Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.

Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.

The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands.

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment.

Cloud computing has revolutionized the digital era by providing a more efficient, scalable, and cost-effective infrastructure. Secure systems that encrypt and protect data before it is transmitted over a network and stored in the cloud benefit the entire transmission process. Transmission data can be encrypted and protected with a secure dynamic substitution box (S-box). In this paper, we propose the QuantumGS-box, which is a dynamic S-box for high-speed cloud-based storage encryption generated by bit shuffling with a genetic algorithm and a quantum random number generator (QRNG). The proposed work generates the S-box optimized values in a dynamic way, and an experimental evaluation of the proposed S-box method has been conducted using several cryptographic criteria, including bit independence criteria, speed, non-linearity, differential and linear approximation probabilities, strict avalanche criteria and balanced output. The results demonstrate that the QuantumGS-box can enhance robustness, is resilient to differential and provide improved linear cryptoanalysis compared to other research works while assuring non-linearity. The characteristics of the proposed S-box are compared with other state of the art S-boxes to validate its performance. These characteristics indicate that the QuantumGS-box is a promising candidate for cloud-based storage encryption applications.

LoRaWAN has become a widely adopted communication technology for Internet of Things applications because of its long-range communication capability and low power consumption. However, resource-constrained LoRaWAN networks remain vulnerable to physical-layer attacks such as jamming and battery-depletion attacks. This paper investigates architectural compression and Knowledge Distillation (KD) for lightweight multi-attribute Physical Layer Authentication (PLA) in LoRaWAN networks. A higher-capacity Teacher model is used to train progressively smaller Student models, and three Student-training strategies—Direct training, Hard-KD, and Soft-KD—are systematically compared to determine whether Teacher supervision provides an advantage beyond architectural compression alone. The framework uses the Received Signal Strength Indicator (RSSI), altitude, and the battery level as input attributes and is evaluated offline using a device-aware semi-synthetic dataset derived from the publicly available Brno LoRaWAN dataset. The resulting dataset contains Normal Communication, Jamming Attack, and Battery Depletion Attack traffic. The experimental results show that Student (8) preserves a classification performance close to that of the Teacher across all three training strategies, achieving Macro F1-scores of 90.619 ± 0.056% for Direct training, 90.591 ± 0.069% for Hard-KD, and 90.626 ± 0.083% for Soft-KD, compared with 91.007% for the Teacher. Student (8) reduces the number of trainable parameters from 2435 to 59 (97.6%) and the serialised model size from 46.65 KB to 2.80 KB (94.0%), while exhibiting a substantially higher measured batch-processing throughput under the evaluated CPU-based implementation. A feature-ablation analysis further indicates that the RSSI and battery level provide the dominant discriminatory information, while the altitude provides a smaller but measurable contextual contribution. The comparable performance of Direct, Hard-KD, and Soft-KD demonstrates that KD does not consistently outperform direct training for the evaluated Student architectures; rather, architectural compression itself accounts for much of the retained performance. These findings demonstrate that compact Student architectures can substantially reduce the model complexity while preserving a strong classification performance within the evaluated multi-attribute PLA framework, supporting the further investigation of lightweight network-side security mechanisms for LoRaWAN deployments.