Due to the existence of uncertain factors such as the power grid system itself, natural climate change and human factors, various faults will still occur in the power grid system. If the fault alarm is not responded to in time, it is likely to cause grid instability or even collapse, resulting in inestimable losses. By building a knowledge graph for massive power grid operation and maintenance information, we can achieve fast and accurate fault information reasoning and traceability, and retrieve reasonable fault resolution measures. Use artificial intelligence technology and big data to assist power grid systems to achieve more efficient operation and maintenance. Realizing the intelligent fault diagnosis of power grid is an urgent problem to be solved at present. With the rapid development and application of artificial intelligence technology, if artificial intelligence and big data technology can be applied to the fault diagnosis and analysis of power grids, this situation of relying on manual analysis will be broken, and the efficient processing of massive operation and maintenance data will be realized.
With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining $W$ ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a $W$ ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.
The increase in Social Engineering (SE) attacks during COVID-19 pandemic has made it imperative to educate people about SE techniques and methods. For the last many years, we have worked on games, which disseminate awareness among the participants about Social Engineering concepts. The aim of this study is to share our newly designed card-based game, which is simple to understand, and can be conducted in classroom environment.
Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.
Detecting code vulnerabilities is a crucial part in secure software development. Many static analysis tools have been proven useful in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some work incorporates the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents vulnerability nets, which are a special Petri net that integrates with data dependence graphs and control flow graphs. Specifically, the proposed approach is intended for detecting taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. In this paper, the construction and use of vulnerability nets are discussed in detail. Furthermore, we show the feasibility by presenting a case study in analyzing an example adapted from a real-world case.
Aiming at the cross-chain problems faced by financial transactions, study the cross-chain communication protocols of the financial-oriented autonomous panda model and golden monkey model, and study the construction of a new scalable and credible multi-chain model that supports homogeneous blockchains and heterogeneous blockchains. The models and protocols that support financial transactions in the blockchain environment need to be able to meet the SSL or SET security protocols similar to traditional Internet transactions, and meet various requirements such as transaction integrity, reliability, and privacy protection.
Future Industrial 4.0 applications in the 6G era is calling for high dependability that goes far beyond the current ultra-reliable low latency communication (URLLC), and therewith proposed critical challenges to the communication technology. Instead of struggling against the physical and technical limits towards an extreme URLLC (xURLLC), communication-control co-design (CoCoCo) appears a more promising solution. This work proposes a novel framework of CoCoCo, which is not only enhancing the dependability of 6G industrial applications such as remote control, but also exhibiting rich potential in revolutionizing the future industry per openness and flexibility of manufacturing systems.
With the importance of wireless telecommunication services increasing in our daily lives and society operation, the demand for reliable telecommunication networks rapidly grows. However, in contrast to the high reliability of the core telecom systems, the base stations have low redundancy and high failure rate, which lead to a bottleneck in the reliability improvement of the telecommunication network. To figure out the characteristic of the reliability of the base station network, we analyze the compensation mechanism upon the failures of the base station cell by practice data. The results show that the loss of demands caused by base station failures can be compensated by two types of resilience: intrasector resilience and inter-sector resilience. Intra-sector resilience describes the demands absorption occurred between co-sector cells in a base station. Inter-sector resilience is manifested in the demand absorption by the neighbor base stations. We observe these two types of resilience in practical cases and analyze their compensation effects. The results of this paper show that resilience widely exists in base station network and act as an important factor in ensuring the reliability of telecommunication network.
Auctions are particularly favored as a means of financial transaction for high-value commodities. The Turing-complete script provided by the Ethereum platform facilitates different types of auctions. However, the transparency of Ethereum may lead to threats to the privacy of financial transactions, which is also the biggest obstacle to the practical application of blockchain. This paper proposes an auction contract privacy protection method named TrustAuction, which protects the transaction data privacy and user identity privacy in the auction process through the Trusted Execution Environment(TEE). Specifically, TEE is used to generate new identities for users in the auction process, and the bids submitted by users using the new identities exist are encrypted outside the TEE.
With the wide application of blockchain technology, the user privacy information involved in Ethereum is increasingly rich. But with the introduction of de-anonymization technologies, more and more attacks may lead to the disclosure of user privacy information. In this paper, as a user privacy protection approach, RandomMix is proposed based on account mixing and transaction value encryption. We conduct experiments on the real transactions on Ethereum and find out that RandomMix can resist account clustering attack, transaction fingerprint analysis attack and Sybil attack. Meanwhile, RandomMix consume less gas compared with other approaches.
With the continuous development and innovation of blockchain technology, a large number of blockchain networks with various features have emerged to satisfy the needs of differ-ent scenarios. For example, Cross-Chain technologies have been proposed to enable information interaction and value transfer between two blockchains. In Cross-Chain asset transaction, the Hashed Timelock Contract (HTLC for short) algorithm is the most widely used to control asset transaction. However, the HTLC algorithm has a problem of low-level security assurance in practical applications: Attackers can associate the accounts of both parties involved in a Cross-Chain transaction by comparing contract parameters on different chains, which leads to the disclosure of identity privacy information of both relevant parties in the Cross-Chain transactions. In order to protect the identity privacy information of Cross-Chain traders, a new privacy protection method for Cross-Chain asset transaction was proposed based on Paillier homomorphic encryption (PTLC for short) in this paper, which uses Paillier homomorphic encryption to replace hash encryption in privacy protection process. It not only maintains a high security of Cross-Chain asset transaction, but also solves the major problem of identity privacy information leakage during whole asset transaction process. Experiments of cross-chain asset transactions are conducted in two different Ethereum private chains, and experimental results show that the proposed method PTLC can protect the identity privacy information effectively and achieve a low GAS consumption value in the Cross-Chain process.
Cross-site scripting attacks, as a means of attack against Web applications, are widely used in phishing, information theft and other fields by unscrupulous people because of their wide targeting and hidden implementation methods. Nevertheless, cross-site scripting vulnerability detection is still in its infancy, with plenty of challenges not yet fully explored. In this paper, we propose Crawler-based Cross Site Scripting Detector, a tool based on crawler technology that can effectively detect stored Cross Site Scripting vulnerabilities and reflected Cross Site Scripting vulnerabilities. Subsequently, in order to verify the effectiveness of the tool, we experim ented this tool with existing tools such as XSSer and Burp Suite by selecting 100 vulnerable websites for the tool's efficiency, false alarm rate and underreporting rate. The results show that our tool can effectively detect Cross Site Scripting vulnerabilities.
The online broadcast scene is a novel scenario in culture and tourism industry in new period. However, the online broadcast control system has not been solved at the technical level. This research describes a collaborative smart control system for seizing actors and scenarios in broadcast online scene based on drama script with pattern recognition. Based on the script of the cultural and artistic forms under the online broadcast scenario, the training parameters of the pattern recognition algorithm are set to recognize the real-time capture of the face, dress, clothing, etc., and the tracking and overall setting control. The kernel algorithm is set with smart decision making system based for seizing actors and clustering algorithm (discrimination analysis) for seizing different scenarios on stage. Connections between equipment serving for the entire broadcasting drama and online presentations are achieved by the wireless network, software and kernel control system.
Interpretation has been successfully applied to static analysis, by computing over-approximation of the concrete semantics of various program for many years. However, in the context of program verification, abstract interpretation is not apt to generate counter-examples when the property does not hold. Dynamic analysis is known for its ability to generate inputs to find program vulnerabilities. In this paper, we propose an method that uses fuzzing to help abstract interpretation based program verification, especially to help generating inputs that violate the target property. During the verification process, we feed the fuzzer with the necessary precondition of violating the target assertion computed by abstract interpretation, and then run the fuzzer to generate inputs satisfying the necessary precondition but violating the target assertion. The result shows promising ability of our approach in generating counter-example for target property in comparison with other state-of-the-art tools.
This paper proposes a mapping method of 2D image and 3D spatial data based on the adversarial relative depth constraint network. The steps are as follows: 1) Input pixel coordinates of key nodes of 2D landscape image, and conduct normalization preprocessing; 2) Input two-dimensional pixel coordinates into the depth prediction network and output the depth values of key nodes; 3) Using depth values and two-dimensional pixel coordinates to reconstruct three-dimensional coordinates of key nodes; 4) Input DEM data to the discriminator of the generated adversarial network to calculate the authenticity error value, and use the relative depth information between the attitude characteristics of mountain and hydrology and the corresponding key nodes of the image to calculate the relative depth error; 5) Add the authenticity error and relative depth error calculated above to get the total error, and feed back to the depth prediction network to get a more accurate mapping evaluation, so as to realize mapping discovery. The problems solved in this paper include: lack of characteristic pose data in the traditional geo-evidence-based process of 2D landscape images; The results of the generative adversarial network method do not conform to the relative depth relationship of feature points in 3D spatial data.
This paper introduces a natural language-enabled virtual assistant (VA), called Max, developed to enhance humanrobot interaction (HRI) with industrial robots. Regardless of the numerous natural language interfaces already available for commercial use and social robots, most VAs remain tightly bound to a specific robotic system. Besides, they lack a natural and efficient human-robot communication protocol to advance the user experience and the required robustness for use on the industrial floor. Therefore, the proposed framework is designed based on three key elements. A Client-Server style architecture that provides a centralised solution for managing and controlling various types of robots deployed on the shop floor. A communication protocol inspired by human-human conversation strategies, i.e., lexical-semantic strategy and general diversion strategy, is used to guide Max's response generation. These conversation strategies are embedded in Max's architecture to improve the engagement of the operators during the execution of industrial tasks. Finally, the state-of-the-art pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), is fine-tuned to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted for validating Max's performance in a real industrial environment.
In this paper, we propose an analytical model that can analyze the impact of emergencies on open source software (OSS) development. As the core of this model, a metric system is used to comprehensively describe the OSS development process, which includes three dimensions: team activity, development activity, and development risk, with a total of 30 metrics. To demonstrate the effectiveness of the model, we construct an empirical study analyzing the impact of COVID-19 on OSS development. This study is based on the development process events between January 2019 and April 2022 belonging to 50 selected open source projects on GitHub. The results show that more than 72.4% of projects were negatively impacted following the COVID-19 outbreak. Interestingly, we observe that variants of covide-19 did not exacerbate its impact on software development. On the contrary, some project development activities have obviously resumed, indicating that the development team has adapted and gradually got rid of the impact of the epidemic.
Rust is an emerging programming language designed for both performance and security, and thus many research efforts have been conducted recently to migrate legacy code bases in C/C++ to Rust to exploit Rust's safety benefits. Unfortunately, prior studies on C to Rust conversion still have three limitations: 1) complex structure; 2) code explosion; and 3) poor performance. These limitations greatly affect the effectiveness and usefulness of such conversions. This paper presents Rusty, the first system for effective C to Rust code conversion via unstructured control specialization. The key technical insight of Rusty is to implement C-style syntactic sugars on top of Rust, thus eliminating the discrepancies between the two languages. We have implemented a software prototype for Rusty and conducted experiments to evaluate the effectiveness and testify the usefulness of it by applying Rusty to micro-benchmarks, as well as 3 real-world C projects: 1) Vim; 2) cURL; and 3) the silver searcher. And experimental results demonstrated that Rusty is effective in eliminating unstructured controls, reducing the code size by 16% on average with acceptable overhead (less than 61 microseconds per line of C code).
We consider a destination port logistics service provider (DPLSP), which wants to improve its service quality by reducing risk of delivery time delay. This paper diagnoses potential risk factors that estimate the performances of the DPLSP who provides services only after the arrival of freight, with the intention of reducing supply chain risk and improve supply chain performance through creative computing approach. Self-organizing feature map (SOFM) computing is a type of artificial neural network based on an unsupervised learning algorithm. We propose the approach of SOFM computing for the purpose of clustering risk data of DPLSPs from a less subjective perspective and then rank the cluster results into different levels based on the total risk value of each cluster. Numerical studies to test the effectiveness of this model would be carried out using air import logistics lead- time reports from a large DPLSP. The results illustrate that the proposed approach could successfully cluster and rank the risk data according to their values.
Log analysis is quite significant for reliability issues in large cloud data centers. There are noticeable problems in log anomaly detection, such as single feature extraction, unsatisfactory anomaly detection effect. In this paper, we propose a novel log anomaly detection method, which could be divided into two related parts. First, a dataset partitioning method is proposed, named K-fold Sub Hold-out Method (KSHM), which is built on the features of logs to preserve the temporality of training data when sampling. KSHM could enhance the effectiveness of sampling without increasing the number of samples, and change the way the model is trained. Second, an anomaly detection model based on hybrid Transformer-BiLSTM (TFBL) is well constructed, which could extract both temporal and semantic features of logs to serve as a source of features for comprehensive anomaly detection. Experiment results show that TFBL outperforms baseline methods in assessment criteria of accuracy, precision and F1-score, and our log anomaly detection method based on integrated KSHM and TFBL also has better anomaly detection performence.
