Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.

The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm.

Many studies have proposed a peer-to-peer energy market where the prosumers’ actions, including energy consumption, charge and discharge schedule of energy storage systems, and transactions in local energy markets, are controlled by a central operator. In this paper, prosumers’ actions are not controlled by an operator, and the prosumers freely participate in the local energy market to trade energy with other prosumers. We designed and modeled a local energy market with a management algorithm that uses community energy storage for prosumers who competitively participate in trade in the real-time energy market. We propose an energy-trade management algorithm that manages the trades of prosumers in two phases based on bids and offers submitted by prosumers. The first phase is to manage the trade of prosumers who have submitted fair prices to trade with other prosumers in the real-time energy market. The second phase is managing the trade of prosumers that could not trade in the first phase. Community energy storage is employed in the second phase and controlled by a reinforcement learning-based trading algorithm to decide whether to buy, sell, or do nothing with the prosumers. The action of buying and selling means charging and discharging the community energy storage, respectively. Numerical results show that the proposed trading algorithm gains a near-maximum profit. Besides, we verified that community energy storage yields more profit than the battery wear-out cost.

The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being collected every month and petabytes of data already being made public. Heterogeneous marine data collected through various devices can be used in various fields such as environmental protection, defect prediction, transportation route optimization, and energy efficiency. However, it is difficult to manage vessel related data due to high heterogeneity of such marine big data. Additionally, due to the high heterogeneity of these data sources and some of the challenges associated with big data, such applications are still underdeveloped and fragmented. In this paper, we propose the Vessel Data Lakehouse architecture consisting of the Vessel Data Lake layer that can handle marine big data, the Vessel Data Warehouse layer that supports marine big data processing and AI, and the Vessel Application Services layer that supports marine application services. Our proposed a Vessel Data Lakehouse that can efficiently manage heterogeneous vessel related data. It can be integrated and managed at low cost by structuring various types of heterogeneous data using an open source-based big data framework. In addition, various types of vessel big data stored in the Data Lakehouse can be directly utilized in various types of vessel analysis services. In this paper, we present an actual use case of a vessel analysis service in a Vessel Data Lakehouse by using AIS data in Busan area.

As the modern computing market experiences a surge in demand for efficient data-management solutions, challenges posed by centralized storage systems become more pronounced, especially with the proliferation of Internet of Things devices. Centralized storage, although cost-effective, faces issues of scalability, performance bottlenecks, and security vulnerabilities. With decentralized storage, data are distributed across nodes, offering redundancy, data availability, and enhanced security. Unfortunately, decentralized storage introduces its own challenges, such as complex data retrieval processes, potential inconsistencies in data versions, and difficulties in ensuring data privacy and integrity in a distributed setup. Effectively managing these challenges calls for innovative techniques. In response, this paper introduces a decentralized storage system that melds cloud-native concepts with blockchain technology. The proposed design delivers enhanced scalability, data security, and privacy. When operating on a containerized edge infrastructure, this storage system provides higher data-transfer speeds than the interplanetary file system. This research thus blends the advantages of cloud-native frameworks with the security mechanisms of blockchain, crafting a storage system that addresses the present-day challenges of data management in decentralized settings.

Industrial control systems (ICS) are vital for ensuring the reliability and operational efficiency of critical infrastructure across various industries. However, due to their integration into modernized network environments, they are inadvertently exposed to a variety of cybersecurity threats that can compromise the reliability of critical infrastructure. This study aims to enhance ICS security by introducing a Zero Inflated Poisson (ZIP) based GRU Learning model to detect anomalies of ICS traffic in conjunction with the MITRE ATT&CK framework. The model’s effectiveness was validated through experiments simulating two major cyberattack scenarios: the ‘Stuxnet’ attack and the ‘Industroyer’ attack, achieving over 95% success in attack detection. By mapping the anomalies to the MITRE ATT&CK framework, we were able to lay the groundwork for an efficient response strategy to the attacks. These findings are expected to make a meaningful contribution to assessing and strengthening the security posture of ICS.