With the rapid ubiquity of Internet of Things (IoT) technology, a growing number of devices are being connected to the Internet, thereby increasing the potential for cyberattacks. For instance, due to firmware compatibility issues and release delays, N-day vulnerabilities pose significant threats to IoT devices that run outdated firmware versions. Consequently, accurately and efficiently identifying firmware versions of devices is crucial for detecting device vulnerabilities and enhancing the security of IoT ecosystems. In this work, we present DeepFW, which utilizes a Fusion Feature Attention Network (FFAN) to extract subtle differences in embedded web interfaces within the firmware, facilitating the identification of firmware versions in online IoT devices. To address the challenge of high similarity between versions caused by firmware homogeneity in the supply chain, we propose a novel metric loss, namely the Hard Mining Cosine Triplet-Center Loss (HCTCL), to improve intraclass compactness and inter-class separability. To validate the effectiveness of our method, we collected 4,442 firmware images and obtained $\mathbf{1 3 0, 4 4 5}$ valid embedded web pages. Experimental results show that DeepFW outperforms the state-of-the-art approaches by over $25 \%$ on average in both precision and recall. Furthermore, DeepFW revealed that only $2.28 \%$ of devices in our dataset were running the latest firmware version. Our evaluation also indicates that $\mathbf{6, 6 8 4}$ devices (approximately $\mathbf{6 1. 2 6 \%}$) with outdated firmware versions remain vulnerable to known exploits.

Collocation of measurements from active and passive satellite sensors refers to the combination of data from two sensors that observe the same geographic area at nearly the same time but with differing spatial resolutions and viewing angles. This collocated data, often known as on-track data, comes with precise product labels from the active sensor but comprises only the pixels located directly on the path of an active satellite’s orbit. As a result, its spatial coverage is quite limited, especially when compared to the vast quantities of off-track data. Handling the abundant and information-dense off-track data is crucial for training machine learning models that can effectively integrate the unique features of this data along with on-track data. However, the sheer volume of off-track data presents significant challenges for these models. To address the challenges of large amounts of unlabeled off-track data in remote sensing applications, we introduce a self-supervised representation learning model with VAE and domain adaptation methods to learn a domain invariant classifier for the on-track and off-track data. The model’s performance is enhanced by pre-training off-track data with VAE generative model using off-track data, to learn a good representation that can be transferred to the down-streaming domain adaptation and classification tasks. The classifier is built on these representations to classify different cloud types in passive sensing data, with the goal of achieving higher accuracy in cloud property retrieval. Extensive quantitative and qualitative evaluation demonstrate our method achieves higher accuracy in cloud property retrieval for off-track remote sensing data.

Wireless body area networks (WBANs) have been deployed in numerous applications, where the most common communication technology is Bluetooth. Bluetooth uses the numeric comparison protocol (NCP) to negotiate session keys based on the elliptic curve cryptography (ECC) and Out-of-Band (OoB) channels. However, the scalar multiplication of ECC is a heavy computing operation for devices in WBANs. To address this issue, we propose the lightweight and secure NCP (LSNCP) which requires less scalar multiplication than the NCP in Bluetooth. New logic expressions and rules are proposed to verify the security of LSNCP in GNY logic. The proof shows that LSNCP is secure. We conduct a provable security analysis by integrating the commitment scheme and short hash function. The result shows that LSNCP is secure in the modified Bellare–Rogaway model. Finally, we conduct theoretical analysis and experiments to evaluate the performance of LSNCP. The results confirm that LSNCP has less computation cost than NCP and other benchmark protocols. LSNCP has many potential application scenarios, such as healthcare, Metaverse, and blockchain.

Spatial resolution is critical for observing and monitoring environmental phenomena. Acquiring high-resolution bathymetry data directly from satellites is not always feasible due to limitations on equipment, so spatial data scientists and researchers turn to single image super-resolution (SISR) methods that utilize deep learning techniques as an alternative method to increase pixel density. While super resolution residual networks (e.g., SR-ResNet) are promising for this purpose, several challenges still need to be addressed: (1) Earth data such as bathymetry is expensive to obtain and relatively limited in its data record amount; (2) certain domain knowledge needs to be complied with during model training; (3) certain areas of interest require more accurate measurements than other areas. To address these challenges, following the transfer learning principle, we study how to leverage an existing pre-trained super-resolution deep learning model, namely SR-ResNet, for high-resolution bathymetry data generation. We further enhance the SR-ResNet model to add corresponding loss functions based on domain knowledge. To let the model perform better for certain spatial areas, we add additional loss functions to increase the penalty of the areas of interest. Our experiments show our approaches achieve higher accuracy than most baseline models when evaluating using metrics including MSE, PSNR, and SSIM.

Domain adaptation techniques using deep neural networks have been mainly used to solve the distribution shift problem in homogeneous domains where data usually share similar feature spaces and have the same dimensionalities. Nevertheless, real world applications often deal with heterogeneous domains that come from completely different feature spaces with different dimensionalities. In our remote sensing application, two remote sensing datasets collected by an active sensor and a passive one are heterogeneous. In particular, CALIOP actively measures each atmospheric column. In this study, 25 measured variables/features that are sensitive to cloud phase are used and they are fully labeled. VIIRS is an imaging radiometer, which collects radiometric measurements of the surface and atmosphere in the visible and infrared bands. Recent studies have shown that passive sensors may have difficulties in prediction cloud/aerosol types in complicated atmospheres (e.g., overlapping cloud and aerosol layers, cloud over snow/ice surface, etc.). To overcome the challenge of the cloud property retrieval in passive sensor, we develop a novel VAE based approach to learn domain invariant representation that capture the spatial pattern from multiple satellite remote sensing data (VDAM), to build a domain invariant cloud property retrieval method to accurately classify different cloud types (labels) in the passive sensing dataset. We further exploit the weight based alignment method on the label space to learn a powerful domain adaptation technique that is pertinent to the remote sensing application. Experiments demonstrate our method outperforms other state-of-the-art machine learning methods and achieves higher accuracy in cloud property retrieval in the passive satellite dataset.

Wireless reprogramming is a significant yet challenging issue in the Internet of Things (IoT). Existing methods designed for wireless sensor networks (WSN) are inadequate for IoT scenarios and have exposed severe security vulnerabilities. To address this problem, we propose Software-Defined Function (SDF), a secure and wireless reprogramming architecture for IoT named SFIOT. The key is to implement a secure communication interface between the control layer and the infrastructure layer. To this end, a set of security protocols which ensure authentication and confidentiality are designed, and their security is proved theoretically and formally verified. In addition, the constrained capabilities of IoT devices are taken into consideration in our design. We have tested the performance of SDF through a set of experiments and presented a use case on a photovoltaic energy system. The evaluation results show that the proposed protocols can be implemented in real-world IoT applications.

In this paper, we design and implement a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms. In this system, some important aspects like knowledge bases system, knowledge representation, inference engine have been addressed. The system asks users different questions and inference engines will use the certainty factor to prune out low possible solutions. The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system. Our expert system is generic and flexible, which can be integrated with any rule bases system in disease diagnosis.

The recent surge in the prosperity of the Internet of Things (IoT) has been attracting an increasing number of researchers and experts with great attention due to its significant economic and social values. The IoT brings appealing opportunities and new challenges for both the current and future Internet. In practice, various IoT smart devices are generally pre-programmed and deployed specifically in the proper place to fulfill corresponding functions according to divergent requirements. However, lately, these pre-stored functions tend to be upgraded or reprogrammed more frequently on account of the increment of dynamic needs or urgent situations. Inspired by Software Defined Networking (SDN), the authors propose a framework in this work: Software Defined Function (SDF) for IoT, enabling IoT smart devices to be upgraded or reprogrammed securely and remotely. The authors further present a protocol named as OpenFunction stemmed from OpenFlow. Moreover, the security properties of this protocol are analyzed. Finally, the authors implement a preliminary SDF system and evaluate its performance. Experimental results indicate that OpenFunction allows a controller to update or rewrite functions in IoT devices, as well as to obtain flexibility and security. Accordingly, this work contributes to the future fusion of SDN and IoT technologies.

Topologically associated domains (TADs) are spatial and functional units of metazoan chromatin structure. Interpretation of the interplay between regulatory factors and chromatin structure within TADs is crucial to understand the spatial and temporal regulation of gene expression. However, a computational metric for the sensitive characterization of TAD regulatory landscape is lacking. Here, we present the spatial density of open chromatin (SDOC) metric as a quantitative measurement of intra-TAD chromatin state and structure. SDOC sensitively reflects epigenetic properties and gene transcriptional activity in TADs. During mouse T-cell development, we found that TADs with decreased SDOC are enriched in repressed developmental genes, and the joint effect of SDOC-decreasing and TAD clustering corresponds to the highest level of gene repression. In addition, we revealed a pervasive preference for TADs with similar SDOC to interact with each other, which may reflect the principle of chromatin organization.

Edge Artificial Intelligence (AI) is a timely complement of cloud-based AI. By introducing intelligence to the edge, it alleviates privacy concerns of streaming and storing data to the cloud, enables real-time operations where milliseconds matter, and brings AI services to remote areas with poor networking infrastructures. Security is a significant problem in Edge AI applications such as self-driving cars and intelligent healthcare. Since the edge devices are empowered to process data and take actions, attacking and compromising them can cause serious damage. However, the wide deployment of computationally limited devices in edge environments and the increasing happening of side-channel (or leakage) attacks pose critical challenges to security. This article thereby aims to enhance the security for Edge AI by designing and developing lightweight and leakage-resilient authenticated key exchange (LRAKE) protocols. Compared with available LRAKE protocols, the proposed protocols in this article can be effortless applied in some mainstreaming security and communication standards. Moreover, this article realizes prototypes and presents implementation details; and a use case of applying the proposed protocol in Bluetooth 5.0 is illustrated. The theoretical design and implementation details will provide a guidance of applying the LRAKE protocols in Edge AI applications.

Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate since 1989. However, a relatively high false positive rate and a low specificity in mammography technology still exist. In this work, a computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous, consisting of three consecutive image processing, feature selection, and image classification stages. In designing the system, the discrete wavelet transforms (Daubechies 2, Daubechies 4, and Biorthogonal 6.8) and the Fourier cosine transform were first used to parse the mammogram images and extract statistical features. Then, an entropy-based feature selection method was implemented to reduce the number of features. Finally, different pattern recognition methods (including the Back-propagation Network, the Linear Discriminant Analysis, and the Naive Bayes Classifier) and a voting classification scheme were employed. The performance of each classification strategy was evaluated for sensitivity, specificity, and accuracy and for general performance using the Receiver Operating Curve. Our method is validated on the dataset from the Eastern Health in Newfoundland and Labrador of Canada. The experimental results demonstrated that the proposed automatic mammogram analysis system could effectively improve the classification performances.

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as 'hubs' in protein-protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

MOTIVATION: During development of the mammalian embryo, histone modification H3K4me3 plays an important role in regulating gene expression and exhibits extensive reprograming on the parental genomes. In addition to these dramatic epigenetic changes, certain unchanging regulatory elements are also essential for embryonic development. RESULTS: Using large-scale H3K4me3 chromatin immunoprecipitation sequencing data, we identified a form of H3K4me3 that was present during all eight stages of the mouse embryo before implantation. This 'stable H3K4me3' was highly accessible and much longer than normal H3K4me3. Moreover, most of the stable H3K4me3 was in the promoter region and was enriched in higher chromatin architecture. Using in-depth analysis, we demonstrated that stable H3K4me3 was related to higher gene expression levels and transcriptional initiation during embryonic development. Furthermore, stable H3K4me3 was much more active in blood tumor cells than in normal blood cells, suggesting a potential mechanism of cancer progression. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In disaster scenarios, such as an area after a terrorist attack, security is a significant problem since communications involve information for the rescue officers, such as polices, militaries, emergency medical technicians, and the survivors. Such information is critically important for the rescue organizations; and protecting the privacy of the survivors is required. Normally, authenticated key exchange (AKE) is an underlying approach for security. However, available AKE protocols are either inconvenient or infeasible in disaster areas due to the very nature of disasters. To address the security problem in disaster scenarios, we propose two pairing-free identity-based AKE (ID-AKE) protocols that have unbalanced computational requirements on the two parties. Compared with existing AKE protocols, the proposed protocols have a number of advantages in disaster scenarios: 1) they are more convenient than symmetric cryptography-based AKE protocols since they do not require any preshared secret between the parties; 2) they are more feasible than asymmetric cryptography-based AKE protocols since they do not require any online server; and 3) they are more friendly to battery-powered and computationally limited devices than pairing-based and pairing-free ID-AKE protocols since they do not involve any bilinear pairing (a time-consuming operation), and have lower computational requirement on the limited party. Security of the proposed protocols are analyzed in detail; and prototypes of them are implemented to evaluate the performance. We also illustrate the application of the protocols through a vivid use case in a terrorist attack scenario.

The more and more connected devices and rapidly developing Internet of Things (IoT) applications are the foundations of the future smart cities which provide ubiquitous services. The extension and proliferation of the technology brings huge security challenges, especially for the infrastructural IoT applications in the open environments. The traditional access model, such as Role-Based Access Control (RBAC) cannot provide the flexible fine-grained access control which is required due to dynamic changing users and environments. On the other hand, some other features of the IoT applications like constrained-resources devices and large-scale deployments make it very difficult to apply Attribute-Based Access Control (ABAC). Furthermore, the ABAC mechanism cannot control the way that the requester uses the services once the requester obtains the access permission. To address these issues, in this paper, we propose an access control model based on ABAC with Contexts-States-Awareness. The proposed model is implemented by using Semantic Web technologies with a sample ontology for the model and some access control policies in SWRL (Semantic Web Rule Language). We also give a logical architecture which is the extension from the reference architecture of XACML eXtensible Access Control Markup Language specification.

Location spoofing and non-line-of-sight (NLOS) propagation are two leading reasons of serious localization errors in wireless networks. Previous studies have managed to identify these two factors separately. However, when present in the same system, these two factors can cause localization errors in a similar manner, making the identification difficult. In this paper, we address the problem of identifying location spoofing in NLOS conditions. We first carry out a geometric analysis on NLOS and derive a bound that can be used to differentiate NLOS from location spoofing. Based on the bound, we propose an identification method. We show that the proposed method is secure against different types of spoofing attacks including those from individuals and from multiple collaborative attackers. In particular, it can be used to identify the well-known “perfect location spoofing.” Simulation in wireless sensor networks indicates that our method can achieve a high accuracy with 0 false positive on identifying individual attacks and perfect location spoofing in NLOS conditions.

With technologies developed in the Internet of Things, embedded devices can be built into every fabric of urban environments and connected to each other; and data continuously produced by these devices can be processed, integrated at different levels, and made available in standard formats through open services. The data, obviously f a form of `big data', is now seen as the most valuable asset in developing intelligent applications. As the sizes of the IoT data continue to grow, it becomes inefficient to transfer all the raw data to a centralised, cloud-based data centre and to perform efficient analytics even with the state-of-the-art big data processing technologies. To address the problem, this article demonstrates the idea of "distributed intelligence" for sensor data computing, which disperses intelligent computation to the much smaller while autonomous units, e.g., sensor network gateways, smart phones or edge clouds in order to reduce data sizes and to provide high quality data for data centres. As these autonomous units are usually in close proximity to data consumers, they also provide potential for reduced latency and improved quality of services. We present our research on designing methods and apparatus for distributed computing on sensor data, e.g., acquisition, discovery, and estimation, and provide a case study on urban air pollution monitoring and visualisation.

The 5th generation wireless system 5G will support Internet of Things IoT by increasing the interconnectivity of electronic devices to support a variety of new and promising networked applications such as the home of the future, environmental monitoring networks, and infrastructure management systems. The potential benefits of the IoT are as profound as they are diverse. However, the benefits of the IoT come with some significant challenges. Not the least of these is that the increased interconnectivity integral to an IoT network increases its vulnerability to malevolent attacks. There is still no proven methodology for the design of security frameworks with device authentication and access control. This paper attempts to address this problem through the development of a prototype security framework with robust and transparent security protection. This includes an investigation into the security requirements of three different characteristic IoT scenarios concretely, body IoT, home IoT, and hotel IoT, a design of new authentication mechanisms, and an access control subsystem with fine-grained roles and risk indicators. Our prototype security framework gives us an insight into some of the major difficulties of IoT security as well as providing some feasible solutions. Copyright © 2015 John Wiley & Sons, Ltd.

The networks of intelligent building are usually consist of a great number of smart devices. Since many smart devices only support on-site configuration and upgrade, and communication between devices could be observed and even altered by attackers, efficiency and security are two key concerns in maintaining and managing the devices used in intelligent building networks. In this paper, the authors apply the technology of software defined networking to satisfy the requirement for efficiency in intelligent building networks. More specific, a protocol stack in smart devices that support OpenFlow is designed. In addition, the authors designed the lightweight security mechanism with two foundation protocols and a full protocol that uses the foundation protocols as example. Performance and session key establishment for the security mechanism are also discussed.

A body sensor network (BSN) is typically a wearable wireless sensor network. Security protection is critical to BSNs, since they collect sensitive personal information. Generally speaking, security protection of BSN relies on identity (ID) and key distribution protocols. Most existing protocols are designed to run in general wireless sensor networks, and are not suitable for BSNs. After carefully examining the characteristics of BSNs, the authors propose human interactive empirical channel‐based security protocols, which include an elliptic curve Diffie–Hellman version of symmetric hash commitment before knowledge protocol and an elliptic curve Diffie–Hellman version of hash commitment before knowledge protocol. Using these protocols, dynamically distributing keys and IDs become possible. As opposite to present solutions, these protocols do not need any pre‐deployment of keys or secrets. Therefore compromised and expired keys or IDs can be easily changed. These protocols exploit human users as temporary trusted third parties. The authors, thus, show that the human interactive channels can help them to design secure BSNs.