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AN OPEN-SOURCE INTRUSION DETECTION AND NOTIFICATION FRAMEWORK FOR OT ATTACKS TARGETING S7-SERIES PLCS
(2026-06-01)
Smerpitak, Krit
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Saleethong, Chayangkun
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Suphapod, Tanawat
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Wongkharn, Siripong
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Julsereewong, Amphawan
This article presents an open-source intrusion detection and notification (IDN) framework designed to detect attacks targeting Siemens S7-series programmable logic controllers (PLCs) in operational technology (OT) environments. The framework is validated using a simulated weight-based sorting system developed in Factory I/O, with TIA Portal control programs deployed on S7-300, S7-1200, and S7-1500 PLCs. Suricata serves as the core intrusion detection engine, while the Elasticsearch, Logstash, and Kibana (ELK) stack and LINE Notify are integrated to visualize alerts and provide realtime operator notifications. Simulated attacks are carried out using Snap7 to interact with each PLC during live process operations. The experimental results show that the framework reliably detects intrusions across all tested S7 PLC models and delivers timely alerts, demonstrating its effectiveness for real-time monitoring and incident response. In contrast to previous studies that focus primarily on protocol analysis or on individual PLC types, this work offers a practical and scalable intrusion detection solution validated on real hardware and designed to accommodate the coexistence of legacy and modern controllers within OT systems.
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Better Learning to Drive Autonomously with Proximal-Policy Reinforcement Learning and Visual Perception Representations
(2025-01-01)
Sittigorn, Jirasak
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Tungtrakool, Racha
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Petchhan, Jirayu
This study presents an approach based on a deep reinforcement learning framework for vision-based autonomous driving in the CARLA environment, focusing on urban driving tasks. Our study implements a sub-policy Proximal Policy Optimization (PPO) algorithm, demonstrating its effectiveness in navigating complex scenarios including lane following, straight driving, and left/right turns, and outperforming a single-policy approach for intersection maneuvers. To enhance learning efficiency, our representation learning leverages a deep mobile network for state representation, which significantly reduces image feature complexity. Furthermore, the integration of a single-shot multi-box detector enables the agent to perform realistic tasks such as responding to traffic lights and maintaining safe distances from leading vehicles, without compromising training speed. While the system demonstrates stable driving in various scenarios, current limitations include handling highly complex decisions and adapting to diverse speed limits due to environmental constraints. Future work will focus on expanding training environments and exploring more advanced network architectures to improve real-world applicability and learning efficiency.
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AVAILABILITY THROUGH FIELD DEVICE DIAGNOSIS IN FEEDFORWARD CONTROL: A CASE STUDY OF FOUNDATION FIELDBUS-BASED TEMPERATURE CONTROL
(2022-02-01)
Kummool, Sart
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Suwanmanee, Ittimon
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Weerathaweemas, Songchai
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Julsereewong, Amphawan
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Sittigorn, Jirasak
For endusers, requiring an action to enable a fieldbus-based process control that is not hazardous to continue its operation in the event of transmitter failures, it is essential to understand how diagnostic capability of field instruments is helpful in availability enhancement. In order to effectively implement a feedforward control strategy with increased availability, this article presents a control configuration method when utilizing a Foundation Fieldbus (FF)-based temperature feedforward control as an illustrative case study. Four function block assignment options for configuring the studied control strategy during engineering phase are described. The ‘Uncertain’ measurement status provided by temperature transmitters is treated as ‘Good’ measurement status in all control configuration options for availability goal by reducing process downtime. In addition, based on experimental results, a simplified Petri net model for logical behavior representation of the interested FF-based feedforward is also proposed.
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Real-time dashboard monitoring and alerting system for compressed air supply in rubber compound manufacturer
(2020-01-01)
Thepmanee, Teerawat
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Ngamsangiam, Pornprasert
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Sittigorn, Jirasak
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Pongswatd, Sawai
To minimize unexpected disruptions resulting from air supply problems in manufacturing production, a continuous monitoring of air pressures is crucial for predicting and preventing significant issues that may arise. This paper proposes a technique to build a real-time dashboard monitoring and alerting system for improving an availability of existing compressed air supply in a rubber compound manufacturer in Thailand. Flow and pressure transmitters as well as motorized ball valves are installed in the ma-jor supply lines of air piping system. In addition, a 3-phase power and energy meter is also installed to measure key electrical parameters of two main air compressors. The proposed technique is based on the use of cost-effective Node-RED web application not only to monitor volumetric flow rate, air pressure values, valve statuses, input/output module statuses, and energy-related parameters on the created dashboard in real time but also to alert plant personnel to pressure readings that fall outside an acceptable range via the LINE Notify application. There are three different notification patterns depending on how long is the pressure reading value out of the acceptable range. Moreover, the on/off status of all ball valves can also be remotely set on the dashboard subpages. Experimental results verify that the improved system can be utilized to assist plant personnel in trou-bleshooting and solving the problems with compressed air supply.

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