Biblio

With the wide application of Internet technology in the industrial control field, industrial control networks are getting larger and larger, and the industrial data generated by industrial control systems are increasing dramatically, and the performance requirements of the acquisition and storage systems are getting higher and higher. The collection and analysis of industrial equipment work logs and industrial timing data can realize comprehensive management and continuous monitoring of industrial control system work status, as well as intrusion detection and energy efficiency analysis in terms of traffic and data. In the face of increasingly large realtime industrial data, existing log collection systems and timing data gateways, such as packet loss and other phenomena [1], can not be more complete preservation of industrial control network thermal data. The emergence of software-defined networking provides a new solution to realize massive thermal data collection in industrial control networks. This paper proposes a 10-gigabit industrial thermal data acquisition and storage scheme based on software-defined networking, which uses software-defined networking technology to solve the problem of insufficient performance of existing gateways.

The student's fault is not the only cause of dropping out of school. Often, cases of dropping out of school are only associated with too general problems. However, sensitive issues that can be detrimental to certain parties in this regard, such as the institution's reputation, are usually not made public. To overcome this, an in-depth analysis of these cases is needed for proper handling. Many risks are associated with creating a single repository for this sensitive information. Therefore, some encryption is required to ensure data is not leaked. However, encryption at rest and in transit is insufficient as data leakage is a considerable risk during processing. In addition, there is also a risk of abuse of authority by insiders so that no single entity is allowed to have access to all data. Homomorphic encryption presents a viable solution to this challenge. Data may be aggregated under the security provided by Homomorphic Encryption. This method makes the data available for computation without being decrypted first and without paying the risk of having a single repository.

Research on intelligent accounting system based on intelligent financial data sheet analysis system considering complex data mining is conducted in the paper. The expert audit system extracts business records from the business database according to the specified audit conditions, and the program automatically calculates the total amount of the amount data items, and then compares it with the standard or normal business, reflecting the necessary information such as differences and also possible audit trails. In order to find intrusion behaviors and traces, data collection is carried out from multiple points in the network system. The collection content includes system logs, network data packets, important files, and the status and the behavior of the user activities. Furthermore, complex data mining model is combined for the systematic analysis on the system performance. The simulation on the collected data is provided to the validate the performance.

As the nature of the website, the EJBCA digital signatures may have vulnerabilities. The list of web-based vulnerabilities can be found in OWASP's Top 10 2021. Anticipating the attack with an effective and efficient forensics application is necessary. The concept of digital forensic readiness can be applied as a pre-incident plan with a digital forensic lifecycle pipeline to establish an efficient forensic process. Managing digital evidence in the pre-incident plan includes data collection, examination, analysis, and findings report. Based on this concept, we implemented it in designing an information system that carries out the entire flow, provides attack evidence collection, visualization of attack statistics in executive summary, mitigation recommendation, and forensic report generation in a physical form when needed. This research offers an information system that can help the digital forensic process and maintain the integrity of the EJBCA digital signature server web.

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.

National cultural security has existed since ancient times, but it has become a focal proposition in the context of the times and real needs. From the perspective of national security, national cultural security is an important part of national security, and it has become a strategic task that cannot be ignored in defending national security. Cultural diversity and imbalance are the fundamental prerequisites for the existence of national cultural security. Finally, the artificial intelligence algorithm is used as the theoretical basis for this article, the connotation and characteristics of China's national cultural security theory; Xi Jinping's "network view"; network ideological security view. The fourth part is the analysis of the current cultural security problems, hazards and their root causes in our country.

Virtual power plants are among the promising ways that variable generation and flexible demand may be optimally balanced in the future. The virtual power plant is an important branch of the energy internet, and it plays an important role in the aggregation of distributed power generation resources and the establishment of virtual power resource transactions. However, in the existing virtual power plant model, the following problems are becoming increasingly prominent, such as safeguard, credit rating system, privacy protection, benefit distribution. Firstly, the operation and transaction mechanism of the virtual power plant was introduced. Then, the blockchain technology is introduced into the virtual power plant transaction to make it more conducive to the information transparent, stable dispatch system, data security, and storage security. Finally, the operation and transaction system based on blockchain technology for the virtual power plant was design.

User authentication based on muscle tension manifested during password typing seems to be an interesting additional layer of security. It represents another way of verifying a person’s identity, for example in the context of continuous verification. In order to explore the possibilities of such authentication method, it was necessary to create a capturing software that records and stores data from EMG (electromyography) sensors, enabling a subsequent analysis of the recorded data to verify the relevance of the method. The work presented here is devoted to the design, implementation and evaluation of such a solution. The solution consists of a protocol and a software application for collecting multimodal data when typing on a keyboard. Myo armbands on both forearms are used to capture EMG and inertial data while additional modalities are collected from a keyboard and a camera. The user experience evaluation of the solution is presented, too.

Domestic robots and agents are widely sold to the grand public, leading us to ethical issues related to the data harvested by such machines. While users show a general acceptance of these robots, concerns remain when it comes to information security and privacy. Current research indicates that there’s a privacy-security trade-off for better use, but the anthropomorphic and social abilities of a robot are also known to modulate its acceptance and use. To explore and deepen what literature already brought on the subject we examined how users perceived their robot (Replika, Roomba©, Amazon Echo©, Google Home©, or Cozmo©/Vector©) through an online questionnaire exploring acceptance, perceived privacy and security, anthropomorphism, disclosure, perceived intimacy, and loneliness. The results supported the literature regarding the potential manipulative effects of robot’s anthropomorphism for acceptance but also information disclosure, perceived intimacy, security, and privacy.

Connected devices are being deployed at a steady rate, providing services like data collection. Pervasive applications rely on those edge devices to seamlessly provide services to users. To connect applications and edge devices, using a middleware has been a popular approach. The research is active on the subject as there are many open challenges. The secure management of the edge devices and the security of the middleware are two of them. As security is a crucial requirement for pervasive environment, we propose a middleware architecture easing the secure use of edge devices for pervasive applications, while supporting the heterogeneity of communication protocols and the dynamism of devices. Because of the heterogeneity in protocols and security features, not all edge devices are equally secure. To allow the pervasive applications to gain control over this heterogeneous security, we propose a model to describe edge devices security. This model is accessible by the applications through our middleware. To validate our work, we developed a demonstrator of our middleware and we tested it in a concrete scenario.

With the widespread deployment of data-driven services, the demand for data volumes continues to grow. At present, many applications lack reliable human supervision in the process of data collection, which makes the collected data contain low-quality data or even malicious data. This low-quality or malicious data make AI systems potentially face much security challenges. One of the main security threats in the training phase of machine learning is data poisoning attacks, which compromise model integrity by contaminating training data to make the resulting model skewed or unusable. This paper reviews the relevant researches on data poisoning attacks in various task environments: first, the classification of attacks is summarized, then the defense methods of data poisoning attacks are sorted out, and finally, the possible research directions in the prospect.

In the recent years, we have witnessed quite notable cyber-attacks targeting industrial automation control systems. Upgrading their cyber security is a challenge, not only due to long equipment lifetimes and legacy protocols originally designed to run in air-gapped networks. Even where multiple data sources are available and collection established, data interpretation usable across the different data sources remains a challenge. A modern hydro power plant contains the data sources that range from the classical distributed control systems to newer IoT- based data sources, embedded directly within the plant equipment and deeply integrated in the process. Even abundant collected data does not solve the security problems by itself. The interpretation of data semantics is limited as the data is effectively siloed. In this paper, the relevance of semantic integration of diverse data sources is presented in the context of a hydro power plant. The proposed semantic integration would increase the data interoperability, unlocking the data siloes and thus allowing ingestion of complementary data sources. The principal target of the data interoperability is to support the data-enhanced cyber security in an operational hydro power plant context. Furthermore, the opening of the data siloes would enable additional usage of the existing data sources in a structured semantically enriched form.

Many organizations process and store classified data within their computer networks. Owing to the value of data that they hold; such organizations are more vulnerable to targets from adversaries. Accordingly, the sensitive organizations resort to an ‘air-gap’ approach on their networks, to ensure better protection. However, despite the physical and logical isolation, the attackers have successfully manifested their capabilities by compromising such networks; examples of Stuxnet and Agent.btz in view. Such attacks were possible due to the successful manipulation of human beings. It has been observed that to build up such attacks, persistent reconnaissance of the employees, and their data collection often forms the first step. With the rapid integration of social media into our daily lives, the prospects for data-seekers through that platform are higher. The inherent risks and vulnerabilities of social networking sites/apps have cultivated a rich environment for foreign adversaries to cherry-pick personal information and carry out successful profiling of employees assigned with sensitive appointments. With further targeted social engineering techniques against the identified employees and their families, attackers extract more and more relevant data to make an intelligent picture. Finally, all the information is fused to design their further sophisticated attacks against the air-gapped facility for data pilferage. In this regard, the success of the adversaries in harvesting the personal information of the victims largely depends upon the common errors committed by legitimate users while on duty, in transit, and after their retreat. Such errors would keep on repeating unless these are aligned with their underlying human behaviors and weaknesses, and the requisite mitigation framework is worked out.

Vehicular crowdsensing (VCS) is a subset of crowd-sensing where data collection is outsourced to group vehicles. Here, an entity interested in collecting data from a set of Places of Sensing Interest (PsI), advertises a set of sensing tasks, and the associated rewards. Vehicles attracted by the offered rewards deviate from their ongoing trajectories to visit and collect from one or more PsI. In this win-to-win scenario, vehicles reach their final destination with the extra reward, and the entity obtains the desired samples. Unfortunately, the efficiency of VCS can be undermined by the Sybil attack, in which an attacker can benefit from the injection of false vehicle identities. In this paper, we present a case study and analyze the effects of such an attack. We also propose a defense mechanism based on generative adversarial neural networks (GANs). We discuss GANs' advantages, and drawbacks in the context of VCS, and new trends in GANs' training that make them suitable for VCS.

The security of Energy Data collection is the basis of achieving reliability and security intelligent of smart grid. The newest security communication of Data collection is Zero Trust communication; The Strategy of Zero Trust communication is that don’t trust any device of outside or inside. Only that device authenticate is successful and software and hardware is more security, the Energy intelligent power system allow the device enroll into network system, otherwise deny these devices. When the device has been communicating with the Energy system, the Zero Trust still need to detect its security and vulnerability, if device have any security issue or vulnerability issue, the Zero Trust deny from network system, it ensures that Energy power system absolute security, which lays a foundation for the security analysis of intelligent power unit.

Identity resolution system is the primary technical research problem to set up the data collection capability of industrial internet, and the configuration resolution of complex assets is an application difficulty. To implement the particular requirements of complex equipment configuration management, an industry-oriented identity resolution architecture and the configuration resolution service were designed. In accordance with the technical information management of high-speed train, corresponding handle structures was proposed to describe the configuration structure and related components information of EMU (Electric Multiple Unit). A distributed processing algorithm for configuration resolution and the hit-ratio evaluation method of handle service sites was proposed. The performance, stability, and resolution consistency of the handle system in this paper are proved by experiments, which is also great significant to the intelligent identity applications in other industries.

Underwater acoustic sensor networks (UASNs) have been receiving more and more attention due to their wide applications and the marine data collection is one of the important applications of UASNs. However, the openness and unreliability of underwater acoustic communication links and the easy capture of underwater wireless devices make UASNs vulnerable to various attacks. On the other hand, due to the limited resources of underwater acoustic network nodes, the high bit error rates, large and variable propagation delays, and low bandwidth of acoustic channels, many mature security mechanisms in terrestrial wireless sensor networks cannot be applied in the underwater environment [1]. In this paper, a secure communication protocol for marine data collection was proposed to ensure the confidentiality and data integrity of communication between under sensor nodes and the sink node in UASNs.

Thousands of people have lost their life by COVID-19 infection. Authorities have seen the calamities caused by the corona virus in China. So, when the trace of virus was found in India, the only possible way to stop the spread of the virus was to go into lockdown. In a country like India where a major part of the population depends on the daily wages, being in lockdown started affecting their life. People where tend to go out for getting the food items and other essentials, and this caused the spread of virus. Many were infected and many lost their life by this. Due to the pandemic, the whole world was affected and many people working in foreign countries lost their jobs as well. These people who came back to India caused further spread of the virus. The main reason for the spread is lack of hygiene and a proper system to monitor the symptoms. Even though our country was in lockdown for almost 6 months the number of COVID cases doesn't get diminished. It is not practical to extend the lockdown any further, and people have decided to live with the virus. But it is essential to take the necessary precautions while interacting with the society. Automated system for checking that all the COVID protocols are followed and early symptom identification before entering to a place are essential to stop the spread of the infection. This research work proposes a smart door system, which evaluates the COVID-19 risk factors and collects the data of person before entering into any place, thereby ensuring that non-infected people are only entering to the place and thus the spread of virus can be avoided.

In this information age, web crawling on the internet is a prime source for data collection. And with the surface web already being dominated by giants like Google and Microsoft, much attention has been on the Dark Web. While research on crawling approaches is generally available, a considerable gap is present for URL extraction on the dark web. With most literature using the regular expressions methodology or built-in parsers, the problem with these methods is the higher number of false positives generated with the Dark Web, which makes the crawler less efficient. This paper proposes the dedicated parsers methodology for extracting URLs from the dark web, which when compared proves to be better than the regular expression methodology. Factors that make link harvesting on the Dark Web a challenge are discussed in the paper.

As it is easy to ensure the confidentiality of users on the Dark Web, malware and exploit kits are sold on the market, and attack methods are discussed in forums. Some services provide IoT Botnet to perform distributed denial-of-service (DDoS as a Service: DaaS), and it is speculated that the purchase of these services is made on the Dark Web. By crawling such information and storing it in a database, threat intelligence can be obtained that cannot otherwise be obtained from information on the Surface Web. However, crawling sites on the Dark Web present technical challenges. For this paper, we implemented a crawler that can solve these challenges. We also collected information on markets and forums on the Dark Web by operating the implemented crawler. Results confirmed that the dataset collected by crawling contains threat intelligence that is useful for analyzing cyber attacks, particularly those related to IoT Botnet and DaaS. Moreover, by uncovering the relationship with security reports, we demonstrated that the use of data collected from the Dark Web can provide more extensive threat intelligence than using information collected only on the Surface Web.

The dark web searching mechanism is unlike surface web searching. On one popular dark web, Tor dark web, the search is often directed by directory like services such as Hidden Wiki. The numerous dark web data collection mechanisms are discussed and implemented via crawling. The dark web crawler assumes seed link, i.e. hidden service from where the crawling begins. One such popular Tor directory service is Hidden Wiki. Most of the hidden services listed on the Hidden Wiki 2020 page became unreachable with the recent upgrade in the Tor version. The Hidden Wiki 2021 page has a limited listing of services compared to the Hidden Wiki 2020 page. This motivated authors of the present work to establish the role of Hidden wiki service in dark web research and proposed the hypothesis that the dark web could be reached better through customized harvested links than Hidden Wiki-like service. The work collects unique hidden services/ onion links using the opensource crawler TorBot and runs similarity analysis on collected pages to map to corresponding categories.

With the development of technology, the structure of power grid becomes more and more complex, and the amount of data collected is also increasing. In the existing smart power grid, the data collected by sensors need to be uploaded and stored to the trusted central node, but the centralized storage method is easy to cause the malicious attack of the central node, resulting in single point failure, data tampering and other security problems. In order to solve these information security problems, this paper proposes a new data security storage framework based on private blockchain. By using the improved raft algorithm, partial decentralized data storage is used instead of traditional centralized storage. It also introduces in detail the working mechanism of the smart grid data security storage framework, including the process of uploading collected data, data verification, and data block consensus. The security analysis shows the effectiveness of the proposed data storage framework.

Anomaly detection is the primary method of detecting intrusion. Unsupervised models, such as auto-encoders network, auto-encoder, and GMM, are currently the most widely used anomaly detection techniques. In reality, the samples used to train the unsupervised model may not be pure enough and may include some abnormal samples. However, the classification effect is poor since these approaches do not completely understand the association between reconstruction errors, reconstruction characteristics, and irregular sample density distribution. This paper proposes a novel intrusion detection system architecture that includes data collection, processing, and feature extraction by integrating data reconstruction features, reconstruction errors, auto-encoder parameters, and GMM. Our system outperforms other unsupervised learning-based detection approaches in terms of accuracy, recall, F1-score, and other assessment metrics after training and testing on multiple intrusion detection data sets.

As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response-time, power dissipation and cost goals of performance-critical applications in various domains like Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on a edge platform. In the implementation part we show the benefits of FPGA-based platform for the task of object detection. Furthermore we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.