Refine
Year of publication
Document Type
- conference proceeding (article) (16)
- Article (3)
- Part of Periodical (1)
Is part of the Bibliography
- no (20)
Keywords
- Internet of Things (7)
- IoTAG (3)
- Automotive (2)
- Penetration Testing (2)
- device identification (2)
- safety-critical infrastructure (2)
- security rating (2)
- Activating Learning (1)
- Artificial Intelligence (1)
- Automotive Security (1)
Institute
- Fakultät Informatik und Mathematik (19)
- Labor Informationssicherheit und Complience (ISC) (19)
- Fakultät Elektro- und Informationstechnik (7)
- Laboratory for Safe and Secure Systems (LAS3) (5)
- Hochschulleitung/Hochschulverwaltung (1)
- Institut für Angewandte Forschung und Wirtschaftskooperationen (IAFW) (1)
- Labor Intelligente Materialien und Strukturen (1)
Begutachtungsstatus
- peer-reviewed (10)
- begutachtet (1)
Forschungsbericht 2016
(2016)
Cloud Computing (CC), Internet of Thing (IoT) and Smart Grid (SG) are separate technologies. The digital transformation of the energy industry and the increasing digitalization in the private sector connect these technologies. At the moment, CC is used as a service provider for IoT. Currently in Germany, the SG is under construction and a cloud connection to the infrastructure has not been implemented yet. To build the SG cloud, the new laws for privacy must be implemented and therefore it’s important to know which data can be stored and distributed over a cloud. In order to be able to use future
innovative services, SG and IoT must be combined. For this, in
the next step we connect the SG infrastructure with the IoT.
A potential insecure device and network (IoT) should be able
to transfer data to and from a critical infrastructure (SG). In
detail, we focus on two different connections: the communication
between the smart meter switching box and the IoT device and the data transferred between the IoT and SG cloud. In our example, a connected charging station with cloud services is connected with a SG infrastructure. To create a really smart service, the charging station needs a connection to the SG to get the current amount of renewable energy in the grid. Private data, such as name, address and payment details, should not be transferred to the IoT cloud. With these two connections, new threads emerge. In this case, availability, confidentiality and integrity must be ensured. A risk analysis over all the cloud connections, including the vulnerability and the ability of an attacker and the resulting risk are developed in this paper.
Internet of Thing (IoT) and Smart Grid (SG) are separate technologies. The digital transformation of the energy industry and the increasing digitalization in the private sector connect these technologies. Currently in Germany, the SG is under construction. In order to use future innovative services, SG and IoT must be combined. For this, we connect the SG Infrastructure with the IoT. A potential insecure device and network (IoT) should be able to transfer data to and from a critical infrastructure (SG). Open research question in this context are the security requirements architecture SG and IoT and the mechanism for authentication and authorisation in future application (SG and IoT). Due to the increasing networking of the systems (SG and IoT) new threats and attack vectors arise. The attacks to the architecture influence the target of authenticity, security and privacy. For the security analysis we focus on two communication points: the communication between the smart meter gateway, and the IoT device. In our example, a connected charging station with cloud services is connected with a SG infrastructure. To create a really smart service, the charging station needs a connection to the SG to get the current amount of renewable energy in the grid. With this two connections, new threats emerge. A security analysis over all the connections, including the vulnerability and the ability of an attacker, is developed in this paper. The analysis shows us challenges of the communication between IoT and SG. For this, we defined technical and organizational requirements for authentication and authorization. Current authentication and authorization mechanisms are no longer sufficient for the defined requirements. We present the Role-based trust model for Safety-critical Systems for these defined requirements. The new trust model is integrated into a role-based access control model. It defines data classes, which separate the sensitive and non-sensitive information.
Controller Area Network (CAN) is still the most used network technology in today's connected cars. Now and in the near future, penetration tests in the area of automotive security will still require tools for CAN media access. More and more open source automotive penetration tools and frameworks are presented by researchers on various conferences, all with different properties in terms of usability, features and supported use-cases. Choosing a proper tool for security investigations in automotive network poses a challenge, since lots of different solutions are available. This paper compares currently available CAN media access solutions and gives advice on competitive hard-and software tools for automotive penetration testing.
To ensure the secure operation of IoT devices in the
future, they must be continuously monitored. This starts with
an inventory of the devices, checking for a current software
version and extends to the encryption algorithms and active
services used. Based on this information, a security analysis and
rating of the whole network is possible. To solve this challenge
in the growing network environments, we present a proposal for
a standard. With the IoT Device IdentificAtion and RecoGnition
(IoTAG), each IoT device reports its current status to a central
location as required and provides information on security. This
information includes a unique ID, the exact device name, the
current software version, active services, cryptographic methods
used, etc. The information is signed to make misuse more difficult
and to ensure that the device can always be uniquely identified.
In this paper, we introduce IoTAG in detail and describe the
necessary requirements.
The ongoing digitization and digitalization entails the increasing risk of privacy breaches through cyber attacks. Internet of Things (IoT) environments often contain devices monitoring sensitive data such as vital signs, movement or surveil-lance data. Unfortunately, many of these devices provide limited security features. The purpose of this paper is to investigate how artificial intelligence and static analysis can be implemented in practice-oriented intelligent Intrusion Detection Systems to monitor IoT networks. In addition, the question of how static and dynamic methods can be developed and combined to improve net-work attack detection is discussed. The implementation concept is based on a layer-based architecture with a modular deployment of classical security analysis and modern artificial intelligent methods. To extract important features from the IoT network data a time-based approach has been developed. Combined with
network metadata these features enhance the performance of the artificial intelligence driven anomaly detection and attack classification. The paper demonstrates that artificial intelligence
and static analysis methods can be combined in an intelligent Intrusion Detection System to improve the security of IoT environments.
Since IoT devices are potentially insecure and offer great attack potential, in our past research we presented IoTAG, a solution where devices communicate security-related information about themselves. However, since this information can also be exploited by attackers, we present in this paper a solution against the misuse of IoTAG. In doing so, we address the two biggest problems: authentication and pairing with a trusted device. This is solved by introducing a pairing process, which uses the simultaneous authentication of equals algorithm to securely exchange and verify each others signature, and by using the server and client authentication provided by HTTP over TLS. We provide the minimum requirements and evaluate the methods used. The emphasis is on known and already proven methods. Additionally, we analyze the potential consequences of an attacker tapping the IoTAG information. Finally, we conclude that the solution successfully prevents access to IoTAG by unauthorized clients on the same network.
The Internet of Things (IoT) is widely used as a
synonym for nearly every connected device. This makes it really
difficult to find the right kind of scientific publication for the
intended category of IoT. Conferences and other events for
IoT are confusing about the target group (consumer, enterprise,
industrial, etc.) and standardisation organisations suffer from
the same problem. To demonstrate these problems, this paper
shows the results of an analyses over IoT publications in different
research libraries. The number of results for IoT, consumer,
enterprise and industrial search queries were evaluated and a
manual study about 100 publications was done. According to
the research library or search engine, different results about
the distribution of consumer-, enterprise- and industrial- IoT
are visible. The comparison with the results of the manual
evaluation shows that some search queries do not show all desired
publications or that considerably more, unwanted results are
returned. Most researchers do not use the keywords right and
the exact category of IoT can only be accessed via the abstract.
This shows major problems with the use of the term IoT and its
minor limitations.
To support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100% accuracy.