Technology

Asset Administration Shell as a catalyst of Industry 4.0

“Country of poets and thinkers” or ” Country of ideas”: Germany is proud of its writers, scientists, researchers, and engineers. And of its meticulous bureaucracy, which aims for absolute precision in statements or indications. Combined, this often results in awkward word creation when naming technical terms. A current example of this is the “Verwaltungsschale” (literally: administration shell), whose innovative potential and central relevance for Industry 4.0 are not immediately apparent.

What exactly is a “Verwaltungsschale”?

The “Verwaltungsschale” is not a dusty administrative authority, but the very German translation of the English term “Asset Administration Shell” (AAS). The AAS is a standardized complete digital description of an asset. An asset is basically anything that can be connected as part of an Industrie 4.0 solution (for example, plants, machines, products as well as their individual components). It contains all information and enables the exchange and interaction between different assets, systems, and organizations in a networked industry. Therefore, it is pretty much the opposite of a sluggish authority and currently the buzzword in digital transformation.

As with many new topics, definitions of AAS vary and are quite broad. From very specific like the Asset Administration Shell as an implementation of the digital twin for Industry 4.0 to the loose description of AAS as a data plug or integration plug for digital ecosystems.

I prefer the representation of the AAS as a metamodel for self-describing an asset. With this metamodel, further models can be generated to provide collected information. Through the use of software, these models are then “brought to life” and are made available to others via interfaces.

Concept and usage of the Asset Administration Shell

As a digital representation of an asset, the AAS provides information or functions related to a specific context through its submodels. Examples include digital nameplates, technical documents, the component or asset structure, simulation models, time series data, or sustainability-relevant information such as the carbon footprint. The information is generated along the various phases of the lifecycle, and it depends on the specific value network which asset information is of importance. Thus, submodels are initially created in certain lifecycle phases, specified and elaborated in subsequent phases, and enriched or updated with information in the further process. Thereby, the AAS refers to either a very generic (type) or a very concrete (instance) representation of an asset.

As assets change over time (as-defined, as-designed, as-ordered, as-built, as-maintained), so does the Asset Administration Shell. Thus, multiple AASs can exist for the same asset over the lifecycle. In order to utilize the information in the AAS within its value network, it needs to be accessible. Access is usually given via the Internet or via the cloud (repository-deployed AAS). In intelligent systems, the management shell can also be part of the asset itself (asset-deployed AAS).

Information can be exchanged in various ways. Either via files, so-called AASX files (AAS type 1), via a server-client interaction such as RestAPI (AAS type 2) or via peer-to-peer interaction (AAS type 3), in which the AASs communicate independently using the so-called I4.0 language and perform tasks cooperatively.

While type 1 and 2 take a passive role in the value network and are more likely to be used with repository-held AAS, type 3 describes an active participation in the value network and is more likely to be used with asset-held AAS running smart products.

Common standards connect!

No matter what type of Asset Administration Shell you choose: Important is that the recipient and the provider speak the same language. To achieve this, the exchange of concrete information must be standardized. Considering the amount of different industries, scenarios, assets, and functions, this is an immense number of submodels that need to be standardized. Organizations and associations such as the Industrial Digital Twin Association (IDTA), formed by research institutes, industrial companies, and software providers, are tackling this mammoth task. The rapidly growing number of members as well as the lively exchange at trade fairs and conferences among each other illustrate the potential for the industry. It is important not to leave SMEs behind, but to involve them in the standardization work in the best possible way.

Conclusion

The Asset Administration Shell is at the core of successful Industrie 4.0 scenarios. It enables manufacturer-independent interoperability and simplifies the integration of all types of assets into a collaborative value network. It increases efficiency within production processes by providing complete transparency of the real-time status of each asset. And it also offers a comprehensive security concept to protect the data. Within a very short time, the AAS has thus transformed from a theoretical construct to a real application in practice. Together with partners from research and industry, we are working within the ESCOM and Flex4Res research projects to make it usable on an industrial scale.

More cybersecurity using the password

Today is “change your password day” again. A well-intentioned
initiative for more IT security. Coming originally from the military context of the 1960s, the recommendation to change your password regularly can still be found in many corporate policies today. Modern guidelines such as the current BSI Basic Protection Compendium and the NIST Digital Identities Guidelines drop this requirement because there are more effective strategies to increase password security:

Password length over complexity

First of all, a strong password needs to be changed only if there is a suspicion that it has been revealed.

Today, attackers can try out billions of passwords within a very short time using automated systems. Especially if these systems are accessible via the network or have access to the password hashes and can therefore be effectively tried offline. The complexity of the password is therefore completely irrelevant if it is too short. Recommendations for length vary from 8 to at least 14 characters. Advances in attack tools such as Hashcat, and faster, specialized password-guessing hardware, are driving these requirements ever higher.

Compliance policies today require individualized login credentials. This eliminates the risk a password is known to many people and thus the need to change it regularly. One long password for exactly one person for exactly one service. Pretty secure.

Passwords are no repeat parts

To be honest, haven’t you ever used the same or a very similar password for multiple services? You should get rid of this habit quickly because a successful attack on one service automatically leads to a successful attack on others. The use of already privately used passwords in a corporate environment is particularly critical.

Modern password policies ensure that passwords appearing in lists of captured passwords are rejected. The website haveibeenpwand, for example, indicates whether a password has been captured. Modern systems offer interfaces to check passwords in this way. In CONTACT Elements you can easily activate them:

from cdb.sig import connect
from cdb.authentication import check_pwned_password
connect(‘password_acceptable_hook’)(check_pwned_password)

Password manager instead of one-size-fits-all

Password repeating is bad, and so are short passwords. Users face the challenge of remembering a large number of long passwords in their heads. Writing it down on a piece of paper and hiding it under the keyboard or sticking it on the bulletin board is not a solution, because a camera can capture it.

It is better to use a password manager. It can create and manage long passwords and makes them easier to enter via copy and paste. Unfortunately, some companies, driven by the concern that a Trojan will intercept the passwords on the clipboard, block the copy and paste method in their applications, preventing the use of a password manager. However, in the case of a Trojan attack, this measure is usually ineffective and companies should instruct users to use a password manager to increase their IT security.

Beware of highwaymen and tricksters

Even the strongest password does not protect against attacks if it is intercepted. It’s often surprisingly easy to do. Connections without a minimum level of security like Transport Layer Security (TLS) are an open book for any attacker. Older network protocols such as Kerberos also offer numerous gateways. Ransomware exploits these to spread across the corporate network. As soon as an administrator logs on to a compromised computer, the attacker has the credentials, and shortly thereafter gold and silver tickets are created and the Windows domain is firmly in the attacker’s hands.

Here, too, security stands or falls with the password, because it is used in the calculation of the authentication tickets and, due to the symmetrical encryption, enables the attacker to calculate the password back from the ticket.

Increase security through multiple factors

One recommendation to get around the weaknesses of passwords is to include other factors. This works very well from a security perspective. A second factor significantly increases security in almost every case. In most cases, it is of secondary importance whether these are one-time passwords such as TANs via SMS, time-based codes such as Definition Time-based One-time Password (TOTP), or even simple confirmation emails with links.

The downside of second factors is the additional effort and the impact on usability. Helpdesk processes become more complicated, users need to be trained, and login processes often happen more slowly.

Single sign-on – both a curse and a blessing

Users love single sign-on (SSO), where you only have to enter a password and a second factor once to use numerous services. This minimizes the effort enormously – but also for the attacker. Particularly if access depends on a weak password only. A central login system also solves many problems for compliance when users are blocked or reports are generated. The costs for user administration are also reduced.

Single sign-on turns the “one password per service” argument above on its head. Again, only one password stands between the attacker and your system. If the attacker knows the password, he has access. And then the single sign-on system opens all doors for the attacker.

Detect phishing

Even stronger mechanisms such as TOTP or hardware key generators do not protect if the password and access code are entered on a fake website. This practice is known as phishing. The solution, on the other hand, is channel or token binding and links (binds) the desired access to the channel through which the access is requested. This means that a token is only accepted for access to device A but not to device B of the attacker. This form of multi-factor authentication is very secure and easy to use with modern hardware or cell phones. For enterprise IT, integration with common platforms is relevant here. Windows Hello, Apple and Android support the FIDO2 / WebAuthn standard specified by the FIDO Alliance to detect phishing and make single sign-on secure.

Passwords are obsolete!?

Starting from the WebAuthn standard, there is a new initiative since 2022 with passkeys – driven by Apple, Microsoft and Google – to banish passwords from applications and single sign-on. You can change your password to a passkey today if your device supports it and use 2024’s “Change your Password Day” to delete your password and never have to use it again.

More Information on Cybersecurity

Learn everything you need to know about building a reliable IT security architecture for protection against cyberattacks in our free white paper “IT Security for Enterprises”.

Big, bigger, giant. The rise of giant AI models

The evolution of language models in the field of NLP (Natural Language Processing) has led to huge leaps in the accuracy of these models for specific tasks, especially since 2019, but also in the number and scope of the capabilities themselves. As an example, the GPT-2 and GPT-3 language models released with much media hype by OpenAI are now available for commercial use and have amazing capabilities both in type, scope, and accuracy, which I will discuss in another blog post. This was achieved in the case of GPT-3 by training using a model with 750 billion parameters on a data set of 570 GB. These are jaw-dropping values.

The larger the models, the higher the cost

However, the costs of training these models are also gigantic: Taking only the stated compute costs ¹ for a complete training run, the total amount for training GPT-3 is 10 million USD ^{2, 3}. In addition, there are further costs for pre-testing, storage, commodity costs for deployment, etc., which are likely to be in a similar amount. Over the past few years, the trend of building larger and larger models has been consistent, adding about an order of magnitude each year, i.e., the models are 10x larger than the year before.

Size of NLP models from 2018-2022. Parameter sizes are plotted logarithmically in units of billions. The red line represents the average growth: approx. 10-20 times larger models per year ².

The next model of OpenAI GPT-4 is supposed to have about 100 trillion parameters (100 x 1012 ). For comparison, the human brain has about 100 billion neurons (100 x 109) which is 1000 times less. The theoretical basis for this gigantism is based on studies which show a clear scaling behavior between model size and performance ⁴. According to these studies, the so-called loss – a measure for the error of the predictions of the models – decreases by 1, if the model becomes 10 times larger. However, this only works if the computing power and the amount of training are also scaled upwards.

In addition to the enormous amounts of energy required to calculate these models and the associated CO2 footprint, which is assuming worrying proportions, there are direct economic consequences: Apparently, not only smaller companies cannot afford the cost of training such models, but also larger corporations are likely to balk at costs of $10 million, or $100 million or more in the future. Not to mention the necessary infrastructure and staffing for such an endeavor.

Monopoly position of the big players

This has a direct impact on availability: while the smaller models are now open source until the end of 2019 and can be freely accessed via specialized providers, this no longer applies to the larger models from around the end of 2020 (the appearance of GPT-2). OpenAI, for example, offers a commercialized API and only grants access through an approval process. On the one hand, this is convenient for developing applications with these NLP models, as the work of hosting and administration is eliminated; on the other hand, the barrier to entry for competitors in this market is so steep that essentially the super-big AI companies participate there: Google with OpenAI, Microsoft with Deepmind, and Alibaba.

The consequences of these monopoly positions of the leading AI companies are, as with every monopoly, pricing models without alternatives and rigid business practices. However, the capabilities of the current large language models such as GPT-3 and Megatron Turing NLG are already so impressive that it is foreseeable that in 10 years every company will probably need access to the current models for the most varied applications. Another problem is that the origin of the models from the American or Chinese area brings a large bias into the models, which on the one hand is clearly expressed in the fact that English or Chinese is the language with which the models work best. On the other hand, the training datasets that come from these cultural areas bring with them the very cultural tendencies from these spaces, so it is to be expected that other regions of the world will be underrepresented and continue to fall behind..

What can be done?

In my opinion, it is important to keep a careful eye on the development and to be more active in shaping the development of AI in the European area. In any case, a greater effort is needed to avoid dependence on monopolized AI providers in the long term. It is perhaps conceivable to involve national computing centers or research alliances that, united with companies, train and commercialize their own models and form a counterweight to American or Chinese companies. The next 10 years will be decisive here.

¹ See here in section D as well as compute costs per GPU e.g. on Google Cloud approx. 1USD/hour for an NVIDIA V100
² Calculation approach: V100 = 7 TFLOPs = 7 10^12 / s, 3.14 10^23 flops => 3.14 10^23/7×10^12 / 3600 = 10^7 hours = 10 million USD, details of the calculation and research of the parameters here.
³ see also here for comparison graph with older data.
⁴ see arxiv and Deepmind