Distributed acoustic sensing (DAS) serves as an embedded physical-layer sensing modality within critical coastal infrastructure, providing high-resolution spatiotemporal observations of marine geohazards (like marine seismic) to support the modeling, monitoring, and resilience assessment of computational social systems under oceanic disturbances. However, strong background noise from marine gravity waves, biological activity, and shipping traffic often overwhelms low-level seismic signals within DAS data. This article proposes a self-supervised denoising model, termed progressive masked autoencoder denoising network (PgMAD), which exploits the inherent spatiotemporal correlations among multichannel seismic recordings to perform high noise suppression without any annotated clean data. Specifically, it utilizes an easy-to-difficult reconstruction strategy that progressively uncovers the underlying signal features. This approach alleviates the computational burden typically imposed by fixed masking schemes. Real-world experiments on the DAS recordings of Shantou earthquakes reveal that PgMAD delivers a competitive boost in signal-to-noise ratio at an affordable training cost. DAS signals Denoising is essential not only for data quality but also for unlocking their utility in computational social systems, particularly in building resilient applications such as disaster early warning and critical infrastructure monitoring.

The advent of unmanned aerial vehicle (UAV) networks, renowned for their expansive coverage capabilities and heightened adaptability, presents a promising landscape for bolstering the efficacy of Internet of Things (IoT) data transmissions. Nevertheless, the integration of UAVs into IoT ecosystems introduces a spectrum of security challenges, notably data tampering, man-in-the-middle (MitM) attacks, and eavesdropping, which threaten the integrity and confidentiality of transmitted information. Since covert transmission has the characteristics of strong concealment and difficult detection, UAV networks based on information hiding become a new paradigm for solving these security problems. This article proposes an information hiding algorithm based on super-resolution enhancement and significant region detection (IH-SESD). This algorithm incorporates the super-resolution enhanced SRCNN and the U2Net salient region detection technologies. Comprehensive performance analysis and comparisons with existing methods demonstrate the superiority of the proposed IH-SESD algorithm.

Leveraging artificial intelligence models to enhance the performance of intrusion detection systems has become an important component in the field. However, as the scale of networks continues to expand, the structure of networks becomes more complex, and the amount of data in the networks grows larger. Existing methods are facing numerous challenges, including difficulties in constructing training datasets for models, challenges in transferring and reusing models, and high costs associated with model training. This paper introduces a novel approach named BedIDS. This method involves constructing the evolutionary process of network behavior and calculating the evolutionary characteristics of network behavior. Using only the most fundamental five network traffic features, including IP addresses, BedIDS achieves rapid and accurate detection performance on a device equipped with a 3060ti graphics card. We conducted tests using the CICIDS2017 and UNSW-NB15 datasets to evaluate its performance. Experimental results demonstrate that BedIDS maintains high detection accuracy and improves detection speed while requiring a relatively low AI computing force.

Cloud native application is especially susceptible to application layer DDoS attack. This attributes to the internal service calls, by which microservices cooperate and communicate with each other, amplifying the effect of application layer DDoS attack. Since different services have varying degrees of sensitivity to an attack, a sophisticated attacker can take advantage of those especially expensive API calls to produce serious damage to the availability of services and applications with ease. To better analyze the severity of and mitigate application layer DDoS attacks in cloud native applications, we propose a novel method to evaluate the effect of application layer DDoS attack, that is able to quantitatively characterize the amplifying effect introduced by the complex structure of application system. We first present the descriptive model of the scenario. Then, Riemannian manifolds are constructed as the state spaces of the attack scenarios, in which attacks are described as homeomorphisms. Finally, we apply differential geometry principles to quantitatively calculate the attack effect, which is derived from the action of an attack and the movement it produces in the state spaces. The proposed method is validated in various application scenarios. We show that our approach provides accurate evaluation results, and outperforms existing solutions.

In the age of data science and connected devices of all kinds, users have gained a lot of convenience. However, the massive data generated by users from the communication network is faced with security problems such as intrusion, tampering, hijacking, etc. According to the properties of covert transmission, the network with data covert embedding becomes an effective means to solve these challenges. In this paper, we propose a four-dimensional space-based data multi-embedding mechanism to protect data in the network. Specifically, a feature extractor is first used to extract vectors for embedding. Next, a Schmitt-based four-dimensional space is constructed, two of which are used to embed robust data to hide confidential information. The other two dimensions are used to embed fragile data to detect whether the signal has been tampered with. According to the experimental comparison with other methods, it shows that the method proposed in this paper achieves satisfactory performance.

In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger grasp poses and hand configurations from a single-shot point cloud. Moreover, we create a synthetic grasp dataset that consists of five thousand cluttered scenes, 80 object categories, and 20 million annotations. We perform a comprehensive empirical study and demonstrate the effectiveness of our grasping representation and CMG-Net. Our work significantly outperforms the state-of-the-art for three-finger robotic hands. We also demonstrate that the model trained using synthetic data perform very well for real robots.

The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no man’s land, such as deserts and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this article, to secure entity identity and task data, we propose a blockchain-supported secure multi-UAV task management scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multitasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved practical byzantine fault tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about$0.5\times $and$3.7\times $of the existing method when the block size or the number of blockchain nodes increases, respectively.

Calculating system damage caused by a cyberattack can help in understanding the impact and destructiveness of the attack to discover system security weaknesses. Thus, system damage calculations is important in the process of network offense–defense confrontation. However, there is little research on attack damage calculation. Current methods are unable to quantitatively evaluate the impact of an attack in a rational and accurate way. The lack of theoretical support and the complexity of both cyber systems and attacks bring tremendous challenges to attack damage calculations. In this paper, we propose a novel method called ADCaDeM to enable quantitative attack damage calculation based on a differential manifold. The damage is a negative utility produced by attack behaviors on an attacked object, which can be characterized and expressed by its attributes. We formally map the attack behaviors into a space constructed by the attributes of the attacked object in a mathematical way. Then, we propose an algorithm to construct these attributes as a differential manifold to represent their algebraic topological structure. According to the theory of tangent vectors and geodesics on the differential manifold, we can calculate attack behavioral utility in a physical way, such as computing the work done in physics. Regardless of the complexity of the dimensional structure of the attributes, the differential manifold structure can reasonably represent and calculate the damage caused by an attack. We simulate a data theft attack and a web penetration attack to test the performance of ADCaDeM and compare it with existing methods. Our experimental results illustrate ADCaDeM's advance in terms of rationality for calculating the damage caused by some typical cyberattacks.

Multimedia information (e.g., photos) is subject to numerous attacks when extensively dispersed, excellent stealth and robustness play a critical role in information concealment. Next, to further improve the imperceptibility of the generated images, we introduce an information embedding based on feature extraction, since the feature points themselves are robust as strong corner points in the images. We will perform specific practical applications in information-hiding scenarios, such as ticket anticounterfeiting, copyright protection, tampering hints, and covert marking. As a result, while assuring the accuracy of hidden data, assault resistance must also be addressed. In this paper, a strategy for watermark embedding utilizing image feature points is proposed. In principle, the embedding and extraction of digital watermarks are achieved in the transform domain by discrete wavelet transform processing to make digital images more robust than before. The effectiveness of the algorithm is analyzed by experimental data, in which different wavelet bases are used. To check the robustness of the watermarking algorithm, various attacks are performed on watermarked images.

In medicine, any abnormalities in the number of chromosomes or the structure of chromosomes may cause the newborn baby to suffer from genetic diseases, such as Edward syndrome and so on. Chromosome karyotype analysis is the most important and common method for prenatal diagnosis to determine whether a newborn baby has chromosome defects refers to segment chromosome instances from stained cell images and arrange chromosome instances according to their categories. However, due to the non-rigid nature of chromosomes, chromosome instances may overlap and adhere to each other, which makes the task of segmenting chromosome instances time-consuming and error-prone. This paper proposes a novel chromosome instance segmentation method that includes three stages. First, we segment a given stained cell image into several segments using geometric connectivity. Second, a machine learning method is proposed to distinguish chromosome of individual instances and clusters. Finally, a deep learning-based method is applied to separate chromosome instances from clusters. It shows that the proposed method achieves 97.61% instance segmentation accuracy in a hold-out clinical dataset with 162 cell images consisting of 7,452 chromosome instances, which is a promising result in clinical application. The innovation of this work is to combine geometry and deep learning to handle tasks for different stages of chromosome instance segmentation issue. The benefit of this innovation is that it can obtain a much better performance than existing geometric-based methods with a small number of training samples. Meanwhile, the segmentation performance of the proposed method is superior to existing methods fully based on deep learning.

Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

This paper proposes a new structural design to excite surface plasmonic polaritons to enhance the double‐ring interference structure. The double‐ring structure was etched into a thin film to form fundamental interference patterns, and periodic concentric‐ring grooves were employed to gather energy from the surrounding regions through the excitation of surface plasmonic polaritons. Accordingly, the energy of the incident light can be concentrated at the center. The surface plasmon modulates the interference pattern and the transmission spectra. The transmission peak position and its intensity can be tuned by changing the alignment of the grooves. The proposed structure can be applied for designing plasmonic devices as useful components of the plasmonic toolbox.

Because of the sequence and number of calls of functions, software network cannot reflect the real execution of software. Thus, to detect crucial functions (DCF) based on software network is controversial. To address this issue, from the viewpoint of software dynamic execution, a novel approach to DCF is proposed in this paper. It firstly models, the dynamic execution process as an execution sequence by taking functions as nodes and tracing the stack changes occurring. Second, an algorithm for deleting repetitive patterns is designed to simplify execution sequence and construct software sequence pattern sets. Third, the crucial function detection algorithm is presented to identify the distribution law of the numbers of patterns at different levels and rank those functions so as to generate a decision-function-ranking-list (DFRL) by occurrence times. Finally, top-k discriminative functions in DFRL are chosen as crucial functions, and similarity the index of decision function sets is set up. Comparing with the results from Degree Centrality Ranking and Betweenness Centrality Ranking approaches, our approach can increase the node coverage to 80%, which is proven to be an effective and accurate one by combining advantages of the two classic algorithms in the experiments of different test cases on four open source software. The monitoring and protection on crucial functions can help increase the efficiency of software testing, strength software reliability and reduce software costs.

Malware threats and privacy protection are two of the biggest challenges in the cloud computing environment. Many studies have focused on the accuracy of malware detection, but they did not sufficiently take into account the privacy protection of cloud tenants. This paper proposes a novel malware detection model, based on semi-supervised transfer learning (SSTL) for the cloud, that consists of detection, prediction, and transfer components. To protect the privacy of tenants in the public cloud, a byte classifier based on a recurrent neural network (RNN) for its detection component is designed to detect malware. However, because it is limited by the scarcity of training samples, the accuracy of the byte classifier is only 94.72% after supervised learning. An asm classifier is proposed for the prediction component, and it achieves 99.69% accuracy. The transfer component invokes the prediction component to classify an unlabeled dataset, and it combines the predicted labels and byte features of the unlabeled dataset into a new training dataset. Through the advantages of semi-supervised learning, the new dataset is transferred to the byte classifier for training again. The test results on the Kaggle malware datasets show that semi-supervised transfer learning improved the accuracy of the detection component from 94.72% to 96.9%. The improved malware detection method can not only do a better job of resolving the privacy concerns of tenants in the public cloud than other similar methods, but it can also detect malware more accurately.

Summary Information Centric Networking (ICN) is a novel future network architecture that is focusing on content distribution. Its ubiquitous caching schemes can improve network performance. In this paper, we propose a node importance to community based caching scheme with network coding in ICN, which is named as NICNC. For each community, content router makes cache decision depending on its node importance to community to make content cached more reasonable in temporal and spatial distribution. Moreover, by applying network coding into ICN, one coded blocks containing information of multiple chunks can satisfy multiple interests for different chunks sent by different consumers. This can significantly enhance cache diversity and cache hit rate without increasing cache capacity. Experimental results show that our scheme can improve the network performance at many aspects, such as average download time, cache hit rate, and instantaneous hop reduction rate.

In a software network system, it is of great significance to identify key functions for software fault detection and maintenance. In order to better understand the characteristics and internal structure of software, a key Node Discovery algorithm based on Evidence Theory called NDET is proposed in this paper. First, the software complex network model is constructed according to the execution process of the software. Based on the Dempster-Shafer evidence theory (D-S evidence theory), the discernment frame is formed, the maximum and minimum values of the network degree and strength are determined. Second, the Basic Probability Assignment (BPA) of each node degree is calculated by considering the node degree distribution ratio value. Third, based on Dempster’s rule of combination, the evidential centrality of the node itself and the fluctuation value of the node influenced by neighbor nodes are considered for the key measurement. Finally, by using the Susceptible–Infected–Recovered (SIR) model to simulate the spreading process on real software networks, the performance of NDET is evaluated. Experiment results verify the validity and accuracy of NDET for identifying key function nodes in software.

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

With the rapid development of the Internet of vehicles, there is a huge amount of multimedia data becoming a hidden trouble in the Internet of Things. Therefore, it is necessary to process and store them in real time as a way of big data curation. In this paper, a method of real-time processing and storage based on CDN in vehicle monitoring system is proposed. The MPEG-DASH standard is used to process the multimedia data by dividing them into MPD files and media segments. A real-time monitoring system of vehicle on the basis of the method introduced is designed and implemented.

Identifying influential nodes is important for software in terms of understanding the design patterns and controlling the development and the maintenance process. However, there are no efficient methods to discover them so far. Based on the invoking dependency relationships between the nodes, this paper proposes a novel approach to define the node importance for mining the influential software nodes. First, according to the multiple execution information, we construct a weighted software network (WSN) to denote the software execution dependency structure. Second, considering the invoking times and outdegree about software nodes, we improve the method PageRank and put forward the targeted algorithm FunctionRank to evaluate the node importance (NI) in weighted software network. It has higher influence when the node has lager value of NI. Finally, comparing the NI of nodes, we can obtain the most influential nodes in the software network. In addition, the experimental results show that the proposed approach has good performance in identifying the influential nodes.

A network security metric may provide quantifiable evidence to assist security practitioners in securing computer networks. However, research on security metrics based on attack graph is not applicable to the characteristics of internal attack; therefore we propose an internal network security metric method based on attack probability. Our approach has the following benefits: it provides the method of attack graph simplification with monitoring event node which could solve the attack graph exponential growth with the network size, while undermining the disguise of internal attacks and improving the efficiency of the entire method; the method of attack probability calculation based on simplified attack graph can simplify the complexity of internal attacks and improve the accuracy of the approach.