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The recent advances in digitalization, improved connectivity and cloud based services are making a huge revolution in manufacturing domain. In spite of the huge potential benefits in productivity, these trends also bring in some concerns related to safety and security to the traditionally closed industrial operation scenarios. This paper presents a high-level view of some of the research results and technological contributions of the InSecTT Project for meeting safety/security goals. These technology contributions are expected to support both the design and operational phases in the production life cycle. Specifically, our contributions spans (a) enforcing stricter but flexible access control, (b) evaluation of machine learning techniques for intrusion detection, (c) generation of realistic process control and network oriented datasets with injected anomalies and (d) performing safety and security analysis on automated guided vehicle platoons.

The rapid growth of the Internet has led to the evolution of sophisticated security threats that exploit vulnerabilities within networks. The defence mechanisms must quickly adapt to these new threats to ensure that networks stay secure. One possible mechanism is to use Machine Learning (ML) algorithms to detect malicious activities. The edge devices that control and manage the network, such as routers, already have access to the data that is flowing through the network and may utilize its own computational resources to host ML algorithms and use them to detect intrusions. This paper presents a system for network intrusion detection which is deployed to an edge device and evaluated for live binary classification of network traffic. Different ML algorithms (Decision Tree, Random Forest, and Artificial Neural Network) are evaluated on existing datasets (Westermo and CIC-IDS-2017). Flow-based data pre-processing is performed and different labeling strategies and flow durations are used and compared. The most effective version of each algorithm is implemented and deployed on the Westermo Lynx- 3510 routing-capable network switch and system performance is assessed across various scenarios with simulated network attacks. The experiments showed that Random Forest is the best option, closely followed by Decision Tree.

Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community.

A small deviation in manufacturing systems can cause huge economic losses, and all components and sensors in the system must be continuously monitored to provide an immediate response. The usual industrial practice is rather simplistic based on brute force checking of limited set of parameters often with pessimistic pre-defined bounds. The usage of appropriate machine learning techniques can be very valuable in this context to narrow down the set of parameters to monitor, define more refined bounds, and forecast impending issues. One of the factors hampering progress in this field is the lack of datasets that can realistically mimic the behaviours of manufacturing systems. In this paper, we propose a new dataset called MIDAS (Modular Ice cream factory Dataset on Anomalies in Sensors) to support machine learning research in analog sensor data. MIDAS is created using a modular manufacturing simulation environment that simulates the ice cream-making process. Using MIDAS, we evaluated four different supervised machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron) for two different problems: anomaly detection and anomaly classification. The results showed that multilayer perceptron is the most suitable algorithm with respect to model accuracy and execution time. We have made the data set and the code for the experiments publicly available, to enable interested researchers to enhance the state of the art by conducting further studies.

Modular automation provides a challenge for traditional physics simulators, especially if they are used as a simulator in the loop of a development or research project looking at behavior from a systems level. In this paper, we present extensions of a previously developed simulation environment that is tailored to provide these characteristics. The extensions include simulation engine level improvements, such as including better modeling of the material flow, and sensor anomaly injections to model sensor faults or tampering, as well as system-level enhancements and functionality including certificate handling and anomaly detection methods using machine learning. This simulation environment has proven useful for education as well as research and engineering work, and with the provided extensions several new directions of use can be envisioned. The system is demonstrated in the use case of a modular ice-cream factory, including all the new and enhanced functionalities.

Industrial control systems have been targeted by numerous cyber attacks over the past few decades which causes different problems related to data privacy, financial losses and operational failures. One potential approach to detect these attacks is by analyzing network data using machine learning and employing network anomaly detection techniques. However, the nature of these systems often involves their geographical dispersion across multiple zones, which poses a challenge in applying local machine learning methods for detecting anomalies. Additionally, there are instances where sharing complete operational data between different zones is restricted due to security concerns. As a result, a promising solution emerges by implementing a federated model for anomaly detection in these systems. In this study, we investigate the application of machine learning techniques for anomaly detection in network data, considering centralized, local, and federated approaches. We implemented the local and centralized methods using several simple machine-learning techniques and observed that Random Forest and Artificial Neural Networks exhibited superior performance compared to other methods. As a result, we extended our analysis to develop a federated version of Random Forest and Artificial Neural Network. Our findings reveal that the federated model surpasses the performance of the local models, and achieves comparable or even superior results compared to the centralized model, while it ensures data privacy and maintains the confidentiality of sensitive information.

There is a growing number of network attacks and the data on the network is more exposed than ever with the increased activity on the Internet. Applying Machine Learning (ML) techniques for cyber-security is a popular and effective approach to address this problem. However, the data which is used by ML algorithms have to be protected. In this paper, we present a framework that combines autoencoder, multi-objective optimization algorithms, and different ML algorithms to encode the network data, reduce its size, and detect and classify the network attacks. The novel element used in this framework, with respect to earlier research, is the application of multi-objective optimization algorithms, such as Multi-Objective Differential Evolution or Non-dominated Sorting Genetic Algorithm-II, to handle the different objectives in the fitness function of the autoencoder (autoencoder decoding error and accuracy of ML algorithm). We evaluated six different ML algorithms for attack detection and classification on network dataset UNSWNB15. The performance of the proposed framework is compared with single-objective Differential Evolution. The results showed that Multi-Objective Differential Evolution outperforms the counterparts for attack detection, while all the evaluated algorithms showed similar performance for attack classification.

There is a growing body of knowledge on network intrusion detection, and several open data sets with network traffic and cyber-security threats have been released in the past decades. However, many data sets have aged, were not collected in a contemporary industrial communication system, or do not easily support research focusing on distributed anomaly detection. This paper presents the Westermo network traffic data set, 1.8 million network packets recorded in over 90 minutes in a network built up of twelve hardware devices. In addition to the raw data in PCAP format, the data set also contains pre-processed data in the form of network flows in CSV files. This data set can support the research community for topics such as intrusion detection, anomaly detection, misconfiguration detection, distributed or federated artificial intelligence, and attack classification. In particular, we aim to use the data set to continue work on resource-constrained distributed artificial intelligence in edge devices. The data set contains six types of events: harmless SSH, bad SSH, misconfigured IP address, duplicated IP address, port scan, and man in the middle attack. 

Modern manufacturing systems collect a huge amount of data which gives an opportunity to apply various Machine Learning (ML) techniques. The focus of this paper is on the detection of anomalous behavior in industrial manufacturing systems by considering the temporal nature of the manufacturing process. Long Short-Term Memory (LSTM) networks are applied on a publicly available dataset called Modular Ice-cream factory Dataset on Anomalies in Sensors (MIDAS), which is created using a simulation of a modular manufacturing system for ice cream production. Two different problems are addressed: anomaly detection and anomaly classification. LSTM performance is analysed in terms of accuracy, execution time, and memory consumption and compared with non-time-series ML algorithms including Logistic Regression, Decision Tree, Random Forest, and Multi-Layer Perceptron. The experiments demonstrate the importance of considering the temporal nature of the manufacturing process in detecting anomalous behavior and the superiority in accuracy of LSTM over non-time-series ML algorithms. Additionally, runtime adaptation of the predictions produced by LSTM is proposed to enhance its applicability in a real system.