Automated IoT device identification based on full packet information using real-time network traffic

In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness

A Comprehensive Security Architecture for Information Management throughout the Lifecycle of IoT Products

The Internet of things (IoT) is expected to have an impact on business and the world at large in a way comparable to the Internet itself. An IoT product is a physical product with an associated virtual counterpart connected to the internet with computational as well as communication capabilities. The possibility to collect information from internet-connected products and sensors gives unprecedented possibilities to improve and optimize product use and maintenance. Virtual counterpart and digital twin (DT) concepts have been proposed as a solution for providing the necessary information management throughout the whole product lifecycle, which we here call product lifecycle information management (PLIM). Security in these systems is imperative due to the multiple ways in which opponents can attack the system during the whole lifecycle of an IoT product. To address this need, the current study proposes a security architecture for the IoT, taking into particular consideration the requirements of PLIM. The security architecture has been designed for the Open Messaging Interface (O-MI) and Open Data Format (O-DF) standards for the IoT and product lifecycle management (PLM) but it is also applicable to other IoT and PLIM architectures. The proposed security architecture is capable of hindering unauthorized access to information and restricts access levels based on user roles and permissions. Based on our findings, the proposed security architecture is the first security model for PLIM to integrate and coordinate the IoT ecosystem, by dividing the security approaches into two domains: user client and product domain. The security architecture has been deployed in smart city use cases in three different European cities, Helsinki, Lyon, and Brussels, to validate the security metrics in the proposed approach. Our analysis shows that the proposed security architecture can easily integrate the security requirements of both clients and products providing solutions for them as demonstrated in the implemented use cases

Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts

A key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel–Cobalt–Manganese (NMC) cells

Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain

In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts

Lifecycle Management in the Smart City Context: Smart Parking Use-Case

Lifecycle management enables enterprises to manage their products, services and product-service bundles. IoT and CPS have made products and services smarter by closing the loop of data across different phases of lifecycle. Similarly, CPS and IoT empower cities with real-time data streams from heterogeneous objects. Yet, cities are smarter and more powerful when relevant data can be exchanged between different systems across different domains. From engineering perspective, smart city can be seen as a System of Systems composed of interrelated/ interdependent smart systems and objects. To better integrate people, processes, and systems in the smart city ecosystem, this paper discusses the use of Lifecycle Management in the smart city context. Considering the differences between ordinary and smart service systems, this paper seeks better understanding of lifecycle aspects in the smart city context. For better understanding, some of the discussed lifecycle aspects are demonstrated in a smart parking use-case

DESIGN PATTERNS FOR MANAGING PRODUCT LIFECYCLE INFORMATION

As the number of companies participating in the manufacturing of products increases, the challenges on managing the product life cycle also increase. A major challenge is how to manage product-related information when it is spread on computer systems of multiple companies. It is possible to perform this task in many ways ranging from centralised "portal " systems to distributed peer-to-peer (P2P) architectures. This paper attempts to point out the advantages and drawbacks of these different approaches for managing of product information through the products' whole lifecycle. Design Patterns from object-oriented programming are presented as a potential model for organizing product information and operations performed on it

Globally unique product identifiers - Requirements and solutions to product lifecycle management

Managing product information for product items during their whole lifetime is challenging, especially during their usage and end-of-life phases. A major challenge is how to keep a link between the product item and its associated information, which may be stored in backend systems of different organisations. In this paper, we analyse and compare three approaches for addressing this task, i.e. the EPC Network, DIALOG and WWAI. Copyright © 2006 IFAC