Crowdsourced location networks turn billions of consumer devices into a global sensor grid for locating lost items, but the same reach enables two systemic abuses: (i) location tracking via beacons that masquerade as “lost tags,” and (ii) data exfiltration by embedding short secrets in Bluetooth Low Energy (BLE) advertisements that are relayed forward without inspection. Using Apple’s Find My as a case study, we show that covert beacons reliably reach the cloud and then the attacker within minutes due to relay density. We also find that basic single-layer countermeasures such as packet dropping, TCP ACK/RST injection, fixed-delay insertion, or traffic flooding fail under realistic operational conditions. We contribute the first end-to-end experimental evaluation of deployable mitigations that require no vendor changes. Our defense-in-depth design combines: endpoint controls that correlate OS location-service access with immediate BLE advertising and enforce per-process advertising limits; a hybrid perimeter detector that correlates on-host BLE advertisement counts with outbound traffic to crowd-location backends; and physical controls for high-security areas, including exclusion zones of 35 m indoors and 200 m outdoors (line of sight), optionally supported by selective, low-duty RF jamming. For the longer term, we outline protocol changes that vendors can adopt, such as basic beacon admission control and authentication, shorter helper-retention timers, and helper-side quotas. While evaluated on Find My , these findings generalize to crowdsourced location systems built under similar design assumptions.

The highly dynamic characteristics of the maritime environment present significant challenges for vessel trajectory prediction. Traditional statistical and machine learning models often struggle to adapt to changing conditions and new data streams, leading to performance degradation. To address these well known issues, we propose the use of Continual Learning that enables the system to learn incrementally from sequential data streams. Our proposal avoids catastrophic forgetting of previously acquired knowledge through a replay-based approach. This strategy ensures that the prediction model can track and adapt to shifting environmental factors and variations in vessel behavior. We test the Continual Learning-based model using high-frequency trajectory data recorded by a cruise vessel Voyage Data Recorder. Experimental results indicate that our approach achieves a lower error compared to conventional static learning models. It mitigates catastrophic forgetting, ensuring the retention of critical information from past vessel movements, and demonstrates a strong capacity to adapt to data shifts inherent in real-world maritime operations. These findings highlight the potential of Continual Learning to enhance the reliability and robustness of vessel trajectory prediction systems in an ever-changing maritime landscape.

The increasing reliance on convolutional neural networks (CNNs) for automatic target recognition (ATR) in critical applications necessitates robust defenses against adversarial attacks, which can undermine their reliability. To address this challenge, this letter proposes a novel classification framework that enhances CNN robustness for ATR under adversarial perturbations. Although CNNs are renowned for their high recognition accuracy, their performance can be compromised by subtle adversarial perturbations designed to deceive the classifier. Our methodology is based on extracting specific features from Shapley additive explanations (SHAP) analysis within and outside the detected target area. These features are then used to train a multinomial logistic regression model using the training labels, and the trained regressor performs the classification. The key strength of our framework relies on robustness enhancement against adversarial attacks, particularly designed by the fast gradient sign method (FGSM). We validate our findings through extensive evaluations using two publicly available datasets: the multitype aircraft remote sensing images (MTARSI) dataset, which contains optical images of various aircraft types, and the moving and stationary target acquisition and recognition (MSTAR) dataset, which contains radar images.

Accurately determining the number of people affected by emergencies is essential for deploying effective response measures during disasters. Traditional solutions like cellular and Wi-Fi networks are often rendered ineffective during such emergencies due to widespread infrastructure damage or non-functional connectivity, prompting the exploration of more resilient methods. This paper proposes a novel solution utilizing Bluetooth Low Energy (BLE) technology and decentralized networks composed entirely of mobile and wearable devices to count individuals autonomously without reliance on external communication equipment or specialized personnel. This count leverages uncoordinated relayed communication among devices within these networks, enabling us to extend our counting capabilities well beyond the direct range of rescuers. A formally evaluated, experimentally validated, and privacy-preserving counting algorithm that demonstrates rapid convergence and high accuracy even in large-scale scenarios is employed.

This paper investigates the vulnerability of Synthetic Aperture Radar (SAR)-based ship recognition models to adversarial attacks. We employ the Fast Gradient Sign Method (FGSM) to generate adversarial examples, adding imperceptible perturbations to SAR ship images to mislead a pre-trained convolutional neural network (CNN). To analyze the impact of these attacks, we utilize the Local Interpretable Model-agnostic Explanations (LIME) algorithm, an Explainable Artificial Intelligence (XAI) method, to explain the contributing area in the input image to the CNN’s decision-making process under adversarial conditions. Finally, we propose an ensemble learning strategy combining multiple transfer learning-based architectures to enhance the robustness of ship recognition systems against adversarial examples and mitigate their transferability. Our real data experiment is conducted on OpenSARShip dataset, which consists of different ship images extracted from 41 images captured by Sentinel-1 SAR satellite.

The risk of data exfiltration remains a concern, even when the connectivity of the victim system is limited or the domain is physically isolated. This paper delves into the unique challenges associated with data exfiltration in surface and submarine naval scenarios in the absence of persistent data connections. It explores contexts where attacks through the supply chain can pose a serious risk even if the compromised hardware or software is not connected to any network or even (apparently) switched off. The attacks exploiting vulnerabilities in some components of the supply chain can serve as a conduit for data exfiltration. This study aims to enhance the overall security posture of maritime systems by identifying possible exposures and mitigating the risk of data exfiltration and covered channel attacks.

Navigational systems, the heart of maritime operations, face escalating cybersecurity risks due to their system of systems nature and reliance on diverse suppliers. Amid concerns about supply chain attacks and insider threats, the potential for malicious radar image injections, including Inverse Synthetic Aperture Radar (ISAR) images, is emerging. Such manipulations can critically undermine navigational integrity through the generation of decoy targets, the strategic relocation of existing ones, and the effective concealment of additional targets. We have identified where an Advanced Persistent Threat (APT) could be concealed within the processing chain of a modern radar system. This study demonstrates the potential of APTs to exploit Generative Adversarial Networks (GANs) for the creation of deceptive ISAR images, thereby spotlighting this previously unexplored threat vector. Our findings offer novel insights into bolstering maritime cybersecurity in an increasingly AI-dominated landscape.

An overview of the challenges and technologies used in both technical and judicial official investigations of the titled disaster as a use case. This article will show some of both the legal limits of current regulations and the technical limits of their applicability. Improvements will be proposed using Open Source and Format solutions that also implement techniques derived from Cybersecurity.