Biblio

A direct access (DAX) file system maximizes the benefit of persistent memory(PM)’s low latency through removing the page cache layer from the file system access paths. However, this paper reveals that data block allocation of the DAX file systems in common is significantly slower than that of conventional file systems because the DAX file systems require the zero-out operation for the newly allocated blocks to prevent the leakage of old data previously stored in the allocated data blocks. The retarded block allocation significantly affects the file write performance. In addition to this revelation, this paper proposes an off-critical-path data block sanitization scheme tailored for DAX file systems. The proposed scheme detaches the zero-out operation from the latency-critical I/O path and performs that of released data blocks in the background. The proposed scheme’s design principle is universally applicable to most DAX file systems. For evaluation, we implemented our approach in Ext4-DAX and XFS-DAX. Our evaluation showed that the proposed scheme reduces the append write latency by 36.8%, and improved the performance of FileBench’s fileserver workload by 30.4%, YCSB’s workload A on RocksDB by 3.3%, and the Redis-benchmark by 7.4% on average, respectively.

In recent years, generative adversarial networks (GAN) have become a research hotspot in the field of deep learning. Researchers apply them to the field of anomaly detection and are committed to effectively and accurately identifying abnormal images in practical applications. In anomaly detection, traditional supervised learning algorithms have limitations in training with a large number of known labeled samples. Therefore, the anomaly detection model of unsupervised learning GAN is the research object for discussion and research. Firstly, the basic principles of GAN are introduced. Secondly, several typical GAN-based anomaly detection models are sorted out in detail. Then by comparing the similarities and differences of each derivative model, discuss and summarize their respective advantages, limitations and application scenarios. Finally, the problems and challenges faced by GAN in anomaly detection are discussed, and future research directions are prospected.

This article analyzes the analysis of the joint data security architecture that integrates artificial intelligence and cloud computing in the era of big data. The article discusses and analyzes the integrated applications of big data, artificial intelligence and cloud computing. As an important part of big data security protection, joint data security Protecting the technical architecture is not only related to the security of joint data in the big data era, but also has an important impact on the overall development of the data era. Based on this, the thesis takes the big data security and joint data security protection technical architecture as the research content, and through a simple explanation of big data security, it then conducts detailed research on the big data security and joint data security protection technical architecture from five aspects and thinking.

{Health diseases have been issued seriously harmful in human life due to different dehydrated food and disturbance of working environment in the organization. Precise prediction and diagnosis of disease become a more serious and challenging task for primary deterrence, recognition, and treatment. Thus, based on the above challenges, we proposed the Medical Things (MT) and machine learning models to solve the healthcare problems with appropriate services in disease supervising, forecast, and diagnosis. We developed a prediction framework with machine learning approaches to get different categories of classification for predicted disease. The framework is designed by the fuzzy model with a decision tree to lessen the data complexity. We considered heart disease for experiments and experimental evaluation determined the prediction for categories of classification. The number of decision trees (M) with samples (MS), leaf node (ML), and learning rate (I) is determined as MS=20

In the prevailing situation, the sports like economic, industrial, cultural, social, and governmental activities are carried out in the online world. Today's international is particularly dependent on the wireless era and protective these statistics from cyber-assaults is a hard hassle. The reason for cyber-assaults is to damage thieve the credentials. In a few other cases, cyber-attacks ought to have a navy or political functions. The damages are PC viruses, facts break, DDS, and exceptional attack vectors. To this surrender, various companies use diverse answers to prevent harm because of cyberattacks. Cyber safety follows actual-time data at the modern-day-day IT data. So, far, numerous techniques have proposed with the resource of researchers around the area to prevent cyber-attacks or lessen the harm due to them. The cause of this has a look at is to survey and comprehensively evaluate the usual advances supplied around cyber safety and to analyse the traumatic situations, weaknesses, and strengths of the proposed techniques. Different sorts of attacks are taken into consideration in element. In addition, evaluation of various cyber-attacks had been finished through the platform called Kali Linux. It is predicted that the complete assessment has a have a study furnished for college students, teachers, IT, and cyber safety researchers might be beneficial.

Dynamic Host Control Protocol (DHCP) is a protocol which provides IP addresses and network configuration parameters to the hosts present in the network. This protocol is deployed in small, medium, and large size organizations which removes the burden from network administrator to manually assign network parameters to every host in the network for establishing communication. Every vendor who plans to incorporate DHCP service in its device follows the working flow defined in Request for Comments (RFC). DHCP Starvation and DHCP Flooding attack are Denial of Service (DoS) attacks to prevents provision of IP addresses by DHCP. Port Security and DHCP snooping are built-in security features which prevents these DoS attacks. However, novel techniques have been devised to bypass these security features which uses ARP and ICMP protocol to perform the attack. The purpose of this research is to analyze implementation of DHCP in multiple devices to verify the involvement of both ARP and ICMP in the address acquisition process of DHCP as per RFC and to validate the results of prior research which assumes ARP or ICMP are used by default in all of devices.

Aim: Data is secured in the cloud using Elliptic Curve Cryptography (ECC) compared with Advanced Encryption Standard (AES) with improved time efficiency. Materials and Methods: Encryption and decryption time is performed with files stored in the cloud. Protecting data with improved time efficiency is carried out using ECC where the number of samples (\textbackslashmathrmN=6) and AES (\textbackslashmathrmN=6), obtained using the G-power value of 80%. Results: Mean time of ECC is 0.1683 and RSA is 0.7517. Significant value for the proposed system is 0.643 (\textbackslashmathrmp \textgreater 0.05). Conclusion: Within the limit of study, ECC performs faster in less consumption time when compared to AES.

In order to solve the problems that may arise from the negative impact of EV charging loads on the power distribution network, it is very important to predict the distribution network variability according to EV charging loads. If appropriate facility reinforcement or system operation is made through evaluation of the impact of EV charging load, it will be possible to prevent facility failure in advance and maintain the power quality at a certain level, enabling stable network operation. By analysing the degree of change in the predicted load according to the EV load characteristics through the load prediction model, it is possible to evaluate the influence of the distribution network according to the EV linkage. This paper aims to investigate the effect of EV charging load on voltage stability, power loss, reliability index and economic loss of distribution network. For this, we transformed univariate time series of EV charging data into a multivariate time series using feature engineering techniques. Then, time series forecast models are trained based on the multivariate dataset. Finally, XAI techniques such as LIME and SHAP are applied to the models to obtain the feature importance analysis results.

Design a new generation of smart power meter components, build a smart power network, implement power meter safety protection, and complete smart power meter network security protection. The new generation of smart electric energy meters mainly complete legal measurement, safety fee control, communication, control, calculation, monitoring, etc. The smart power utilization structure network consists of the master station server, front-end processor, cryptographic machine and master station to form a master station management system. Through data collection and analysis, the establishment of intelligent energy dispatching operation, provides effective energy-saving policy algorithms and strategies, and realizes energy-smart electricity use manage. The safety protection architecture of the electric energy meter is designed from the aspects of its own safety, full-scenario application safety, and safety management. Own security protection consists of hardware security protection and software security protection. The full-scene application security protection system includes four parts: boundary security, data security, password security, and security monitoring. Security management mainly provides application security management strategies and security responsibility division strategies. The construction of the intelligent electric energy meter network system lays the foundation for network security protection.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.

Promising means of detecting small UAVs are FMCW radar systems. Small UAVs with an RCS value of the order of 10−3••• 10−1m2 are characterized by a low SNR (less than 10 dB). To ensure an acceptable probability of detection in the resolution element (more than 0.9), it becomes necessary to reduce the detection threshold. However, this leads to a significant increase in the probability of false alarms (more than 10−3) and is accompanied by the appearance of a large number of false plots. The work describes an algorithm for trajectory detecting of a small UAV based on a “l/n-d” criterion using Kalman filtering in a spherical coordinate system due to FMCW radar data. Statistical analysis of algorithms based on two types of criteria “3/5-2” and “5/9-2” is performed. It is shown that the algorithms allow to achieve the probability of target trajectory detection greater than 0.9 and low probability of false detection of the target trajectory less than 10−4 with the false alarm probability in the resolution element 10−3••• 10−2•

A “tripartite and bilateral” dynamic game model was constructed to study the impact of space deterrence on the challenger's military strategy in a military conflict. Based on the signal game theory, the payment matrices and optimal strategies of the sheltering side and challenging side were analyzed. In a theoretical framework, the indicators of the effectiveness of the challenger's response to space deterrence and the influencing factors of the sheltering's space deterrence were examined. The feasibility and effective means for the challenger to respond to the space deterrent in a “tripartite and bilateral” military conflict were concluded.

Artificial intelligence is a subfield of computer science that refers to the intelligence displayed by machines or software. The research has influenced the rapid development of smart devices that have a significant impact on our daily lives. Science, engineering, business, and medicine have all improved their prediction powers in order to make our lives easier in our daily tasks. The quality and efficiency of regions that use artificial intelligence has improved, as shown in this study. It successfully handles data organisation and environment difficulties, allowing for the development of a more solid and rigorous model. The pace of life is quickening in the digital age, and the PC Internet falls well short of meeting people’s needs. Users want to be able to get convenient network information services at any time and from any location

The new paradigm of industrial development, called Industry 4.0, faces the problems of Cybersecurity, and as it has already manifested itself in Information Systems, focuses on the use of Artificial Intelligence tools. The authors of this article build on their experience with the use of the above mentioned tools to increase the resilience of Information Systems against Cyber threats, approached to the choice of an effective structure of Cyber-protection of Industrial Systems, primarily analyzing the objective differences between them and Information Systems. A number of analyzes show increased resilience of the decentralized architecture in the management of large-scale industrial processes to the centralized management architecture. These considerations provide sufficient grounds for the team of the project to give preference to the decentralized structure with flock behavior for further research and experiments. The challenges are to determine the indicators which serve to assess and compare the impacts on the controlled elements.

Data Analytics is at the core of almost all modern ap-plications ranging from science and finance to healthcare and web applications. The evolution of data analytics over the last decade has been dramatic - new methods, new tools and new platforms - with no slowdown in sight. This rapid evolution has pushed the boundaries of data analytics along several axis including scalability especially with the rise of distributed infrastructures and the Big Data era, and interoperability with diverse data management systems such as relational databases, Hadoop and Spark. However, many analytic application developers struggle with the challenge of production deployment. Recent experience suggests that it is difficult to deliver modern data analytics with the level of reliability, security and manageability that has been a feature of traditional SQL DBMSs. In this tutorial, we discuss the advances and innovations introduced at both the infrastructure and algorithmic levels, directed at making analytic workloads scale, while paying close attention to the kind of quality of service guarantees different technology provide. We start with an overview of the classical centralized analytical techniques, describing the shift towards distributed analytics over non-SQL infrastructures. We contrast such approaches with systems that integrate analytic functionality inside, above or adjacent to SQL engines. We also explore how Cloud platforms' virtualization capabilities make it easier - and cheaper - for end users to apply these new analytic techniques to their data. Finally, we conclude with the learned lessons and a vision for the near future.

Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like-even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate network views of the text(s), that we then can use for vulnerability mapping, risk assessments and note control point analysis. For proof of concept we draw on NIST conceptual model and analysis of guidelines for smart grid cybersecurity, more than 600 pages of text.

Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

In recent years, new types of cyber attacks called targeted attacks have been observed. It targets specific organizations or individuals, while usual large-scale attacks do not focus on specific targets. Organizations have published many Word or PDF files on their websites. These files may provide the starting point for targeted attacks if they include hidden data unintentionally generated in the authoring process. Adhatarao and Lauradoux analyzed hidden data found in the PDF files published by security agencies in many countries and showed that many PDF files potentially leak information like author names, details on the information system and computer architecture. In this study, we analyze hidden data of PDF files published on the website of police agencies in Japan and compare the results with Adhatarao and Lauradoux's. We gathered 110989 PDF files. 56% of gathered PDF files contain personal names, organization names, usernames, or numbers that seem to be IDs within the organizations. 96% of PDF files contain software names.

In control systems, the operation of the system after an incident occurs is important. This paper proposes to design a whitelist model that can detect anomalies and identify locations of anomalous actuators using finite automata during multiple actuators attack. By applying this model and comparing the whitelist model with the operation data, the monitoring system detects anomalies and identifies anomaly locations of actuator that deviate from normal operation. We propose to construct a whitelist model focusing on the order of the control system operation using binary search trees, which can grasp the state of the system when anomalies occur. We also apply combinatorial compression based on BDD (Binary Decision Diagram) to the model to speed up querying and identification of abnormalities. Based on the model designed in this study, we aim to construct a secured control system that selects and executes an appropriate fallback operation based on the state of the system when anomaly is detected.

Industrial Control Systems (ICS) are increasingly facing the threat of False Data Injection (FDI) attacks. As an emerging intrusion detection scheme for ICS, process-based Intrusion Detection Systems (IDS) can effectively detect the anomalies caused by FDI attacks. Specifically, such IDS establishes anomaly detection model which can describe the normal pattern of industrial processes, then perform real-time anomaly detection on industrial process data. However, this method suffers low detection accuracy due to the complexity and instability of industrial processes. That is, the process data inherently contains sophisticated nonlinear spatial-temporal correlations which are hard to be explicitly described by anomaly detection model. In addition, the noise and disturbance in process data prevent the IDS from distinguishing the real anomaly events. In this paper, we propose an Anomaly Detection approach based on Robust Spatial-temporal Modeling (AD-RoSM). Concretely, to explicitly describe the spatial-temporal correlations within the process data, a neural based state estimation model is proposed by utilizing 1D CNN for temporal modeling and multi-head self attention mechanism for spatial modeling. To perform robust anomaly detection in the presence of noise and disturbance, a composite anomaly discrimination model is designed so that the outputs of the state estimation model can be analyzed with a combination of threshold strategy and entropy-based strategy. We conducted extensive experiments on two benchmark ICS security datasets to demonstrate the effectiveness of our approach.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

In today’s world, digital data are enormous due to technologies that advance data collection, storage, and analyses. As more data are shared or publicly available, privacy is of great concern. Having privacy means having control over your data. The first step towards privacy protection is to understand various aspects of privacy and have the ability to quantify them. Much work in structured data, however, has focused on approaches to transforming the original data into a more anonymous form (via generalization and suppression) while preserving the data integrity. Such anonymization techniques count data instances of each set of distinct attribute values of interest to signify the required anonymity to protect an individual’s identity or confidential data. While this serves the purpose, our research takes an alternative approach to provide quick privacy measures by way of anonymity especially when dealing with large-scale data. This paper presents a study of anonymity measures based on their relevant properties that impact privacy. Specifically, we identify three properties: uniformity, variety, and diversity, and formulate their measures. The paper provides illustrated examples to evaluate their validity and discusses the use of multi-aspects of anonymity and privacy measures.

As one of the effective methods to enhance traffic safety and improve traffic efficiency, the Internet of vehicles has attracted wide attention from all walks of life. V2X secure communication, as one of the research hotspots of the Internet of vehicles, also has many security and privacy problems. Attackers can use these vulnerabilities to obtain vehicle identity information and location information, and can also attack vehicles through camouflage.Therefore, the identity authentication process in vehicle network communication must be effectively protected. The anonymous identity authentication scheme based on moving target defense proposed in this paper not only ensures the authenticity and integrity of information sources, but also avoids the disclosure of vehicle identity information.