In-vehicle communication systems are usually managed by controller area networks (CAN). By broadcasting packets to their bus, the CAN facilitates the interaction between Electronic Control Units (ECU) that coordinate, monitor and control internal vehicle components. With no authentication mechanism for identifying the legitimacy and source of packets, CAN are vulnerable to cyber-attacks. An Intrusion Detection System (IDS) can detect attacks on CAN and machine learning can be used to create the models for the IDSs to detect non-linear attack patterns. However, car manufacturers and owners might want to keep the sensitive information required for training the models confidential. Therefore, we proposed a Blockchain-based Federated Forest Software-Defined Networking (SDN)-enabled IDS (BFF-IDS) to address the problem of data sharing the sensitive CAN data. To ensure scalability, we used InterPlanetary File System (IPFS) to host the models, and the blockchain is designed to store only a hash of the model and a pointer to its location. The SDN provides the dynamic routing of packets and model exchanges. We used Federated Learning (FL) to create a random forest model. Individuals provide partially trained models, allowing them to keep the underlying data confidential. Using Fourier transform, we decomposed the CAN IDs cycle from CAN bus traffic in the frequency domain for better generalization in multiclass detection of attacks. Multiple statistical and entropy features were extracted to handle the high complexity and non-linearity in CAN bus traffic. The proposed system allows manufacturers and car owners to contribute to the training of the models, as their sensitive data is protected. By storing hashes of the models on a blockchain, the risk of adversaries poisoning the models is reduced and a single point of failure is avoided. We evaluated the proposed system by conducting experiments on a testbed. We found that the proposed system has efficient use of memory and CPU resources and that the detection rate of closely related attacks was high. We recorded the highest model attack detection rate of about 0.981.

Business-to-government information exchange has over the past decades greatly benefited from data exchange standards and inter-organisational systems. The data era enables a new shift in the type of information sharing; from formal reporting to opening up full (and big) data sets. This enables new analytics and insights by government, more effective and efficient compliance assessment, and other uses. The emphasis here shifts from establishing formats to deciding what information can be shared, under what conditions, and how to create added value. There are numerous initiatives that explore how to put data to better use for businesses, for government and for their interactions. However, there is limited attention to exactly how these new forms of extensive data sharing affects the supervision relationships. In this paper, we exploratively look across three research projects to identify the implications of information sharing beyond the regulatory requirements (‘over-compliant’). We find that the lack of attention to those implications lead to solutions that are hard to scale up and present unexpected consequences down the line, which may negatively impact the future willingness to explore new potential added value of data sharing.

Supply chain management is hampered by a lack of information sharing among partners. Information is not shared as organizations in the supply chain do not have direct contact and/or do not want to share competitive and privacy sensitive information. In addition, companies are often part of multiple supply chains and trading partners vary over time. Blockchains are distributed ledgers in which all parties in a network can have access to data under certain conditions. Private blockchains can be used to support parties in making their demand data directly available to all other parties in their supply chain. These parties can use this data to improve their planning and reduce the bullwhip effect. However, the transparency that blockchain technology offers makes it more difficult to protect sensitive data. The dynamics between these properties are not well understood. In this paper, we design and evaluate a blockchain architecture to explore its feasibility for reducing information asymmetry, while at the same time protecting sensitive data. We found that blockchain technology can allow parties to balance their need for inventory management with their need for flexibility for changing partners. However, measures to protect sensitive data lead either to reduced information, or to reduced speed by which the information can be accessed.