

AI models are not all that different. In terms of the discipline required, you need to know the model you started from. If it’s generative AI, you need to know exactly what data you brought in and how you trained or did refinement training or prompt engineering. You need to be able to track the output and test against it before you publish it into production, so if a result changes, you know where it came from. This is the tricky part as data changes so quickly that you can’t just publish it and not retrain it. Retraining is going to be as constant as code generation.

So, what they do in OpenShift AI is MLOps – pulling in data, training models, and using very similar pipelines as you would with code. But you need to have a foundation model, and this leads to how the model was trained in the first place, which is something that Red Hat does not do. It’s done by the likes of IBM, Meta, OpenAI and other model generators and within Hugging Face, there’s also a lot of open source model generation.

In the case of IBM, they tightly control their model because it was domain specific to Ansible. They tightly controlled what they trained against so they could drive that core attribution at the end. There are two different camps – some train on everything publicly available, giving you those massive parameter models where attribution will always be a challenge. Then, there’s Hugging Face which has a lot of specialised models that may start with a foundation model but are bounded to domains.

Our goal is to make sure they can add that discipline to what you started with. What did you change in terms of data? How did you retrain? What were the results and where was it published? There’s a lot of training right now, but in the next year or two, they think we’ll move more into the inference space and how you iterate becomes critical.

Are there plans to work with other players in the market apart from IBM? Also, Red Hat has deep relationships with hyperscalers which also have MLOps capabilities – what are your thoughts on the competitive landscape?

Hicks: One of the reasons why they don’t do models is that they want to make sure that we’re a platform company. Their job is to run the best models in the best way possible. How can they use RHEL and OpenShift to bridge a model – whichever one it is – to Nvidia, Intel or AMD hardware to drive training and inference? Not being in the model space makes us a natural partner with everybody and it really becomes a hardware statement. How can they get the most out of the training environment on OpenShift distributed computing, and then inference, which a lot of times comes closer to core RHEL or maybe a smaller OpenShift instance. So, that’s the first layer.

The second layer when they look at OpenShift AI is that they partner with a lot of other companies today that add specialised capabilities, whether it’s Starburst that’s looking at function array and others. It’s exciting to see the work that IBM is doing on Watsonx. They’ve utilised OpenShift AI heavily, but they were comfortable with OpenShift to start with. Their goal is to make sure that as a platform company, they have that neutrality and independence. I’m glad they can serve IBM, but there will be other partners as well because there’s just so much specialisation and niche offerings in this space.

We've had this incredible run and opportunity of making Linux, OpenShift and Ansible successful in the enterprise. But the walls of the datacentre are shifting, and new technologies are changing how enterprises build things. That's their next opportunity to tackle and there's still plenty of work going from datacentres to cloud Matt Hicks, Red Hat

I met with SUSE’s CEO recently and they spoke about the recent decision by Red Hat to limit access to RHEL’s source code to its customers. A lot has been written by Red Hat executives to explain the rationale, but how are you framing the issue for customers and addressing community concerns over the decision?

Hicks: I’ll tackle that in two ways. On the community concerns, I think half of it is people just starting to realise that they brought access to RHEL, whether it’s RHEL for teams or multiple instances available for non-production use or free RHEL available to individuals and hobbyists. Their goal first is, if you are a contributor to Linux, they never want to stand in your way of using their products. And I think we’ve probably removed a lot of those barriers almost a year ago. Is there room to Excellerate as people use RHEL more? Absolutely, and that’s one part of making sure RHEL is available to that audience.

When they get to communities that want to build a specialised Linux or start from some of the work that we’ve done but take it in a different direction, their argument would be that CentOS Stream, in terms of the next version of RHEL, provides you with everything you need. If you want to make more aggressive changes to it, Fedora provides you with everything that you would need. Your contributions there can then flow into RHEL, if they choose.

The bit-for-bit rebuilding of RHEL just doesn’t serve a use case for us. Now, someone can always go into CentOS and all the code is there to recompose, but their preference for Linux distributions is to add something novel or specialised to make a distribution better in ways that were not before versus reproducing their operating system as close as you can.

As for their customers, most of them don’t live in the same world as the community builders. Their source policy with RHEL covers their customer base extremely well, because if you need the source code as a customer, you’ll get them. They have customers that have used RHEL and CentOS, and that’s certainly a decision point for them. But Linux is the most available operating system on the planet, and so they have plenty of options to choose from. We’ll always want to make sure they can serve them with RHEL, but it hasn’t really been a customer challenge.

I’d say the challenge is communities feeling like they took something away from them. And half of that is just not being super familiar with CentOS Stream and not being familiar with the ways that RHEL is available to them. We’ve been in open source for a while and any change you make in open source tends to get really strong reactions. They still hold true to open source – they still open source everything they do, and they still contribute back a tremendous amount for every dollar they make.

In your letter to Red Hat employees about the recent layoffs, you mentioned about the importance of focusing on things that Red Hat does really well. Can you elaborate on what those things are and what you hope to achieve?

Hicks: It’s a great question and I start almost every company meeting saying, ‘Let’s be comfortable that they are a platform company’. We’re going to sit above hardware, and in the world of edge, on new consolidated boxes outside the datacentre. And we’re going to connect that to applications, whether they are traditional applications or new cloud-native apps. And then you’re going to have AI workloads going forward.

Our job, from the RHEL days to middleware with JBoss to OpenShift and distributed computing, is to make sure that developers who want to build with their platform have the widest reach possible. That’s important because there are so many things changing right now. When you look at just the intersection of edge and AI, to be a platform company, they have to serve that market and that class of workload, which means they have to be investing in engineering and sales.

We’ve had this incredible run and opportunity of making Linux, OpenShift and Ansible successful in the enterprise. But the walls of the datacentre are shifting, and new technologies are changing how enterprises build things. That’s their next opportunity to tackle and there’s still plenty of work going from datacentres to cloud. But they have to keep that relentless focus on being a platform and serving those use cases.

That’s what they want to do very well and some of that work is in the operating system space in areas like securing the software supply chain, PyTorch optimisations or Nvidia integration. Some work will go into distributed computing, which is what we’re doing with OpenShift, and there’s a whole lot of work in orchestration around Ansible.

We will certainly invest in areas outside of those three, but if I do my job right, you’ll never see us invest in an area that you can’t pin back to that platform use case. I think that’s a pretty big market for us right now. They know the dynamics of this market, they know how to sell into this market, and they have the talent in this market. There’s enough opportunity in these evolving areas to focus on.

MS in Machine Learning Engineering

MS in Machine Learning Engineering


The master’s in machine learning engineering from Drexel Engineering prepares professionals to take on the transformation of science and technology impacted by the field, leading to a successful career in an exciting discipline.

What is a MS in Machine Learning Engineering?

Science and engineering is being transformed through the application of machine learning techniques and principles. A graduate degree program in machine learning engineering allows you to earn the skillsets that help you and your organization best leverage its data, incorporate the coming wave of automation in all its varieties, and understand and explore the potential ways machine learning can Excellerate their lives and environment.

A master’s in machine learning engineering provides knowledge in these three important pillars:

  • Fundamentals: Become an expert in the underpinnings of modern machine learning while drawing from an understanding of fundamental principles from various disciplines in order to develop and innovate successful solutions that are best suited to a given problem.
  • Implementation: Integrate industry-leading software tools to rapidly prototype machine learning systems. Gain exposure to novel computing architectures of machine learning for implementation of new and advanced outcomes.
  • Applications: A graduate program should actively demonstrate how the discipline is put to use in cutting-edge areas where machine learning is being applied in industries ranging from technology, healthcare, bioengineering, smart-cities, the Internet-of-Things, cybersecurity and many others.
  • A machine learning engineering master’s program should provide an understanding of the forces governing industry, a global viewpoint, and the entrepreneurial, teambuilding and managerial abilities needed to advance careers in business and research or prepare you for entry into a PhD program in a related field.

  • On-campus
  • Full-time or part-time
  • The program will take approximately 18 months to complete on a full-time basis or can be completed on a part time basis in 3-4 years.
  •   Why choose Drexel for your Machine Learning Engineering Degree?

    The degree program leverages a long history of producing machine learning experts. Designed with working professionals in mind, graduates go on to obtain positions in diverse fields ranging from business analytics and healthcare to finance and defense, as well as with leading tech companies such as Facebook, Google, Amazon, and Microsoft. 

    Students in lab

    Students in the machine learning degree program gain the ability to implement machine learning systems using cutting-edge software libraries including Keras, TensorFlow, and scikit-learn. You will benefit from classes taught by elite world-leading research experts in areas such as music understanding, image and video authentication, intelligent wireless systems, robotics, cell and tissue image analysis, genomics and bioinformatics. You will emerge prepared to lead and take on the demands of a fast-changing industry, or to continue study in a doctoral program in electrical engineering or related subject.

    In the Department of Electrical and Computer Engineering (ECE), and at Drexel, you are encouraged to be innovative and imaginative in identifying the problem and analyzing through critical thinking. The program aims to equip you with the tools for finding sustainable and achievable outcomes to address society’s biggest challenges while also making them relevant to your career goals.


    The city of Philadelphia is their campus – a diverse urban environment with a variety of social, cultural and learning opportunities that will enrich your educational experience. Philadelphia is also a draw for talented instructors and researchers, meaning you will engage with some of the best minds in engineering and other disciplines. Learn more.

    Graduate Co-op

    Graduate co-op is an optional three or six-month work experience woven into academic studies for full-time master’s students. Drexel University co-op provides the opportunity to apply theory learned in class to a work experience before graduating. The insights help to direct the vision you have for your career and provide context for the remainder of your learning. You will take advantage of resources from the Steinbright Career Development Center, including programming that enhances your professionalism and resume writing and provides resources for your job search.

    For more information, visit the Steinbright Career Development Center.

    Curriculum and Requirements Core coursework 12 credits Aligned Mathematical Theory courses (ECE) 6 credits Applications, Signal Processing (1 course each) 6 credits Transformational Electives 6 credits Engineering Electives 9 credits Mastery (Thesis or Non-Thesis option) 6 credits
  • The Master of Science in Machine Learning Engineering plan of study requires a total of 45 credits; 12 credits in core courses; 6 credits of mathematical theory, 3 credits in each applications and signal processing, 9 credits in engineering electives and 6 credits in transformational electives.
  • Students have a choice of a thesis or a non-thesis option of electives or combined with 9 credits of thesis research, recommended for those interested in doctoral study.
  • Graduate advisors are available to guide your course selection and scheduling of core and elective courses. Learn more about the Master’s Thesis option.
  • Dual graduate degrees are also possible. For instance, the degree pairs well with the MS in Computer Engineering, MS in Cybersecurity, or MS in Engineering Management.
  • Visit the Drexel Catalog for more information or learn more about their admissions requirements.


    While not a requirement, all students in the master’s in machine learning engineering program are welcome to engage in research as part of their degree or as extra-curricular participation. Full-time master’s degree candidates or those interested in pursuing a PhD are encouraged to base their master’s thesis on some aspect of faculty research.

    Our labs house research conducted by their world-renowned faculty, funded by the U.S. Departments of Defense, Transportation, Health and Human Services, Commerce and Homeland Security as well as with many notable industry partners.

    Current research in electrical engineering provides opportunities to participate in research being conducted in machine learning labs such as:

    Visit research areas for more about other research activity at the College of Engineering.

    Dr. Matthew Stamm's research uses signal processing and machine learning to help determine when images are real, and more importantly, when they are not.

    Read Story Career Opportunities in Machine Learning Engineering

    A machine learning engineering graduate program will prepare you for a career path that could include continuing your education in a PhD program or pursuing advanced technical positions or management in nearly every technology-based industry such as telecommunications companies, high-tech industries, smart manufacturing, electronics manufacturing, information security, automation or robotics.  According to Indeed.com, job postings for Machine Learning Engineers have grown 344% from 2015-2018 and a Machine Learning Engineer position commands an average base salary of $146,085 per year. Overall, employees with graduate degrees can earn up to 28 percent more than bachelor’s degree holders over the course of their career.


    Drexel places a high value on industry connection and teaching. The ECE department’s deep bench of machine learning research expertise allows students to explore related Topics at the forefront of the industry.  

    Apply Now Graduate Admissions Department Page


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    Warum sind Cyberrisiken so schwer greifbar?

    Als mehr oder weniger neuartiges Phänomen stellen Cyberrisiken Unternehmen und Versicherer vor besondere Herausforderungen. Nicht nur die neuen Schadenszenarien sind abstrakter oder noch nicht bekannt. Häufig sind immaterielle Werte durch Cyberrisiken in Gefahr. Diese wertvollen Vermögensgegenstände sind schwer bewertbar.

    Obwohl die Gefahr durchaus wahrgenommen wird, unterschätzen viele Firmen ihr eigenes Risiko. Dies liegt unter anderem auch an den Veröffentlichungen zu Cyberrisiken. In der Presse finden sich unzählige Berichte von Cyberattacken auf namhafte und große Unternehmen. Den Weg in die Presse finden eben nur die spektakulären Fälle. Die dort genannten Schadenszenarien werden dann für das eigene Unternehmen als unrealistisch eingestuft. Die für die KMU nicht minder gefährlichen Cyber­attacken werden nur selten publiziert.

    Aufgrund der fehlenden öffentlichen Meldungen von Sicherheitsvorfällen an Sicherheitsbehörden und wegen der fehlenden Presseberichte fällt es schwer, Fakten und Zahlen zur Risikolage zu erheben. Aber ohne diese Grundlage fällt es schwer, in entsprechende Sicherheitsmaßnahmen zu investieren.

    Erklärungsleitfaden anhand eines Ursache-Wirkungs-Modells

    Häufig nähert man sich dem Thema Cyberrisiko anlass- oder eventbezogen, also wenn sich neue Schaden­szenarien wie die weltweite WannaCry-Attacke entwickeln. Häufig wird auch akteursgebunden beleuchtet, wer Angreifer oder Opfer sein kann. Dadurch begrenzt man sich bei dem Thema häufig zu sehr nur auf die Cyberkriminalität. Um dem Thema Cyberrisiko jedoch gerecht zu werden, müssen auch weitere Ursachen hinzugezogen werden.

    Mit einer Kategorisierung kann das Thema ganzheitlich und nachvollziehbar strukturiert werden. Ebenso hilft eine solche Kategorisierung dabei, eine Abgrenzung vorzunehmen, für welche Gefahren Versicherungsschutz über eine etwaige Cyberversicherung besteht und für welche nicht.

    Die Ursachen sind dabei die Risiken, während finanzielle bzw. nicht finanzielle Verluste die Wirkungen sind. Cyberrisiken werden demnach in zwei Hauptursachen eingeteilt. Auf der einen Seite sind die nicht kriminellen Ursachen und auf der anderen Seite die kriminellen Ursachen zu nennen. Beide Ursachen können dabei in drei Untergruppen unterteilt werden.

    Nicht kriminelle Ursachen

    Höhere Gewalt

    Häufig hat man bei dem Thema Cyberrisiko nur die kriminellen Ursachen vor Augen. Aber auch höhere Gewalt kann zu einem empfindlichen Datenverlust führen oder zumindest die Verfügbarkeit von Daten einschränken, indem Rechenzentren durch Naturkatastrophen wie beispielsweise Überschwemmungen oder Erdbeben zerstört werden. Ebenso sind Stromausfälle denkbar.

    Menschliches Versagen/Fehlverhalten

    Als Cyberrisiken sind auch unbeabsichtigtes und menschliches Fehlverhalten denkbar. Hierunter könnte das versehentliche Veröffentlichen von sensiblen Informationen fallen. Möglich sind eine falsche Adressierung, Wahl einer falschen Faxnummer oder das Hochladen sensibler Daten auf einen öffentlichen Bereich der Homepage.

    Technisches Versagen

    Auch Hardwaredefekte können zu einem herben Datenverlust führen. Neben einem Überhitzen von Rechnern sind Kurzschlüsse in Systemtechnik oder sogenannte Headcrashes von Festplatten denkbare Szenarien.

    Kriminelle Ursachen


    Hackerangriffe oder Cyberattacken sind in der Regel die Szenarien, die die Presse dominieren. Häufig wird von spektakulären Datendiebstählen auf große Firmen oder von weltweiten Angriffen mit sogenannten Kryptotrojanern berichtet. Opfer kann am Ende aber jeder werden. Ziele, Methoden und auch das Interesse sind vielfältig. Neben dem finanziellen Interesse können Hackerangriffe auch zur Spionage oder Sabotage eingesetzt werden. Mögliche Hackermethoden sind unter anderem: Social Engineering, Trojaner, DoS-Attacken oder Viren.

    Physischer Angriff

    Die Zielsetzung eines physischen Angriffs ist ähnlich dem eines Hacker­angriffs. Dabei wird nicht auf die Tools eines Hackerangriffs zurückgegriffen, sondern durch das physische Eindringen in Unternehmensgebäude das Ziel erreicht. Häufig sind es Mitarbeiter, die vertrauliche Informationen stehlen, da sie bereits den notwendigen Zugang zu den Daten besitzen.


    Obwohl die Erpressung aufgrund der eingesetzten Methoden auch als Hacker­angriff gewertet werden könnte, ergibt eine Differenzierung Sinn. Erpressungsfälle durch Kryptotrojaner sind eines der häufigsten Schadenszenarien für kleinere und mittelständische Unternehmen. Außerdem sind auch Erpressungsfälle denkbar, bei denen sensible Daten gestohlen wurden und ein Lösegeld gefordert wird, damit sie nicht veröffentlicht oder weiterverkauft werden.

    Ihre Cyberversicherung sollte zumindet folgende Schäden abdecken:


    • Soforthilfe und Forensik-Kosten (Kosten der Ursachenermittlung, Benachrichtigungskosten und Callcenter-Leistung)
    • Krisenkommunikation / PR-Maßnahmen
    • Systemverbesserungen nach einer Cyber-Attacke
    • Aufwendungen vor Eintritt des Versicherungsfalls

    Cyber-Drittschäden (Haftpflicht):

    • Befriedigung oder Abwehr von Ansprüchen Dritter
    • Rechtswidrige elektronische Kommunikation
    • Ansprüche der E-Payment-Serviceprovider
    • Vertragsstrafe wegen der Verletzung von Geheimhaltungspflichten und Datenschutzvereinbarungen
    • Vertragliche Schadenersatzansprüche
    • Vertragliche Haftpflicht bei Datenverarbeitung durch Dritte
    • Rechtsverteidigungskosten


    • Betriebsunterbrechung
    • Betriebsunterbrechung durch Ausfall von Dienstleister (optional)
    • Mehrkosten
    • Wiederherstellung von Daten (auch Entfernen der Schadsoftware)
    • Cyber-Diebstahl: elektronischer Zahlungsverkehr, fehlerhafter Versand von Waren, Telefon-Mehrkosten/erhöhte Nutzungsentgelte
    • Cyber-Erpressung
    • Entschädigung mit Strafcharakter/Bußgeld
    • Ersatz-IT-Hardware
    • Cyber-Betrug