This study introduces an advanced federated learning framework tailored for smart home intrusion detection, incorporating knowledge distillation and transfer learning to tackle escalating threats to IoT devices. In light of the rapid expansion of IoT devices and their vulnerability to botnet incursions, our approach specifically addresses the challenges related to the privacy concerns of home device data, heterogeneity of devices, sparse intrusion data, and the dynamic nature of smart home settings. We adaptively select model architectures tailored to the computational capabilities of each device, ranging from simple Neural Networks (NNs) to more complex Convolutional Neural Networks (CNNs) and hybrid CNN-LSTM models, ensuring efficient local training without overburdening the devices. However, it can achieve good performance through collaborative learning, even for devices with lower capacity and sparse data. Our evaluation, conducted using the N-BaIoT dataset, demonstrates the effectiveness of our approach in detecting anomalies across a diverse set of IoT devices infected with real-world botnets such as Mirai and BASHLITE. The results highlight the potential of our framework to provide a robust, privacy-preserving, and adaptable solution for securing smart homes against emerging threats.
Road obstacle detection is one of the crucial tasks for the safe and efficient operation of autonomous vehicles. Existing detection methods often try to detect all objects in the image (i.e. a frame in a video stream). However, it is important to note that not all objects are equally dangerous and that the detection system should not react equally to them. To reduce computation costs (e.g. time and power consumption) in a mobile device, this study introduces a cost-sensitive obstacle detection method to leverage the performance of YOLIC (You Only Look at Interested Cells), a method proposed by us previously. The new method allows YOLIC to pay more attention to important areas for driving. In the experiments, we assign high weights to areas close to the vehicle, and the weights vary depending on the detection distance, direction, and relation to driving. Experimental results on two road obstacle datasets demonstrate that the proposed cost-sensitive detection method can effectively reduce the costs in the most dangerous areas of a given image compared with the baseline YOLIC model. Moreover, our fastest model can achieve real-time performance on a Raspberry Pi 4B, making it possible to deploy the proposed method in low-cost vehicles such as scooters and delivery robots.
The Internet of Medical Things (IoMT) has emerged substantial growth within the healthcare sector, spurred by advancements in smart devices that generate and process critical healthcare data. Sharing this data among trusted healthcare entities is vital for efficient patient care but raises substantial security and privacy concerns. Existing solutions have focused on authentication techniques employing Self-Sovereign Identity (SSI) and Zero-Knowledge Proofs (ZKPs), aiming to ensure secure, auditable, and anonymous authentication. These solutions often prioritize either lightweight authentication or robust security, yet a flexible approach that integrates both characteristics is essential for adaptive cross-domain authentication in the IoMT landscape. Furthermore, the importance of lightweight authentication combined with adaptive verification, pivotal for scaling access control in cross-domain environments, has been underexplored in existing frameworks. Addressing this gap, we proposed a scheme called LSAC which is a lightweight, scalable, and anonymous authentication for SSI-based cross-domain authentication for IoMT setting. Our proposed LSAC scheme not only facilitates rapid and robust identity verification across multiple IoMT domains within a consortium blockchain network but also incorporates the advanced ZK-STARK and Plonk ZKP protocols to enable efficient and secure verification processes. By leveraging Hyperledger for generating smart contracts and managing user interactions across domains, our system represents a significant step forward in cross-domain authentication. Our comprehensive functionality and performance analysis confirms the effectiveness of our solution in providing scalable and secure access to IoMT resources, supporting the ongoing evolution of healthcare services.
A suitable amount of training data is important in efficiently training high-quality deep learning models. In this paper, we performed an empirical study aimed at finding optimal amount of labeled waterfowl annotations or images for training detection and classification deep learning models with real-world aerial images. We proposed a new class-aware sampling method that can generate better training sets which led to significantly better waterfowl detectors. The results show that the trained Faster R-CNN and YOLOv5 models achieved over 86% accuracy with only 300 waterfowl training annotations using the new sampling method while the basic random sampling method needs 500 annotations to achieve same performance. In addition, the class-aware sampling method significantly accelerated training convergence of all detection models we applied. For waterfowl classification, we compared experimental results of two representative supervised-learning models and two semi-supervised learning methods, and combined with two loss functions and two sampling methods to generate training sets. The results show that semi-supervised learning methods outperformed supervised learning models, achieving F1 score over 0.8 using only 100 labeled training images per waterfowl class.
Nowadays, camera traps are widely employed in monitoring biodiversity and assessing the population density of animal species. A challenge in animal recognition in camera trap images is the detection of small animals in complex environments and the identification of heavily obscured animals. This paper presents two novel methods that leverage sequentially captured images to improve animal recognition accuracy: one utilizing optical flow information and the other a motion-based algorithm based on the principle of median filtering. In experiments, we used two new real-world sequence-based camera trap image datasets to evaluate these methods. Our findings indicate that optical flow information effectively reduces false positive cases, while the motion-based algorithm significantly improves the accuracy of detecting animal presence and counting by substantially reducing false negative cases. Specifically, using the MegaDetector with a confidence threshold of 0.5 as the baseline, the motion-based method reduced false negative cases by over 70% while only slightly increasing false positive cases, and improved animal counting accuracy by more than 25%.
