Biblio

Transformer is the key equipment of power system, and its stable operation is very important to the security of power system In practical application, with the progress of technology, the performance of transformer becomes more and more important, but faults also occur from time to time in practical application, and the traditional manual fault diagnosis needs to consume a lot of time and energy. At present, the rapid development of artificial intelligence technology provides a new research direction for timely and accurate detection and treatment of transformer faults. In this paper, a method of transformer fault diagnosis using artificial neural network is proposed. The neural network algorithm is used for off-line learning and training of the operation state data of normal and fault states. By adjusting the relationship between neuron nodes, the mapping relationship between fault characteristics and fault location is established by using network layer learning, Finally, the reasoning process from fault feature to fault location is realized to realize intelligent fault diagnosis.

The development of industrial robots, as a carrier of artificial intelligence, has played an important role in promoting the popularisation of artificial intelligence super automation technology. The paper introduces the system structure, hardware structure, and software system of the mobile robot climber based on computer big data technology, based on this research background. At the same time, the paper focuses on the climber robot's mechanism compound method and obstacle avoidance control algorithm. Smart home computing focuses on “home” and brings together related peripheral industries to promote smart home services such as smart appliances, home entertainment, home health care, and security monitoring in order to create a safe, secure, energy-efficient, sustainable, and comfortable residential living environment. It's been twenty years. There is still no clear definition of “intelligence at home,” according to Philips Inc., a leading consumer electronics manufacturer, which once stated that intelligence should comprise sensing, connectedness, learning, adaption, and ease of interaction. S mart applications and services are still in the early stages of development, and not all of them can yet exhibit these five intelligent traits.

Several excellent devices can communicate without the need for human intervention. It is one of the fastest-growing sectors in the history of computing, with an estimated 50 billion devices sold by the end of 2020. On the one hand, IoT developments play a crucial role in upgrading a few simple, intelligent applications that can increase living quality. On the other hand, the security concerns have been noted to the cross-cutting idea of frameworks and the multidisciplinary components connected with their organization. As a result, encryption, validation, access control, network security, and application security initiatives for gadgets and their inherent flaws cannot be implemented. It should upgrade existing security measures to ensure that the ML environment is sufficiently protected. Machine learning (ML) has advanced tremendously in the last few years. Machine insight has evolved from a research center curiosity to a sensible instrument in a few critical applications.

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

In recent years, the detection of illegal and harmful messages which plays an significant role in Internet service is highly valued by the government and society. Although artificial intelligence technology is increasingly applied to actual operating systems, it is still a big challenge to be applied to systems that require high real-time performance. This paper provides a real-time detection system solution based on artificial intelligence technology. We first introduce the background of real-time detection of illegal and harmful messages. Second, we propose a complete set of intelligent detection system schemes for real-time detection, and conduct technical exploration and innovation in the media classification process including detection model optimization, traffic monitoring and automatic configuration algorithm. Finally, we carry out corresponding performance verification.

The model called CSAI-4-CPS is proposed to characterize the use of Artificial Intelligence in Cybersecurity applied to the context of CPS - Cyber-Physical Systems. The model aims to establish a methodology being able to self-adapt using shared machine learning models, without incurring the loss of data privacy. The model will be implemented in a generic framework, to assess accuracy across different datasets, taking advantage of the federated learning and machine learning approach. The proposed solution can facilitate the construction of new AI cybersecurity tools and systems for CPS, enabling a better assessment and increasing the level of security/robustness of these systems more efficiently.

Cyberspace is the fifth largest activity space after land, sea, air and space. Safeguarding Cyberspace Security is a major issue related to national security, national sovereignty and the legitimate rights and interests of the people. With the rapid development of artificial intelligence technology and its application in various fields, cyberspace security is facing new challenges. How to help the network security personnel grasp the security trend at any time, help the network security monitoring personnel respond to the alarm information quickly, and facilitate the tracking and processing of the monitoring personnel. This paper introduces a method of using situational awareness micro application actual combat attack and defense robot to quickly feed back the network attack information to the monitoring personnel, timely report the attack information to the information reporting platform and automatically block the malicious IP.

In today's society, with the continuous development of artificial intelligence, artificial intelligence technology plays an increasingly important role in social and economic development, and hass become the fastest growing, most widely used and most influential high-tech in the world today one. However, at the same time, information technology has also brought threats to network security to the entire network world, which makes information systems also face huge and severe challenges, which will affect the stability and development of society to a certain extent. Therefore, comprehensive analysis and research on information system security is a very necessary and urgent task. Through the security assessment of the information system, we can discover the key hidden dangers and loopholes that are hidden in the information source or potentially threaten user data and confidential files, so as to effectively prevent these risks from occurring and provide effective solutions; at the same time To a certain extent, prevent virus invasion, malicious program attacks and network hackers' intrusive behaviors. This article adopts the experimental analysis method to explore how to apply the most practical, advanced and efficient artificial intelligence theory to the information system security assessment management, so as to further realize the optimal design of the information system security assessment management system, which will protect our country the information security has very important meaning and practical value. According to the research results, the function of the experimental test system is complete and available, and the security is good, which can meet the requirements of multi-user operation for security evaluation of the information system.

Edge computing is a prospective notion for expanding the potential of cloud computing. It is vital to maintaining a decent atmosphere free of all forms of security and breaches in order to continue utilizing computer services. The security concerns surrounding the edge computing environment has been impeded as a result of the security issues that surround the area. Many researchers have looked into edge computing security issues, however, not all have thoroughly studied the needs. Security requirements are the goals that specify the capabilities and operations that a process that is carried out by a system in order to eliminate various security flaws. The purpose of this study is to give a complete overview of the many different artificial intelligence technologies that are now being utilized for edge computing security with the intention of aiding research in the future in locating research potential. This article analyzed the most recent research and shed light on the following topics: state-of-the-art techniques used to combat security threats, technological trends used by the method, metrics utilize to assess the techniques' ability, and opportunities of research for future researchers in the area of artificial intelligence for edge computing security.

In the digital era, cyber security has become a serious problem. Information penetrates, wholesale fraud, manual human test breaking, and other comparable occurrences proliferate, influencing a large number of individuals just as organizations. The hindrances have consistently been endless in creating appropriate controls and procedures and putting them in place with utmost precision in order to deal with cyber-attacks. To recent developments in artificial intelligence, the danger of cyber - attacks has increased drastically. AI has affected everything from healthcare to robots. Because malicious hackers couldn't keep this ball of fire from them, ``normal'' cyber-attacks have grown in to the ``intelligent'' cyber attacks. In this paper, The most promising artificial intelligence approaches are discussed. Researchers look at how such techniques may be used for cyber security. At last, the conversation concludes with a discussion about artificial intelligence's future and cyber security.

This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.

The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data - vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.

Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.

With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.

Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.

In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.

Cybersecurity is becoming very crucial in the today's world where technology is now not limited to just computers, smartphones, etc. It is slowly entering into things that are used on daily basis like home appliances, automobiles, etc. Thus, opening a new door for people with wrong intent. With the increase in speed of technology dealing with such issues also requires quick response from security people. Thus, dealing with huge variety of devices quickly will require some extent of automation in this field. Generating threat intelligence automatically and also including those which are multilingual will also add plus point to prevent well known major attacks. Here we are proposing an AI based SOAR system in which the data from various sources like firewalls, IDS, etc. is collected with individual event profiling using a deep-learning detection method. For this the very first step is that the collected data from different sources will be converted into a standardized format i.e. to categorize the data collected from different sources. For standardized format Here our system finds out about the true positive alert for which the appropriate/ needful steps will be taken such as the generation of Indicators of Compromise report and the additional evidences with the help of Security Information and Event Management system. The security alerts will be notified to the security teams with the degree of threat.

In order to overcome a series of problems in the application process of traditional communication engineering in the new era, such as information security, this paper proposes a novel communication engineering application system based on artificial intelligence technology. The application system fully combines the artificial intelligence technology, and applies the artificial intelligence thinking to the reform of traditional communication engineering. Based on this, the application strategy also fully combines the application and development of 5g technology, and strengthens the security of communication engineering in the application process from many aspects. The results show that the application system can give full play to the role of artificial intelligence technology and improve the security of communication process as much as possible, which lays a good foundation for the further development of 5g technology.

This paper analyzes the necessity of artificial intelligence and big data in the security application of regulatory places. The author studies the specific application of artificial intelligence and big data in ideological dynamics management, access control system, video surveillance system, emergency alarm system, perimeter control system, police inspection system, daily behavior management, and system implementation management. The author puts forward how to do technical integration, improve information sharing, strengthen the construction of talents, and increase management fund expenditure. The purpose of this paper is to enhance the security management level of regulatory places and optimize the management environment of regulatory places.

This paper combines the common ideas and methods in offensive and defensive confrontation in recent years, and uses artificial intelligence technology-based network asset automatic mining technology and artificial intelligence technology-based vulnerability automatic exploitation technology, carries out research and specific practices in discovering and using system vulnerability based on artificial intelligence technology, designs and implemented automatic binary vulnerability discovering and exploitation system, which improves improves the efficiency and success rate of vulnerability discovering and exploitation.

As a current disruptive and transformative technology, artificial intelligence is constantly infiltrating all aspects of production and life. However, with the in-depth development and application of artificial intelligence, the security challenges it faces have become more and more prominent. In the real world, attacks against intelligent systems such as the Internet of Things, smart homes, and driverless cars are constantly appearing, and incidents of artificial intelligence being used in cyber-attacks and cybercrimes frequently occur. This article aims to discuss artificial intelligence security issues and propose some countermeasures.

Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.

Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments' strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.

Artificial intelligence has been widely used in industries such as smart manufacturing, medical care and home furnishings. Among them, the value of the application in communication security is very important. This paper makes a further exploration of the artificial intelligence technology and its application, and gives a detailed analysis of its development, standardization and the application.

Our research team have devoted to extract internal malicious behavior by monitoring the network traffic for many years. We applied the deep learning approach to recognize the malicious patterns within network, but this methodology may lead to more works to examine the results from AI models production. Hence, this paper addressed the scenario to consider the burden of AI, and proposed an idea for long-term reliable detection in the future work.