The use of IoT systems in industrial environments provides tremendous benefits and economic value leading to an exponential rise in their adoption. Their extended use, however, does not come without concerns related to potential security threats, thereby creating an obstacle in their further use in the field. To address these security concerns, we introduce a specialized Industrial Intrusion Detection System (I2DS). Our proposed system merges the capabilities of deep learning (DL) with FPGA-based hardware acceleration techniques, enabling it to detect subtle anomalies and potential cyber threats that may evade conventional rule-based intrusion detection systems (IDS) in an effective way. More specifically, by implementing the system on FPGA hardware, we achieve low-latency, high-throughput processing of network traffic, essential for real-time intrusion detection in industrial settings. Our architecture is scalable and can be adapted according to network bandwidth requirements, while remaining lightweight, making it an ideal solution for the stringent resource constraints often encountered in IoT environments. The proposed solution has been validated with the modbus TON-IoT dataset, achieving up to two orders of magnitude higher performance compared to a software equivalent implementation.