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SATCOM is crucial for tactical networks, particularly submarines with sporadic communi- cation requirements. Emerging SATCOM technologies, such as low-earth-orbit (LEO) satellite networks, provide lower latency, greater data reliability, and higher throughput than long-distance geostationary (GEO) satellites. Software-defined networking (SDN) has been introduced to SATCOM networks due to its ability to enhance management while strengthening network control and security. In our previous work, we proposed a SD-LEO constellation for naval submarine communication networks, as well as an extreme gradient boosting (XGBoost) machine-learning (ML) approach for classifying denial-of-service attacks against the constellation. Nevertheless, zero-day attacks have the potential to cause major damage to the SATCOM network, particularly the controller architecture, due to the scarcity of data for training and testing ML models due to their novelty. This study tackles this challenge by employing a predictive queuing analysis of the SD-SATCOM controller design to rapidly generate ML training data for zero- day attack detection. In addition, we redesign our singular controller architecture to a decentralized controller architecture to eliminate singular points of failure. To our knowledge, no prior research has investigated using queuing analysis to predict SD-SATCOM controller architecture network performance for ML training to prevent zero-day attacks. Our queuing analysis accelerates the training of ML models and enhances data adaptability, enabling network operators to defend against zero-day attacks without precollected data. We utilized the CatBoost algorithm to train a multi-output regression model to predict network performance statistics. Our method successfully identified and classified normal, non-attack samples and zero-day cyberattacks with over 94% accuracy, precision, recall, and f1-scores. 

First responders and other tactical teams rely on mo- bile tactical networks to coordinate and accomplish emergent time- critical tasks. The information exchanged through these networks is vulnerable to various strategic cyber network attacks. Detecting and mitigating them is a challenging problem due to the volatile and mobile nature of an ad hoc environment. This paper proposes MalCAD, a graph machine learning-based framework for detecting cyber attacks in mobile tactical software-defined networks. Mal- CAD operates based on observing connectivity features among various nodes obtained using graph theory, instead of collecting information at each node. The MalCAD framework is based on the XGBOOST classification algorithm and is evaluated for lost versus wasted connectivity and random versus targeted cyber attacks. Results show that, while the initial cyber attacks create a loss of 30%–60% throughput, MalCAD results in a gain of average throughput by 25%–50%, demonstrating successful attack mitigation. 

Satellite communication (SATCOM) is a critical infrastructure for tactical networks--especially for the intermittent communication of submarines. To ensure data reliability, recent SATCOM research has begun to embrace several advances, such as low earth orbit (LEO) satellite networks to reduce latency and increase throughput compared to long-distance geostationary (GEO) satellites, and software-defined networking (SDN) to increase network control and security. This paper proposes an SD-LEO constellation for submarines in communication networks. An SD-LEO architecture is proposed, to Denial-of-Service (DoS) attack detection and classification using the extreme gradient boosting (XGBoost) algorithm. Numerical results demonstrate greater than ninety-eight percent in accuracy, precision, recall, and F1-scores. 

IoT systems require a wireless infrastructure that supports 5G devices, including handovers between heterogeneous and/or small cell radio access networks. These networks are subject to increased radio link failures and loss of IoT network function. 3GPP new radio (NR) applications include multihoming, i.e., simultaneously connecting devices, and handover, i.e., changing the point of access to the network. This work leverages the open radio access network (O-RAN) alliance, which specifies a new open architecture with intelligent controllers, to improve handover management. A new feedback-based time-to-trigger (TTT) handover mechanism is introduced. Improved throughput and reduced radio link failures over other techniques were achieved. 

For the next generation of wireless technologies, Orthogonal Frequency Division Multiplexing (OFDM) remains a key signaling technique. Peak-to-Average Power Ratio (PAPR) reduction must be included with OFDM to reduce the detrimental high PAPR exhibited by OFDM. The cost of PAPR reduction techniques stems from adding multiple IFFT iterations, which are computationally expensive and increase latency. We propose a novel PAPR Estimation Technique called PESTNet which reduces the necessary IFFT operations for PAPR reduction techniques by using deep learning to estimate the PAPR before the IFFT is applied. This paper gives a brief background on PAPR in OFDM systems and describes the PESTNet algorithm and the training methodologies. A case study of the estimation model is provided where results demonstrate PESTNet is able to give an accurate estimate of PAPR and can compute large batches of resource grids up to 10 times faster than IFFT based techniques.