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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.
Automotive Original Equipment Manufacturer (OEM) and suppliers started shifting their focus towards the security of their connected electronic programmable products recently since cars used to be mainly mechanical products. However, this has changed due to the rising digitalization of vehicles. Security and functional safety have grown together and need to be addressed as a single issue, referred to as automotive security, in the following article. One way to accomplish security is automotive security education. The scientific contribution of this paper is to establish an Automotive Penetration Testing Education Platform (APTEP). It consists of three layers representing different attack points of a vehicle. The layers are the outer, inner, and core layers. Each of those contains multiple interfaces, such as Wireless Local Area Network (WLAN) or electric vehicle charging interfaces in the outer layer, message bus systems in the inner layer, and debug or diagnostic interfaces in the core layer. One implementation of APTEP is in a hardware case and as a virtual platform, referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theory, such as constructivism and problem-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH)Regensburg experienced the challenges as part of a special topic course and provided positive feedback.
Increasing cyber-attacks on Internet of Things (IoT) environments are a growing problem of digitized households worldwide. The purpose of this study is to investigate how an intelligent Intrusion Detection System (iIDS) can provide more security in IoT networks with a novel architecture, combining
multiple classical and machine learning approaches. By combining classical security analysis methods and modern concepts of artificial intelligence, we increase the quality of attack detection and can therefore conduct dedicated attack suppression. The architectural image of the iIDS consists of different layers, which in parts achieve self-sufficient results. The results of
the different modules are calculated by means of statement variables and evaluation techniques adapted for the individual module elements and subsequently combined by limit value considerations. The architecture image combines approaches for the analysis and processing of IoT network traffic and
evaluates it to an aggregated score. From this result it can be determined whether the analyzed data indicates device misuse or attempted break-ins into the network. This study answers the questions whether a connection between classical and modern concepts for monitoring and analyzing IoT network traffic can be implemented meaningfully within a reliable architecture of an
iIDS.
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.
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.
In this research, we investigate the possibility of applying ranking task activity in teaching and learning software engineering courses. We introduce three types of ranking tasks, conceptual-, contextual- and sequential ranking questions, which cover most core topics such as requirement analysis, architecture design and quality validation in the course. We have also done experiments on a group of students to see if ranking tasks could increase their conceptual knowledge in specific areas. Assessments were given in order to evaluate the effectiveness of this activity, showing an obvious increase in complex conceptual understanding.