Biblio

Ensuring high data availability while realizing privacy protection is a research hotspot in the field of privacy-preserving data publishing. In view of the instability of data availability in the existing differential privacy high-dimensional data publishing methods based on Bayesian networks, this paper proposes an improved MEPrivBayes privacy-preserving data publishing method, which is mainly improved from two aspects. Firstly, in view of the structural instability caused by the random selection of Bayesian first nodes, this paper proposes a method of first node selection and Bayesian network construction based on the Maximum Information Coefficient Matrix. Then, this paper proposes a privacy budget elastic allocation algorithm: on the basis of pre-setting differential privacy budget coefficients for all branch nodes and all leaf nodes in Bayesian network, the influence of branch nodes on their child nodes and the average correlation degree between leaf nodes and all other nodes are calculated, then get a privacy budget strategy. The SVM multi-classifier is constructed with privacy preserving data as training data set, and the original data set is used as input to evaluate the prediction accuracy in this paper. The experimental results show that the MEPrivBayes method proposed in this paper has higher data availability than the classical PrivBayes method. Especially when the privacy budget is small (noise is large), the availability of the data published by MEPrivBayes decreases less.

The Zero Trust Architecture is an important part of the industrial Internet security protection standard. When analyzing industrial data for enterprise-level or industry-level applications, differential privacy (DP) is an important technology for protecting user privacy. However, the centralized and local DP used widely nowadays are only applicable to the networks with fixed trust relationship and cannot cope with the dynamic security boundaries in Zero Trust Architecture. In this paper, we design a differential privacy scheme that can be applied to Zero Trust Architecture. It has a consistent privacy representation and the same noise mechanism in centralized and local DP scenarios, and can balance the strength of privacy protection and the flexibility of privacy mechanisms. We verify the algorithm in the experiment, that using maximum expectation estimation method it is able to obtain equal or even better result of the utility with the same level of security as traditional methods.

Dielectric capacitors have attracted attention as energy storage devices that can achieve rapid charge and discharge. But the key to restricting its development is the low energy storage density of dielectric materials. Polyvinylidene fluoride (PVDF), as a polymer with high dielectric properties, is expected to improve the energy storage density of dielectric materials. In this work, the multilayer structure of PVDF ferroelectric polymer is designed, and the influence of the number of layers on the maximum polarization, remanent polarization, applied electric field and energy storage density of the dielectric material is studied. The final obtained double-layer PVDF obtained a discharge energy storage density of 10.6 J/cm3 and an efficiency of 49.1% at an electric field of 410 kV/mm; the three-layer PVDF obtained a discharge energy storage density of 11.0 J/cm3 and an efficiency of 37.2% at an electric field of 440 kV/mm.

In this paper, stock selection strategy design based on machine learning and multi-factor analysis is a research hotspot in quantitative investment field. Four machine learning algorithms including support vector machine, gradient lifting regression, random forest and linear regression are used to predict the rise and fall of stocks by taking stock fundamentals as input variables. The portfolio strategy is constructed on this basis. Finally, the stock selection strategy is further optimized. The empirical results show that the multifactor quantitative stock selection strategy has a good stock selection effect, and yield performance under the support vector machine algorithm is the best. With the increase of the number of factors, there is an inverse relationship between the fitting degree and the yield under various algorithms.

The outages and power shortages are common occurrences in today's world and they have a significant economic impact. These failures can be minimized by making the power grid topologically robust. Therefore, the vulnerability assessment in power systems has become a major concern. This paper considers both pure and extended topological method to analyse the vulnerability of the power system to single line failures. The lines are ranked based on four spectral graph metrics: spectral radius, algebraic connectivity, natural connectivity, and effective graph resistance. A correlation is established between all the four metrics. The impact of load uncertainty on the component ranking has been investigated. The vulnerability assessment has been done on IEEE 9-bus system. It is observed that load variation has minor impact on the ranking.

In the differential privacy clustering algorithm, the added random noise causes the clustering centroids to be shifted, which affects the usability of the clustering results. To address this problem, we design a differential privacy K-means clustering algorithm based on an adaptive allocation of privacy budget to the clustering effect: Adaptive Differential Privacy K-means (ADPK-means). The method is based on the evaluation results generated at the end of each iteration in the clustering algorithm. First, it dynamically evaluates the effect of the clustered sets at the end of each iteration by measuring the separation and tightness between the clustered sets. Then, the evaluation results are introduced into the process of privacy budget allocation by weighting the traditional privacy budget allocation. Finally, different privacy budgets are assigned to different sets of clusters in the iteration to achieve the purpose of adaptively adding perturbation noise to each set. In this paper, both theoretical and experimental results are analyzed, and the results show that the algorithm satisfies e-differential privacy and achieves better results in terms of the availability of clustering results for the three standard datasets.

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

When users request location services, they are easy to expose their privacy information, and the scheme of using a third-party server for location privacy protection has high requirements for the credibility of the server. To solve these problems, a localized differential privacy protection scheme in mobile environment is proposed, which uses Markov chain model to generate probability transition matrix, and adds Laplace noise to construct a location confusion function that meets differential privacy, Conduct location confusion on the client, construct and upload anonymous areas. Through the analysis of simulation experiments, the scheme can solve the problem of untrusted third-party server, and has high efficiency while ensuring the high availability of the generated anonymous area.

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management.

Most of the recent high-profile attacks targeting cyber-physical systems (CPS) started with lengthy reconnaissance periods that enabled attackers to gain in-depth understanding of the victim’s environment. To simulate these stealthy attacks, several covert channel tools have been published and proven effective in their ability to blend into existing CPS communication streams and have the capability for data exfiltration and command injection.In this paper, we report a novel machine learning feature engineering and data processing pipeline for the detection of covert channel attacks on CPS systems with real-time detection throughput. The system also operates at the network layer without requiring physical system domain-specific state modeling, such as voltage levels in a power generation system. We not only demonstrate the effectiveness of using TCP payload entropy as engineered features and the technique of grouping information into network flows, but also pitch the proposed detector against scenarios employing advanced evasion tactics, and still achieve above 99% detection performance.

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Software vulnerabilities threaten the security of computer system, and recently more and more loopholes have been discovered and disclosed. For the detected vulnerabilities, the relevant personnel will analyze the vulnerability characteristics, and combine the vulnerability scoring system to determine their severity level, so as to determine which vulnerabilities need to be dealt with first. In recent years, some characteristic description-based methods have been used to predict the severity level of vulnerability. However, the traditional text processing methods only grasp the superficial meaning of the text and ignore the important contextual information in the text. Therefore, this paper proposes an innovative method, called BERT-CNN, which combines the specific task layer of Bert with CNN to capture important contextual information in the text. First, we use Bert to process the vulnerability description and other information, including Access Gained, Attack Origin and Authentication Required, to generate the feature vectors. Then these feature vectors of vulnerabilities and their severity levels are input into a CNN network, and the parameters of the CNN are gotten. Next, the fine-tuned Bert and the trained CNN are used to predict the severity level of a vulnerability. The results show that our method outperforms the state-of-the-art method with 91.31% on F1-score.

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.

In the field of airport passenger security, a new type of security inspection equipment called intelligent passenger security equipment is applied widely, which can significantly improve the efficiency of airport security screening and passenger satisfaction. This paper establishes a security check channel model based on intelligent passenger security check equipment, and studies the factors affecting the efficiency of airport security screening, such as the number of baggage unloading points, baggage loading points, secondary inspection points, etc. A simulation model of security check channel is established based on data from existing intelligent passenger security check equipment and data collected from Beijing Daxing Airport. Equipment utilization and queue length data is obtained by running the simulation model. According to the data, the bottleneck is that the manual inspection process takes too long, and the utilization rate of the baggage unloading point is too low. For the bottleneck link, an optimization scheme is proposed. With more manual check points and secondary inspection points and less baggage unloading points, the efficiency of airport security screening significantly increases by running simulation model. Based on the optimized model, the effect of baggage unloading point and baggage loading point on efficiency is further studied. The optimal parameter configuration scheme under the expected efficiency is obtained. This research can assist engineers to find appropriate equipment configuration quickly and instruct the airport to optimize the arrangement of security staff, which can effectively improve the efficiency of airport security screening and reduce the operating costs of airport.

This paper addresses the allocation method of offensive resources for man-made attacks on power systems considering extreme weather conditions, which can help the defender identify the most vulnerable components to protect in this adverse situation. The problem is formulated as an attacker-defender model. The attacker at the upper level intends to maximize the expected damage considering all possible line failure scenarios. These scenarios are characterized by the combinations of failed transmission lines under extreme weather. Once the disruption is detected, the defender at the lower level alters the generation and consumption in the power grid using DC optimal power flow technique to minimize the damage. Then the original bi-level problem is transformed into an equivalent single-level mixed-integer linear program through strong duality theorem and Big-M method. The proposed attack resource allocation method is applied on IEEE 39-bus system and its effectiveness is demonstrated by the comparative case studies.

As the effects of climate change are becoming more and more evident, the importance of improved situation awareness is also gaining more attention, both in the context of preventive environmental monitoring and in the context of acute crisis response. One important aspect of situation awareness is the correct and thorough monitoring of air pollutants. The monitoring is threatened by sensor faults, power or network failures, or other hazards leading to missing or incorrect data transmission. For this reason, in this work we propose two complementary approaches for predicting missing sensor data and a combined technique for detecting outliers. The proposed solution can enhance the performance of low-cost sensor systems, closing the gap of missing measurements due to network unavailability, detecting drift and outliers thus paving the way to its use as an alert system for reportable events. The techniques have been deployed and tested also in a low power microcontroller environment, verifying the suitability of such a computing power to perform the inference locally, leading the way to an edge implementation of a virtual sensor digital twin.

Low-Power and Lossy Networks (LLNs) run on resource-constrained devices and play a key role in many Industrial Internet of Things and Cyber-Physical Systems based applications. But, achieving an energy-efficient routing in LLNs is a major challenge nowadays. This challenge is addressed by Routing Protocol for Low-power Lossy Networks (RPL), which is specified in RFC 6550 as a “Proposed Standard” at present. In RPL, a client node uses Destination Advertisement Object (DAO) control messages to pass on the destination information towards the root node. An attacker may exploit the DAO sending mechanism of RPL to perform a DAO Insider attack in LLNs. In this paper, it is shown that an aggressive attacker can drastically degrade the network performance. To address DAO Insider attack, a lightweight defense solution is proposed. The proposed solution uses an early blacklisting strategy to significantly mitigate the attack and restore RPL performance. The proposed solution is implemented and tested on Cooja Simulator.

The Internet of Things (IoT) is a novel paradigm that enables the development of a slew of Services for the future of technology advancements. When it comes to IoT applications, the cyber and physical worlds can be seamlessly integrated, but they are essentially limitless. However, despite the great efforts of standardization bodies, coalitions, companies, researchers, and others, there are still a slew of issues to overcome in order to fully realize the IoT's promise. These concerns should be examined from a variety of perspectives, including enabling technology, applications, business models, and social and environmental consequences. The focus of this paper is on open concerns and challenges from a technological standpoint. We will study the differences in technical such Sigfox, NB-IoT, LoRa, and 6LowPAN, and discuss their advantages and disadvantage for each technology compared with other technologies. Demonstrate that each technology has a position in the internet of things market. Each technology has different advantages and disadvantages it depends on the quality of services, latency, and battery life as a mention. The first will be analysis IoT technologies. SigFox technology offers a long-range, low-power, low-throughput communications network that is remarkably resistant to environmental interference, enabling information to be used efficiently in a wide variety of applications. We analyze how NB-IoT technology will benefit higher-value-added services markets for IoT devices that are willing to pay for exceptionally low latency and high service quality. The LoRa technology will be used as a low-cost device, as it has a very long-range (high coverage).

Consensus is a basic building block in distributed systems for a myriad of related problems that involve agreement. For asynchronous networks, consensus has been proven impossible, and is well known as Augean task. Failure Detectors (FDs) have since emerged as a possible remedy, able to solve consensus in asynchronous systems under certain assumptions. With the increasing use of asynchronous, wireless Internet of Things (IoT) technologies, such as IEEE 802.15.4/6LoWPAN, the demand of applications that require some form of reliability and agreement is on the rise. What was missing so far is an FD that can operate under the tight constraints offered by Low Power and Lossy Networks (LLNs) without compromising the efficiency of the network. We present 6LoFD, an FD specifically aimed at energy and memory efficient operation in small scale, unreliable networks, and evaluate its working principles by using an ns-3 implementation of 6LoFD.

In the IoT (Internet of Things) domain, it is still a challenge to modify the routing behavior of IoT traffic at the decentralized backbone network. In this paper, centralized and flexible software-defined networking (SDN) is utilized to route the IoT traffic. The management of IoT data transmission through the SDN core network gives the chance to choose the path with the lowest delay, minimum packet loss, or hops. Therefore, fault-tolerant delay awareness routing is proposed for the emulated SDN-based backbone network to handle delay-sensitive IoT traffic. Besides, the hybrid form of GNS3 and Mininet-WiFi emulation is introduced to collaborate the SDN-based backbone network in GNS3 and the 6LoWPAN (IPv6 over Low Power Personal Area Network) sensor network in Mininet-WiFi.

Black-box adversarial attack has aroused much research attention for its difficulty on nearly no available information of the attacked model and the additional constraint on the query budget. A common way to improve attack efficiency is to transfer the gradient information of a white-box substitute model trained on an extra dataset. In this paper, we deal with a more practical setting where a pre-trained white-box model with network parameters is provided without extra training data. To solve the model mismatch problem between the white-box and black-box models, we propose a novel algorithm EigenBA by systematically integrating gradient-based white-box method and zeroth-order optimization in black-box methods. We theoretically show the optimal directions of perturbations for each step are closely related to the right singular vectors of the Jacobian matrix of the pretrained white-box model. Extensive experiments on ImageNet, CIFAR-10 and WebVision show that EigenBA can consistently and significantly outperform state-of-the-art baselines in terms of success rate and attack efficiency.

Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano’s inequality) to derive upper bounds on the success rate of any possible attack in terms of the number of side-channel measurements. Equivalently, this gives lower bounds on the number of queries for a given success probability of attack. In this paper, we consider cryptographic implementations protected by (first-order) masking schemes, and derive several information-theoretic bounds on the efficiency of any (second-order) attack. The obtained bounds are generic in that they do not depend on a specific attack but only on the leakage and masking models, through the mutual information between side-channel measurements and the secret key. Numerical evaluations confirm that our bounds reflect the practical performance of optimal maximum likelihood attacks.

Increasing connectivity and automation in vehicles leads to a greater potential attack surface. Such vulnerabilities within vehicles can also be used for auto-theft, increasing the potential for attackers to disable anti-theft mechanisms implemented by vehicle manufacturers. We utilize patterns derived from Controller Area Network (CAN) bus traffic to verify driver “behavior”, as a basis to prevent vehicle theft. Our proposed model uses semi-supervised learning that continuously profiles a driver, using features extracted from CAN bus traffic. We have selected 15 key features and obtained an accuracy of 99% using a dataset comprising a total of 51 features across 10 different drivers. We use a number of data analysis algorithms, such as J48, Random Forest, JRip and clustering, using 94K records. Our results show that J48 is the best performing algorithm in terms of training and testing (1.95 seconds and 0.44 seconds recorded, respectively). We also analyze the effect of using a sliding window on algorithm performance, altering the size of the window to identify the impact on prediction accuracy.

Vehicular networks are vulnerable to large scale attacks. Blockchain, implemented upon application layer, is recommended as one of the effective security and privacy solutions for vehicular networks. However, due to an increasing complexity of connected nodes, heterogeneous environment and rising threats, a robust security solution across multiple layers is required. Motivated by the Physical Layer Security (PLS) which utilizes physical layer characteristics such as channel fading to ensure reliable and confidential transmission, in this paper we analyze the impact of PLS on a blockchain-enabled vehicular network with two types of physical layer attacks, i.e., jamming and eavesdropping. Throughout the analysis, a Full Duplex Non-Orthogonal Multiple Access (FD-NOMA) based vehicle-to-everything (V2X) is considered to reduce interference caused by jamming and meet 5G communication requirements. Simulation results show enhanced goodput of a blockckchain enabled vehicular network integrated with PLS as compared to the same solution without PLS.

Intelligent Environments (IEs) enrich the physical world by connecting it to software applications in order to increase user comfort, safety and efficiency. IEs are often supported by wireless networks of smart sensors and actuators, which offer multi-year battery life within small packages. However, existing radio mesh networks suffer from high latency, which precludes their use in many user interface systems such as real-time speech, touch or positioning. While recent advances in optical networks promise low end-to-end latency through symbol-synchronous transmission, current approaches are power hungry and therefore cannot be battery powered. We tackle this problem by introducing BoboLink, a mesh network that delivers low-power and low-latency optical networking through a combination of symbol-synchronous transmission and a novel wake-up technology. BoboLink delivers mesh-wide wake-up in 1.13ms, with a quiescent power consumption of 237µW. This enables building-wide human computer interfaces to be seamlessly delivered using wireless mesh networks for the first time.