The Fourth Industrial Revolution (Industry 4.0) has transformed factories into smart Cyber-Physical Production Systems (CPPSs), where man, product, and machine are fully interconnected across the whole supply chain. Although this digitalization brings enormous advantages through customized, transparent, and agile manufacturing, it introduces a significant number of new attack vectors—e.g., through vulnerable Internet-of-Things (IoT) nodes—that can be leveraged by attackers to launch sophisticated Distributed Denial-of-Service (DDoS) attacks threatening the availability of the production line, business services, or even the human lives. In this article, we adopt a Machine Learning (ML) approach for network anomaly detection and construct different data-driven models to detect DDoS attacks on Industry 4.0 CPPSs. Existing techniques use data either artificially synthesized or collected from Information Technology (IT) networks or small-scale lab testbeds. To address this limitation, we use network traffic data captured from a real-world semiconductor production factory. We extract 45 bidirectional network flow features and construct several labeled datasets for training and testing ML models. We investigate 11 different supervised, unsupervised, and semi-supervised algorithms and assess their performance through extensive simulations. The results show that, in terms of the detection performance, supervised algorithms outperform both unsupervised and semi-supervised ones. In particular, the Decision Tree model attains an Accuracy of 0.999 while confining the False Positive Rate to 0.001.

Next-generation wireless communications systems need to have high throughputs to satisfy user demand, to be low-cost, and to have an efficient management as principal features. Using a high-performance, low-cost reflective semiconductor optical amplifier (RSOA) as a colorless remote modulator at the antenna unit, the wavelength-division multiplexing (WDM) technique can be used for supporting distributed antenna systems (DASs). Each antenna unit is connected to the central unit using optical fiber and all links are used to transmit radio signals. Due to a large optical bandwidth, RSOAs are potential candidates for cost effective WDM systems. In this paper, simulations are carried out to determine optimized RSOA devices for wireless technology. New RSOA structures are fabricated and evaluated. The optimized RSOA is electrically driven by a standard Wi-Fi input signal (IEEE 802.11 g) with a 64-quadrature amplitude modulation (QAM) format. A large modulation bandwidth and a high electrooptic gain are demonstrated, which are confirmed by good performance when using orthogonal frequency-division multiplexing techniques. Characteristics such as high linearity and large electrooptic modulation bandwidth of our RSOA are sufficient to ensure an error vector magnitude (EVM) lower than 5% with a dynamic range exceeding 35 dB in a back-to-back configuration (at 0 dBm). Uplink transmission over a 20 km of single-mode fiber is also demonstrated with EVM lower than 5% and a dynamic range exceeding 25 dB (at 5 dBm).

We improve and validate the multisection model for a bulk reflective semiconductor optical amplifier (RSOA) as a modulator in radio over fiber (RoF) systems. The RSOA model was developed using the symbolically defined devices (SDD) component under Agilent advanced design systems (ADS) software. An accurate parameter extraction method based on ADS optimization tools is presented. The intrinsic parameters of RSOA are obtained. Here the model is used to assess the static characteristics, harmonic and intermodulation distortions, and transmission performance of RSOA. Simulations have been validated with experimental results providing good agreement.

Reflective semiconductor optical amplifiers (RSOAs) can be used as external modulators in radio over fiber (RoF) links due to their amplification and modulation characteristics and colorless property. The nonlinear distortion of RSOA, however, limits its dynamic range. In this paper we demonstrate digital predistortion (DPD) linearization techniques to improve the linearity of RSOA external modulators. 64 quadrature amplitude modulation (QAM) signals are utilized to extract the model parameters. The dynamic AM/AM and AM/PM characteristics and power spectral densities of the modulated signals from the RSOA are demonstrated without and with DPD. Experimental results show clearly that the nonlinear distortion of RSOA external modulators in RoF links can be compensated using DPD linearization techniques.

We demonstrate that the use of an optimized bi-electrode reflective semiconductor optical amplifier as remote modulator reduces the chirp. Our simulation shows that the chirp reduction is due to the carrier density profile in multi-electrode reflective semiconductor optical amplifiers. As a result of the chirp reduction, penalties for transmission are reduced from 6 to 4.5 dB on standard fiber at 2.5 Gb/s over 50 km. Using tri-electrode devices, a penalty of 3 dB is obtained for the same transmission conditions.

An ultra-broadband optical receiver using discrete, commercial devices is described. The design comprises a GaInAs pin detector followed by a three-stage, high-impedance amplifier employing HEMTs interconnected by impedancematching networks. All devices are mounted on a standard soft substrate. The measured 3dB bandwidth is nearly 10 GHz, and receiver noise does not exceed 13 pA/√(Hz).