IOT based satellite networks are one of the modern cyber attack topics. This technology has important application areas such as data collection, monitoring and control without the need for close access. Especially the increasing use of IOT devices and their recent integration with satellite networks have made these devices the target of attacks. The fact that IOT devices have more than one type and require low processing power makes them vulnerable to attacks. The use of IOT devices together with satellite networks increases the complexity of this situation and the size of cyber attacks. This situation has made it necessary to increase the studies on preventing and detecting cyber attacks on IOT based networks. For this purpose, in this article, we propose a new deep learning architecture (NidaDeepMix) that provides high accuracy in order to detect cyber attacks on IOT based satellite networks. The designed layer structure and parameters of the NidaDeepMix architecture are adjusted to effectively cope with complex and difficult situations. The NidaDeepMix architecture has been tested on two separate comprehensive datasets, CSE-CIC-IDS-2018 and BCCC-CIRA-CIC-DoHBrw-2020. As a result of the training, a serious accuracy rate of %99.99 was achieved for the CSE-CIC-IDS-2018 dataset and %99.98 for the BCCC-CIRA-CIC-DoHBrw-2020 dataset. Considering these high accuracy rates, it has been demonstrated that the proposed architecture is quite effective in classifying attacks. These rates obtained on different datasets reveal the generalization success of the model. At the same time the model has also addressed the issue of cyber attacks on IOT based satellite networks with an innovative approach. In this context, a new and effective architecture has been provided to the literature for detecting attacks on IOT based satellite networks. It is envisaged that the proposed method NidaDeepMix will be an important reference model in important issues such as cyber attacks and anomaly detection in the future.

Unmanned Aerial Vehicles (UAVs) are increasingly being integrated into a wide range of industries and ap- plications, including but not limited to surveillance, logistics, environmental monitoring, infrastructure inspection, and disaster management. The growing deployment of UAVs in both civilian and defence sectors highlights their versatil- ity and operational efficiency. However, one of the core enablers of UAV functionality is their dependence on wireless communication systems and network protocols to facilitate control, telemetry, and coordination with ground control sta- tions or other UAVs. This reliance on open and often unsecured communication channels exposes UAVs to a variety of significant security threats. This paper focuses on performing a comprehensive vulnerability analysis of the MAVLink protocol, which is currently the most extensively adopted communication protocol for UAVs. We analyse key security weaknesses inherent in the MAVLink protocol’s design, as well as additional vulnerabilities that may arise from specific implementations of the protocol. These vulnerabilities can enable a wide range of potential attacks, including spoofing, message injection, replay attacks, and unauthorised access. In addition, we assess the effectiveness of existing security mechanisms that have been proposed or implemented, such as encryption techniques, authentication frameworks, and intrusion detection systems. By systematically highlighting these vulnerabilities and their real-world implications, this study aims to offer meaningful insights into improving MAVLink protocol security and fostering more resilient and secure UAV communication systems.

The convergence of Non-Orthogonal Multiple Access (NOMA) and Cognitive Radio (CR) with Simultaneous Wireless Information and Power Transfer (SWIPT) offers a transformative approach to spectrum and energy efficiency. This paper analyzes CR–NOMA with SWIPT-enabled energy harvesting (EH), focusing on outage
probability (OP) and throughput. We derive explicit models under Rayleigh fading with interference-temperature constraints and validate by simulations. Results show that proper power allocation and time-switching ratios enhance user fairness while respecting primary-network interference. These insights guide robust and energyefficient designs for next-generation systems.

The digitalization of aviation has heightened exposure to cyber risk, yet Philippine aviation governance and practice remain fragmented. This study evaluates sectoral vulnerabilities and feasible mitigations using a multi-method design: (i) document analysis of CAAP circulars, DICT’s National Cybersecurity Plan 2022, and international guidance (ICAO, IATA, NIST, ISO/IEC 27001); (ii) case studies (Cathay Pacific breach; London Heathrow USB mishandling) chosen for analytic transferability to Philippine operations; and (iii) risk modeling via a likelihood–impact matrix with a transparent 1–5 rubric adapted from ICAO SMM, NIST SP 800-30, and DICT, scored independently by two researchers with consensus reconciliation. I integrate results through a SWOT–TOWS synthesis and propose an AI/ML feasibility roadmap tailored to on-prem/air-gapped constraints. Findings reveal high-priority risks, including unauthorized ATC access, reservation-system data breaches, and airport-network ransomware (ris score = 20), driven by monitoring gaps, legacy systems, and uneven policy enforcement. Moderately ranked threats (weak framework implementation; phishing) and under-analyzed insider risk reflect systemic and human-factor weaknesses, compounded by underreporting and limited inter-agency coordination. The study’s novel contribution is a localization map that operationalizes global frameworks for Philippine conditions: phased NIST CSF adoption, tiered ISO/IEC 27001 pathways, and ICAO-aligned CAAP–DICT coordination with centralized incident reporting; plus a staged, low-cost AI/ML roadmap with KPI tracking (MTTD/MTTR, precision/recall). Limitations include the absence of primary stakeholder data and local incident/cost series; we outline a quantitative extension using operator surveys and Expected Annual Loss modeling to strengthen future empirical grounding. The results inform regulators, airlines, and airports on risk-based prioritization and practical governance upgrades to enhance national aviation cyber resilience.

Industrial Control Systems (ICS) and Modbus-enabled networks are facing escalating threats from sophisticated cyber-attacks, while current Intrusion Detection Systems (IDS) struggle to identify intricate and adaptive attacks. This study envisions an ensemble learning-based IDS for Modbus-enabled industrial networks using a real-like Modbus 2023 dataset for industrial networks. The proposed IDS combines four base classifiers, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Adaptive Boosting (AdaBoost), using the stack ensemble framework, where Logistic Regression acts as the meta-classifier. Preprocessing involved PCAP capture and attack log synchronization, feature normalization, and one-hot encoding for balanced and accurate model training. Experimental evaluation demonstrated that the ensemble model has a 99.78% detection accuracy while outperforming the base individual models in terms of precision, recall, and F1-score. The results indicate the efficiency of ensemble learning for enhanced accuracy detection and false-positive reduction for Modbus networks. Future research will consider real-time testing, feature elimination, and explainable AI for higher operational deployment and scalability.