Search

A device's intrinsic security fingerprint, representing its physical characteristics, serves as a unique identifier for user devices and is highly regarded in the realms of device security and identity recognition. However, fluctuations in the environmental noise can introduce variations in the physical features of the device. To address this issue, this paper proposes an innovative method to enable the device's intrinsic security fingerprint to adapt to environmental changes, aiming to improve the accuracy of the device's intrinsic security fingerprint recognition in real-world physical environments. This paper initiates continuous data collection of device features in authentic noisy environments, recording the temporal changes in the device's physical characteristics. The problem of unstable physical features is framed as a restricted statistical learning problem with a localized information structure. This paper employs an aggregated hypergraph neural network architecture to process the temporally changing physical features. This allows the system to acquire aggregated local state information from the interactive influences of adjacent sequential signals, forming an adaptive environment-enhanced device intrinsic security fingerprint recognition model. The proposed method enhances the accuracy and reliability of device intrinsic security fingerprint recognition in outdoor environments, thereby strengthening the overall security of terminal devices. Experimental results indicate that the method achieves a recognition accuracy of 98% in continuously changing environmental conditions, representing a crucial step in reinforcing the security of Internet of Things (IoT) devices when confronted with real-world challenges. [ABSTRACT FROM AUTHOR]

Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Subsequently, the same DLOFs are analyzed in order to characterize the detected outliers with their relevant subspace and their same-type anomalies. Experiments on various datasets show the efficacy of our method. Indeed, we demonstrate through an experimental evaluation that the proposed approach is competitive compared to the existing solutions in terms of both detection and characterization accuracy. [ABSTRACT FROM AUTHOR]

As next generation wireless communication and smart manufacturing evolve, Industrial Internet of Things (IIoT) has emerged. Due to the advantage in coverage and improved capacity of cellular networks, femtocells are densely deployed in macrocell of cellular networks contributing to Ultra-dense Wireless Network (UDWN). Nevertheless, it is faced with the challenge of how to reduce interference to improve spectrum efficiency and system performance for Non-orthogonal Multiple Access (NOMA) in IIoT. To this end, we identify and formulate the channel resource allocation issue as a sum-rate maximization problem under the guarantee of Quality of Service (QoS) and fairness. To solve the problem, we first construct a hypergraph by considering interference relationships. Thereafter, we convert the original problem into a maximum weighted clique set problem in hypergraph theory, based on which a heuristic resource allocation algorithm is proposed. Moreover, we analyse the property of the proposed algorithm and its complexity. Finally, simulation results show that the proposed method outperforms graph-based algorithm and other hypergraph-based algorithm in improving the system throughput. [ABSTRACT FROM AUTHOR]

The rapid development of cloud computing has generated numerous cloud service providers. In a three-tier cloud service architecture, maximizing the cloud service profit is not only a goal pursued by cloud service providers but also a potent force that drives the continuous development of cloud computing technology. The traditional methods of maximizing cloud service profits are either limited to cloud resource management or service request distribution. Few work contributes to maximizing the profit of cloud service providers while considering both resource management and request distribution. Besides, the heterogeneity of servers is also an important concern when configuring the multiserver system. Therefore, this paper proposes a multiserver configuration and request distribution framework oriented to a three-tier cloud service architecture for maximizing the profit of cloud service providers. Compared with the benchmark method, extensive numerical simulation experiments show that our proposed framework can not only maximize the profit of cloud service providers, but also has a well-time efficiency. [ABSTRACT FROM AUTHOR]

Consider the following nonparametric model: Yni=g(xni)+εni, 1 ⩽ i ⩽ n, where x n i ∈ A are the nonrandom design points and A is a compact set of ℝm for some m=1, g (·) is a real valued function defined on A , and εn1, ⋅, εnn are zero mean φ-mixing random errors with finite moment of 2+δ order for some δ>0. Under some general conditions, we obtain the uniformly asymptotic normality of the weighted estimator of g (·). The rate of Berry-Esseen bound can approximate O(n−1/4) under some appropriate conditions. The validity of the main results is partially illustrated via a numerical simulation. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing and the popularization of mobile equipment, location-based cloud services (LBCS) are widely used, bringing great convenience to human industrial production and daily life. However, since LBCS is a location-based service, how to protect personal privacy when receiving the service is a matter of widespread concern for cloud users. To solve this problem, this paper introduces a new privacy protection method based on K-anonymity, which not only protects the cloud user's query privacy as well as location privacy but also resolves the contradiction between privacy protection and service quality. From the simulation experimental results, we verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain. [ABSTRACT FROM AUTHOR]

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, computers, as well as smart devices, are connected through communication networks making them more vulnerable to attacks. Honeypots are proposed as deception tools but usually used as part of a proactive defense strategy. Hence, this article demonstrates how honeypots data can be analyzed in an active defense strategy. Furthermore, anomaly detection based on unsupervised machine learning techniques allows to build autonomous systems and to detect unknown anomalies without the need for prior knowledge. However, the unsupervised techniques applied for honeypots data analysis do not value the advantages of these tools' data, particularly the high probability that they include a large number of previously unseen anomalies with unexpected and diverse patterns. Therefore, in the present work, the aim is to improve the unsupervised anomaly detection in honeypots data by varying the data feature subset and the parameterization of the anomaly detection algorithm. To this purpose, an outlier ensemble with LOF (Local Outlier Factor) as a base algorithm is proposed. The ensemble outperforms existing solutions as depicted in the experiments where a detection rate higher than 92% is achieved. [ABSTRACT FROM AUTHOR]

Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively. [ABSTRACT FROM AUTHOR]

Unlike traditional embedded systems that almost have only one criticality level, many complex embedded systems nowadays are mixed-critical and are more and more widely used. There has been a lot of research on mixed-criticality (MC) systems. In this paper, we present a survey on the MC systems on these research. First, we discuss the exaltation of the schedulability of MC systems. As improving schedulability may lead to quality-of-service (QoS) reduction of MS systems. Therefore, we investigate the approaches to solve this problem. Improving QoS of MS systems may inevitably increase the energy consumption. Then, we introduce the researches that take the energy efficiency as a design requirement of MC systems. Few MC systems regard fault-tolerance as the design requirement, thus, we extensively investigate fault-tolerance of MC systems. In addition, we introduce some of the main applications for MC systems. [ABSTRACT FROM AUTHOR]

Scientific workflow is a common model to organize large scientific computations. It borrows the concept of workflow in business activities to manage the complicated processes in scientific computing automatically or semi-automatically. The workflow scheduling, which maps tasks in workflows to parallel computing resources, has been extensively studied over years. In recent years, with the rise of cloud computing as a new large-scale distributed computing model, it is of great significance to study workflow scheduling problem in the cloud. Compared with traditional distributed computing platforms, cloud platforms have unique characteristics such as the self-service resource management model and the pay-as-you-go billing model. Therefore, the workflow scheduling in cloud needs to be reconsidered. When scheduling workflows in clouds, the monetary cost and the makespan of the workflow executions are concerned with both the cloud service providers (CSPs) and the customers. In this paper, we study a series of cost-and-time-aware workflow scheduling algorithms in cloud environments, which aims to provide researchers with a choice of appropriate cloud workflow scheduling approaches in various scenarios. We conducted a broad review of different cloud workflow scheduling algorithms and categorized them based on their optimization objectives and constraints. Also, we discuss the possible future research direction of the clouds workflow scheduling. [ABSTRACT FROM AUTHOR]

Hierarchical clustering is a classical method to provide a hierarchical representation for the purpose of data analysis. However, in practical applications, it is difficult to deal with massive datasets due to their high computation complexity. To overcome this challenge, this paper presents a novel distributed storage and computation hierarchical clustering algorithm, which has a lower time complexity than the standard hierarchical clustering algorithms. Our proposed approach is suitable for hierarchical clustering on massive datasets, which has the following advantages. First, the algorithm is able to store massive dataset exceeding the main memory space by using distributed storage nodes. Second, the algorithm is able to efficiently process nearest neighbor searching along parallel lines by using distributed computation at each node. Extensive experiments are carried out to validate the effectiveness of the DHC algorithm. Experimental results demonstrate that the algorithm is 10 times faster than the standard hierarchical clustering algorithm, which is an effective and flexible distributed algorithm of hierarchical clustering for massive datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of embedded systems, users and services have been greatly facilitated while also experiencing security threats as a result of cyber-attacks and system vulnerabilities. Currently, the real-time embedded system (RTES) focus is to deal with these security issues. In this paper, we introduce a short review of security-aware techniques for RTES. We mainly discuss two common approaches to improve the security of RTESs. The first approach is achieved by exploring specific attacks. The second approach is realized by deploying security-guaranteed services. However, improving the security of embedded systems may cause excessive energy consumption at the same time. Therefore, we investigate the secure and energy-aware RTESs on a wide range of research. In addition, we study a number of common applications used in secure RETSs. This paper stands for providing awareness and better understanding of the current RTES research status as well as technical theory behind it. Hence, the RTES security issues are resolved. [ABSTRACT FROM AUTHOR]

By advances in cloud storage systems, users have access to the data saved in the cloud and can manipulate the data without limitation of time and place. As the data owner no longer possesses data physically, he is required to ensure the integrity of the data stored in the cloud with the public key given by public key infrastructure (PKI). Thus the security of PKI and certificates are essential. However, there are numerous security risks in the traditional PKI and it is complex to administer the certificates. Certificateless public key cryptography is used in this paper to solve these problems. We also use elliptic curve group to reduce computation overhead. In this paper, we design a certificateless public verification mechanism to check the integrity of data outsourced in the cloud and we further extend it to support a multiuser group by batch verification. Specifically, a public verifier who replaces the data owner to check the integrity in the proposed scheme does not require to manage any certificates during the verification process. Meanwhile, a verifier is not required to download the entire file for integrity checking. Theoretical analyses verify the security of our scheme and experimental results show its efficiency. [ABSTRACT FROM AUTHOR]

Internet of Things devices, such as video-based detectors or road side units are being deployed in emerging applications like sustainable and intelligent transportation systems. Oftentimes, stringent operation and energy cost constraints are exerted on this type of applications, necessitating a hybrid supply of renewable and grid energy. The key issue of a cost-constrained hybrid of renewable and grid power is its uncertainty in energy availability. The characteristic of approximate computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose an energy-adaptive task allocation scheme that optimally assigns real-time approximate-computation tasks to individual processors and subsequently enables a matching of the cost-constrained hybrid supply of energy with the energy demand of the resultant task schedule. We then present a quality of service (QoS)-driven task scheduling scheme that determines the optional execution cycles of tasks on individual processors for optimization of system QoS. A dynamic task scheduling scheme is also designed to adapt at runtime the task execution to the varying amount of the available energy. Simulation results show that our schemes can reduce system energy consumption by up to 29% and improve system QoS by up to 108% as compared to benchmarking algorithms. [ABSTRACT FROM AUTHOR]

Due to the abundant storage resources and high reliability data service of cloud computing, more individuals and enterprises are motivated to outsource their data to public cloud platform and enable legal data users to search and download what they need in the outsourced dataset. However, in “Paid Data Sharing” model, some valuable data should be encrypted before outsourcing for protecting owner’s economic benefits, which is an obstacle for flexible application. Specifically, if the owner does not know who (user) will download which data files in advance and even does not know the attributes of user, he/she has to either remain online all the time or import a trusted third party (TTP) to distribute the file decryption key to data user. Obviously, making the owner always remain online is too inflexible, and wholly depending on the security of TTP is a potential risk. In this paper, we propose a flexible, secure, and reliable data sharing scheme based on collaboration in multicloud environment. For securely and instantly providing data sharing service even if the owner is offline and without TTP, we distribute all encrypted split data/key blocks together to multiple cloud service providers (CSPs), respectively. An elaborate cryptographic protocol we designed helps the owner verify the correctness of data exchange bills, which is directly related to the owner’s economic benefits. Besides, in order to support reliable data service, the erasure-correcting code technic is exploited for tolerating multiple failures among CSPs, and we offer a secure keyword search mechanism that makes the system more close to reality. Extensive security analyses and experiments on real-world data show that our scheme is secure and efficient. [ABSTRACT FROM AUTHOR]

Event-driven wireless sensor networks (WSNs) are equipped with the sensor nodes which are able to capture the vital changes in the measurements and report them to the base station. Due to the nature of harsh and unattended environments, it is impossible to install sensor nodes manually. Therefore, a random deployment fashion such as by aircraft is required. However, with high probability, sensor nodes will not have uniform distribution in the environment which will cause “hole” in the network. In another issue, sensor nodes are limited in energy supply and transmission range, and thus an appropriate technique is essential to calculate energy efficient routes to relay data from the sensor nodes to the base station. In hierarchical routing category, clustering techniques attempt to partition the sensor nodes in the network to appropriate groups and select the cluster heads to contact with the base station directly. However, in large scale WSNs, most of the sensor nodes are far from the base station and cannot contact directly. Most of the clustering techniques do not consider how cluster heads can reach to the base station. In our research, A Delaunay triangulation approach is employed to detect holes in the network. Then, due to the overhead of clustering methods to define cluster areas, a virtual gridding scheme is applied to define cluster areas. To overcome uncertainties in the environment, a fuzzy logic-based approach is designed to select appropriate cluster heads and hop-nodes in a distributed manner. The experimental results prove the effectiveness and accuracy of our proposed model and applicability to large scale WSNs. [ABSTRACT FROM PUBLISHER]