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As the Internet of Things (IoT) industry grows, the risk of network protocol security threats has also increased. One protocol that has come under scrutiny for its security vulnerabilities is MQTT (Message Queuing Telemetry Transport), which is widely used. To address this issue, an automated execution program called fuzz has been developed to verify the security of MQTT brokers. This program is provided with various random and unexpected input data and monitored for different responses, such as acknowledgments, crashes, failures, or memory leaks. To generate a significant number of realistic MQTT protocols, we have proposed a Generative Adversarial Networks (GAN)-based protocol fuzzer called SGANFuzz. Our experimental results show that SGANFuzz has successfully detected 6 vulnerabilities among 7 MQTT implementations, including 3 CVE bugs. Compared to the state-of-the-art fuzzing tools, SGANFuzz has proven to be the most efficient fuzzing tool in terms of vulnerability detection and has expanded the feedback coverage by receiving more unique network responses from MQTT brokers.

In this study, a new approach using a novel ensemble classification algorithm based on ECG morphological features is proposed for accurate detection of heart ventricular and atrial abnormalities. First, the raw ECG signal is preprocessed and the main character waves are detected. Second, a combination of ECG morphological features is proposed and extracted from the selected ECG segments. The proposed feature set contains morphological parameters, morphological visual pattern of QRS complex, and principle components of the third level and fourth level of a four-level Sym8 wavelet-decomposed ECG waveform. Next, a novel ensemble classification algorithm, with the key idea of integrating the knowledge acquired by several popular classification algorithms for this task into an ensemble system, is proposed so that the accuracy and robustness over various arrhythmia types could be improved. Finally, the features are applied to the proposed ensemble classification algorithm for abnormality detection. The proposed approach achieved an overall accuracy of 98.68% when it was validated on fifteen heartbeat types from the MIT-BIH arrhythmia database (MITDB), according to the Association for Advancement of Medical Instrumentation (AAMI) standard. The classification accuracies of the six main types – normal beat (N), right bundled branch blocks beat (R), left bundled branch blocks beat (L), atrial premature beat (A), premature ventricular contractions beat (V), and paced beat (P) are 98.75%, 99.77%, 99.70%, 94.81%, 98.57%, and 99.94%, respectively. The proposed approach proves a solid result in comparison with component classification algorithms as well as recent peer works.

Short QT syndrome (SQTS) is a genetic disease characterized by constantly short QT intervals and high risks of sudden death. SQTS6 is one of the identified SQTS genotype variants associated with the CACNA2D1 S755T mutation. However, the pathogenesis of SQTS induced arrhythmias remains unclear. To identify the underlying mechanisms of SQTS6 induced arrhythmias, a multi-scale human ventricle model comprising cell to organ levels was built. Cellular data was fitted at the cell level to reproduce the electrophysiological alterations reported in experiments. The influences were further explored at tissue and organ levels using idealized strand or tissue sheet models, and realistic ventricular slice and three-dimensional organ models. Simulation results suggested that, at the cellular level, the action potential duration (APD) and the effective refractory period (ERP) of myocytes were significantly abbreviated in the mutation condition. The unevenly changed APD and ERP led to transmural heterogeneity remodeling, and resulted in decreased temporal vulnerability. In addition, the S755T mutation shortened the critical length for initiating reentrant spiral waves, which enhanced the spatial vulnerability and provided substrates for reentry arrhythmias. Regarding the sustainability of arrhythmias, the evoked spiral waves or scroll waves persisted in the mutation condition but did not persist in the wild-type condition. The present study clearly suggested that the CACNA1DC S755T mutation can facilitate the initiation and maintenance of ventricular arrhythmias, and therefore contributes to higher risks of ventricular arrhythmias in SQTS6 patients.

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.

Molecular property prediction is important to drug design. With the development of artificial intelligence, deep learning methods are effective for extracting molecular features. In this paper, we propose a multichannel substructure-graph gated recurrent unit (GRU) architecture, which is a novel GRU-based neural network with attention mechanisms applied to molecular substructures to learn and predict properties. In the architecture, molecular features are extracted at the node level and molecule level for capturing fine-grained and coarse-grained information. In addition, three bidirectional GRUs are adopted to extract the features on three channels to generate the molecular representations. Different attention weights are assigned to the entities in the molecule to evaluate their contributions. Experiments are implemented to compare our model with benchmark models in molecular property prediction for both regression and classification tasks, and the results show that our model has strong robustness and generalizability.

Anomaly detection over HTTP traffic has attracted much attention in recent years, which plays a vital role in many domains. This article proposes an efficient machine learning approach to detect anomalous HTTP traffic that addresses the problems of existing methods, such as data redundancy and high training complexity. This algorithm draws on natural language processing (NLP) technology, uses the Word2vec algorithm to deal with the semantic gap, and implements Term Frequency-Inverse Document Frequency (TF-IDF) weighted mapping of HTTP traffic to construct a low-dimensional paragraph vector representation to reduce training complexity. Then we employs boosting algorithm Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to build an efficient and accurate anomaly detection model. The proposed method is tested on some artificial data sets, such as HTTP DATASET CSIC 2010, UNSW-NB15, and Malicious-URLs. Experimental results reveal that both the boosting algorithms have high detection accuracy, high true positive rate, and low false positive rate. Compared with other anomaly detection methods, the proposed algorithms require relatively short running time and low CPU memory consumption.

Person re-identification plays a critical part in many surveillance applications. Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features. To solve this issue, we propose a novel deep feature ranking scheme. Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs). Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs. Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift. Comprehensive experiments are carried out to demonstrate the significance of the proposed feature. Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods. Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness.

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data. This paper focuses on top-k frequent columns publication on sensitive data, with high result utility under differential privacy. Existing works directly select frequent columns from all columns (called one-phase scheme), which is far from ideal due to the large privacy consumption or misjudgments for columns with frequencies close to the frequency of the k th frequent column (called near-k-fluctuation-column). This paper presents a new solution Two Phase Selection (TPS) to carefully choose frequent columns in two phases. The main idea is to classify columns into two distinct categories based on whether it is one near-k-fluctuation-column or not. Frequent columns are chosen from the two categories using different techniques, which is totally different from existing solutions without classifying. Furthermore, by analyzing the distribution of near-k-fluctuation-columns, we introduce a block-centric column-choosing method privacy-free-mechanism (PFM). By partitioning columns into blocks, PFM makes the privacy consumption proportional to the number of blocks, instead of frequent columns. Extensive experiments on real datasets show that our proposals outperform the state-of-the-art techniques for top-k column publication.

International deep seabed resource surveys have accumulated a large amount of valuable marine geochemical data. These data, however, derive from a number of autonomous heterogeneous information sources that show the characteristics of big data, i.e., multidisciplinary, multidimensional, multisemantic, and with strong correlations. The traditional database federation method is thus not applicable. To achieve marine geochemical data interoperation, an approach is proposed in this paper. The approach first builds a marine sample ontology (MSO) based on marine geochemical metadata standards. With support of the MSO ontology, which serves as a specification for the semantics and model of multisource heterogeneous data, the data integration and unified query APIs are accomplished across different databases. The experiment is applied with three databases: the ICP-AES database model in the ODP project, the PetDB marine petrology database model compiled at the Lamont-Doherty Earth Observatory, and the GEOROC database model operated by the German Max Planck Institute. The experimental results show that this method not only improves the efficiency of the marine geochemical data integration but also realizes the reuse of data and models under the premise of ensuring the independence, security, and timeliness of the data sources.

In the above article [1], there are two errors in Figures 8 and 19. The error of Figure 8 was caused by incorrectly referencing the same figure as Figure 9.