Biblio

The upsurge of Industrial Internet of Things is forcing industrial information systems to enable less hierarchical information flow. The connections between humans, devices, and their digital twins are growing in numbers, creating a need for new kind of security and trust solutions. To address these needs, industries are applying distributed ledger technologies, aka blockchains. A significant number of use cases have been studied in the sectors of logistics, energy markets, smart grid security, and food safety, with frequently reported benefits in transparency, reduced costs, and disintermediation. However, distributed ledger technologies have challenges with transaction throughput, latency, and resource requirements, which render the technology unusable in many cases, particularly with constrained Internet of Things devices.To overcome these challenges within the Industrial Internet of Things, we suggest a set of interledger approaches that enable trusted information exchange across different ledgers and constrained devices. With these approaches, the technically most suitable ledger technology can be selected for each use case while simultaneously enjoying the benefits of the most widespread ledger implementations. We present state of the art for distributed ledger technologies to support the use of interledger approaches in industrial settings.

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.

Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.

Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.

Researchers and industry experts are looking at how to improve a shopper's experience and a store's revenue by leveraging and integrating technologies at the edges of the network, such as Internet-of-Things (IoT) devices, cloud-based systems, and mobile applications. The integration of IoT technology can now be used to improve purchasing incentives through the use of electronic coupons. Research has shown that targeted electronic coupons are the most effective and coupons presented to the shopper when they are near the products capture the most shoppers' dollars. Although it is easy to imagine coupons being broadcast to a shopper's mobile device over a low-power wireless channel, such a solution must be able to advertise many products, target many individual shoppers, and at the same time, provide shoppers with their desired level of privacy. To support this type of IoT-enabled shopping experience, we have designed Aggio, an electronic coupon distribution system that enables the distribution of localized, targeted coupons while supporting user privacy and security. Aggio uses cryptographic mechanisms to not only provide security but also to manage shopper groups e.g., bronze, silver, and gold reward programs) and minimize resource usage, including bandwidth and energy. The novel use of cryptographic management of coupons and groups allows Aggio to reduce bandwidth use, as well as reduce the computing and energy resources needed to process incoming coupons. Through the use of local coupon storage on the shopper's mobile device, the shopper does not need to query the cloud and so does not need to expose all of the details of their shopping decisions. Finally, the use of privacy preserving communication between the shopper's mobile device and the CouponHubs that are distributed throughout the retail environment allows the shopper to expose their location to the store without divulging their location to all other shoppers present in the store.

Botnets have long been used for malicious purposes with huge economic costs to the society. With the proliferation of cheap but non-secure Internet-of-Things (IoT) devices generating large amounts of data, the potential for damage from botnets has increased manifold. There are several approaches to detect bots or botnets, though many traditional techniques are becoming less effective as botnets with centralized command & control structure are being replaced by peer-to-peer (P2P) botnets which are harder to detect. Several algorithms have been proposed in literature that use graph analysis or machine learning techniques to detect the overlay structure of P2P networks in communication graphs. Many of these algorithms however, depend on the availability of a universal communication graph or a communication graph aggregated from several ISPs, which is not likely to be available in reality. In real world deployments, significant gaps in communication graphs are expected and any solution proposed should be able to work with partial information. In this paper, we analyze the effectiveness of some community detection algorithms in detecting P2P botnets, especially with partial information. We show that the approach can work with only about half of the nodes reporting their communication graphs, with only small increase in detection errors.