Search Results (105)

IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While machine learning-based IDS have typically been deployed at the edge (gateways) or in the cloud, in the absence of gateways, the IDS must be embedded within the sensor nodes themselves. Available datasets mainly contain features extracted from network traffic at the edge (e.g., Raspberry Pi/computer) or cloud servers. We developed a unique dataset, named as Intrusion Detection in the Smart Homes (IDSH) dataset, which is based on features retrievable from microcontroller-based IoT devices. In this work, a Tree-based IDS is embedded into a smart thermostat for real-time intrusion detection. The results demonstrated that the IDS achieved an accuracy of 98.71% for binary classification with an inference time of 276 microseconds, and an accuracy of 97.51% for multi-classification with an inference time of 273 microseconds. Real-time testing showed that the smart thermostat is capable of detecting DoS and MITM attacks without relying on a gateway or cloud. Full article

Background: Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) and adjuvant chemotherapy is a therapeutic choice for women with advanced ovarian cancer. Whether NACT affects the tumor’s molecular profile has not been determined. Methods: This was a retrospective study of patients with advanced-stage epithelial ovarian cancer treated with NACT at oncology departments affiliated with the Hellenic Cooperative Oncology Group (HeCOG). Tumor molecular profiling was performed on formalin-fixed and paraffin-embedded (FFPE) tumor pre- and post-NACT tissues. Homologous recombination deficiency (HRD), tumor-infiltrating lymphocytes (TILs), tumor molecular alterations, and tumor mutational burden (TMB) via next-generation sequencing analysis were assessed. Results: Overall, tumors from 36 patients were assessed, and molecular profiling was evaluated in 20 paired tumor samples. HRD positivity exhibited no significant change between pre- and post-NACT tumors. The BRCA1/2 mutational status remained constant, irrespective of the treatment administration. Pre-NACT tumors tended to exhibit a lower percentage of intratumoral TILs compared to post-NACT tumors (p = 0.004). Differences in the mutation profile between pre- and post-treatment tissue were observed in 33.33% (6/18) of the cases. The mean tumor cell content (TCC) (p-value: 0.0840) and the mean genomic instability score (p-value: 0.0636) decreased slightly numerically after therapy. A moderate inverse relationship was observed between the pre-NACT TMB and the chemotherapy response score (p-value: 0.038), indicating this correlation is statistically significant. Conclusion: This study provides insights into the effect of NACT on the tumor molecular landscape. While BRCA1/2 and HRD status remained stable, an increase in TIL proportion and changes in the mutational profiles were observed post-treatment. Full article

The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives. Full article

Acinetobacter bereziniae has emerged as a significant human pathogen, acquiring multiple antibiotic resistance genes, including carbapenemases. This study focuses on characterizing the plasmids harboring the bla_NDM-1 and tet(Y) genes in two carbapenem-resistant A. bereziniae isolates (UCO-553 and UCO-554) obtained in Chile during the COVID-19 pandemic. Methods: Antibiotic susceptibility testing was conducted on UCO-553 and UCO-554. Both isolates underwent whole-genome sequencing to ascertain their sequence type (ST), core genome multilocus sequence-typing (cgMLST) profile, antibiotic resistance genes, plasmids, and mobile genetic elements. Conjugation experiments were performed for both isolates. Results: Both isolates exhibited broad resistance, including resistance to carbapenems, third-generation cephalosporins, fluoroquinolones, tetracycline, cotrimoxazole, and aminoglycosides. Both isolates belong to sequence type ST^PAS1761, with a difference of 17 out of 2984 alleles. Each isolate carried a 47,274 bp plasmid with bla_NDM-1 and aph(3′)-VI genes and two highly similar plasmids: a 35,184 bp plasmid with tet(Y), sul2, aph(6)-Id, and aph(3″)-Ib genes, and a 6078 bp plasmid containing the ant(2″)-Ia gene. Quinolone-resistance mutations were identified in the gyrA and parC genes of both isolates. Importantly, bla_NDM-1 was located within a Tn125 transposon, and tet(Y) was embedded in a Tn5393 transposon. Conjugation experiments successfully transferred bla_NDM-1 and tet(Y) into the A. baumannii ATCC 19606 strain, indicating the potential for horizontal gene transfer. Conclusions: This study highlights the critical role of plasmids in disseminating resistance genes in A. bereziniae and underscores the need for the continued genomic surveillance of this emerging pathogen. The findings emphasize the importance of monitoring A. bereziniae for its potential to cause difficult-to-treat infections and its capacity to spread resistance determinants against clinically significant antibiotics. Full article

In the last few years, the use of convolutional neural networks (CNNs) in intrusion detection domains has attracted more and more attention. However, their results in this domain have not lived up to expectations compared to the results obtained in other domains, such as image classification and video analysis. This is mainly due to the datasets used, which contain preprocessed features that are not compatible with convolutional neural networks, as they do not allow a full exploit of all the information embedded in the original network traffic. With the aim of overcoming these issues, we propose in this paper a new efficient convolutional neural network model for network intrusion detection based on raw traffic data (pcap files) rather than preprocessed data stored in CSV files. The novelty of this paper lies in the proposal of a new method for adapting the raw network traffic data to the most suitable format for CNN models, which allows us to fully exploit the strengths of CNNs in terms of pattern recognition and spatial analysis, leading to more accurate and effective results. Additionally, to further improve its detection performance, the structure and hyperparameters of our proposed CNN-based model are automatically adjusted using the self-adaptive differential evolution (SADE) metaheuristic, in which symmetry plays an essential role in balancing the different phases of the algorithm, so that each phase can contribute in an equal and efficient way to finding optimal solutions. This helps to make the overall performance more robust and efficient when solving optimization problems. The experimental results on three datasets, KDD-99, UNSW-NB15, and CIC-IDS2017, show a strong symmetry between the frequency values implemented in the images built for each network traffic and the different attack classes. This was confirmed by a good predictive accuracy that goes well beyond similar competing models in the literature. Full article

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem by generating meta-embeddings for new items as their initial ID embeddings. This approach has shown notable success in enhancing the accuracy of click-through rate predictions. However, prevalent meta-embedding models often focus solely on the attribute features of the item, neglecting crucial user information associated with it during the generation of initial ID embeddings for new items. This oversight hinders the exploitation of valuable user-related information to enhance the quality and accuracy of the initial ID embedding. To tackle this limitation, we introduce the residual graph meta-embedding model (RGMeta). RGMeta adopts a comprehensive approach by considering both the attribute features and target users of both old and new items. Through the integration of residual connections, the model effectively combines the representation information of the old neighbor items with the intrinsic features of the new item, resulting in an improved initial ID embedding generation. Experimental results demonstrate that RGMeta significantly enhances the performance of the cold-start phase, showcasing its effectiveness in overcoming challenges associated with sparse data and limited reference points. Our proposed model presents a promising step forward in leveraging both item attributes and user-related information to address cold-start problems in recommendation systems. Full article

Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for learning node embeddings, failing to leverage attribute information fully and to clarify the reasons behind a user’s interest in specific items. A heterogeneous graph neural network for recommendation named MIMA (multi-feature interaction meta-path aggregation) is proposed to address these issues. Firstly, heterogeneous graphs consisting of user nodes, item nodes, and their feature nodes are constructed, and the meta-path containing users, items, and their attribute information is used to capture the correlations among different types of nodes. Secondly, MIMA integrates attention-based feature interaction and meta-path information aggregation to uncover structural and semantic information. Then, the constructed meta-path information is subjected to neighborhood aggregation through graph convolution to acquire the correlations between different types of nodes and to further facilitate high-order feature fusion. Furthermore, user and item embedding vector representations are obtained through multiple iterations. Finally, the effectiveness and interpretability of the proposed approach are validated on three publicly available datasets in terms of NDCG, precision, and recall and are compared to all baselines. Full article

Research on recommendation methods using multimodal graph information presents a significant challenge within the realm of information services. Prior studies in this area have lacked precision in the purification and denoising of multimodal information and have insufficiently explored fusion methods. We introduce a multimodal graph recommendation approach leveraging cross-attention fusion. This model enhances and purifies multimodal information by embedding the IDs of items and their corresponding interactive users, thereby optimizing the utilization of such information. To facilitate better integration, we propose a cross-attention mechanism-based multimodal information fusion method, which effectively processes and merges related and differential information across modalities. Experimental results on three public datasets indicated that our model performed exceptionally well, demonstrating its efficacy in leveraging multimodal information. Full article

Three-dimensional multi-object tracking (MOT) using lidar point cloud data is crucial for applications in autonomous driving, smart cities, and robotic navigation. It involves identifying objects in point cloud sequence data and consistently assigning unique identities to them throughout the sequence. Occlusions can lead to missed detections, resulting in incorrect data associations and ID switches. To address these challenges, we propose a novel point cloud multi-object tracker called GBRTracker. Our method integrates an intra-frame graph structure into the backbone to extract and aggregate spatial neighborhood node features, significantly reducing detection misses. We construct an inter-frame bipartite graph for data association and design a sophisticated cost matrix based on the center, box size, velocity, and heading angle. Using a minimum-cost flow algorithm to achieve globally optimal matching, thereby reducing ID switches. For unmatched detections, we design a motion-based re-identification (ReID) feature embedding module, which uses velocity and the heading angle to calculate similarity and association probability, reconnecting them with their corresponding trajectory IDs or initializing new tracks. Our method maintains high accuracy and reliability, significantly reducing ID switches and trajectory fragmentation, even in challenging scenarios. We validate the effectiveness of GBRTracker through comparative and ablation experiments on the NuScenes and Waymo Open Datasets, demonstrating its superiority over state-of-the-art methods. Full article

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition. Full article

The visible-infrared person re-identification (VI-ReID) task aims to retrieve the same pedestrian between visible and infrared images. VI-ReID is a challenging task due to the huge modality discrepancy and complex intra-modality variations. Existing works mainly complete the modality alignment at one stage. However, aligning modalities at different stages has positive effects on the intra-class and inter-class distances of cross-modality features, which are often ignored. Moreover, discriminative features with identity information may be corrupted in the processing of modality alignment, further degrading the performance of person re-identification. In this paper, we propose a progressive discriminative feature learning (PDFL) network that adopts different alignment strategies at different stages to alleviate the discrepancy and learn discriminative features progressively. Specifically, we first design an adaptive cross fusion module (ACFM) to learn the identity-relevant features via modality alignment with channel-level attention. For well preserving identity information, we propose a dual-attention-guided instance normalization module (DINM), which can well guide instance normalization to align two modalities into a unified feature space through channel and spatial information embedding. Finally, we generate multiple part features of a person to mine subtle differences. Multi-loss optimization is imposed during the training process for more effective learning supervision. Extensive experiments on the public datasets of SYSU-MM01 and RegDB validate that our proposed method performs favorably against most state-of-the-art methods. Full article

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine Learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of Feature Selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS. The results, comparing the GA with other well-known techniques in Feature Selection and Dimensionality Reduction, show that the GA has the best F1-score of 76%, among all feature/dimension sizes. Although the processing time to find the optimal set of features surpasses other methods, we observed that the reduction in the number of features decreases the GA processing time without a significant impact on the F1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server, paving the way to a near real-time GA-based embedded IDS. Full article

In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today’s popular tracking methods are implemented using deep learning. But many monitoring devices do not have high computing power, so real-time tracking via neural networks is difficult. Furthermore, matching takes less time than detection and embedding, but it still takes some time, especially for many targets in a scene. Therefore, in order to solve these problems, we propose a new method by using grayscale maps to obtain spatial-temporal features based on traditional methods. Using this method allows us to directly find the position and region in previous frames of the target and significantly reduce the number of IDs that the target needs to match. At the same time, compared to some end-to-end paradigms, our method can quickly obtain spatial-temporal features using traditional methods, which reduces some calculations. Further, we joined embedding and matching to further reduce the time spent on tracking. Our method reduces the calculations in feature extraction and reduces unnecessary matching in the matching stage. Our method was evaluated on benchmark dataset MOT16, and it achieved great performance; the tracking accuracy metric MOTA reached 46.7%. The tracking FPS reached 17.6, and it ran only on a CPU without GPU acceleration. Full article

As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection systems. This approach enables the categorization of incoming network traffic into distinct intrusion types and illustrates the specific attack encountered within the Internet. Numerous studies have leveraged deep learning (DL) for Network-based Intrusion Detection Systems (NIDS), aiming to improve intrusion detection. Among these DL algorithms, Graph Neural Networks (GNN) stand out for their ability to efficiently process unstructured data, especially network traffic, making them particularly suitable for NIDS applications. Although NIDS usually monitors incoming and outgoing flows in a network, represented as edge features in graph format, traditional GNN studies only consider node features, overlooking edge features. This oversight can result in losing important flow data and diminish the system’s ability to detect attacks effectively. To address this limitation, our research makes several key contributions: (1) Emphasize the significance of edge features for enhancing GNN for multi-class intrusion detection, (2) Utilize port information, which is essential for identifying attacks but often overlooked during training, (3) Reorganize features embedded within the graph. By doing this, the graph can represent close to the actual network, which is the node showing endpoint identification information such as IP addresses and ports; the edge contains information related to flow such as Duration, Number of Packet/s, and Length…; (4) Compared to traditional methods, our experiments demonstrate significant performance improvements on both CIC-IDS-2017 (98.32%) and UNSW-NB15 (96.71%) datasets. Full article

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%. Full article