Biblio

Artificial technology developed in recent years. It is an intelligent system that can perform tasks without human intervention. AI can be used for various purposes, such as speech recognition, face recognition, etc. AI can be used for good or bad purposes, depending on how it is implemented. The discuss the application of AI in data security technology and its advantages over traditional security methods. We will focus on the good use of AI by analyzing the impact of AI on the development of big data security technology. AI can be used to enhance security technology by using machine learning algorithms, which can analyze large amounts of data and identify patterns that cannot be detected automatically by humans. The computer big data security technology platform based on artificial intelligence in this paper is the process of creating a system that can identify and prevent malicious programs. The system must be able to detect all types of threats, including viruses, worms, Trojans and spyware. It should also be able to monitor network activity and respond quickly in the event of an attack.

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

Artificial Intelligence (AI) technology is developing rapidly, permeating every aspect of human life. Although the integration between AI and communication contributes to the flourishing development of wireless communication, it induces severer security problems. As a supplement to the upper-layer cryptography protocol, physical layer security has become an intriguing technology to ensure the security of wireless communication systems. However, most of the current physical layer security research does not consider the intelligence and mobility of collusive eavesdroppers. In this paper, we consider a MIMO system model with a friendly intelligent jammer against multiple collusive intelligent eavesdroppers, and zero-sum game is exploited to formulate the confrontation of them. The Nash equilibrium is derived by convex optimization and alternative optimization in the free-space scenario of a single user system. We propose a zero-sum game deep learning algorithm (ZGDL) for general situations to solve non-convex game problems. In terms of the effectiveness, simulations are conducted to confirm that the proposed algorithm can obtain the Nash equilibrium.

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.

With the increasing number of distributed energy sources and the growing demand for free exchange of energy, Energy internet (EI) is confronted with great challenges of persistent connection, stable transmission, real-time interaction, and security. The new definition of metaverse in the EI field is proposed as a potential solution for these challenges by establishing a massive and comprehensive fusion 3D network, which can be considered as the advanced stage of EI. The main characteristics of the metaverse such as reality to virtualization, interaction, persistence, and immersion are introduced. Specifically, we present the key enabling technologies of the metaverse including virtual reality, artificial intelligence, blockchain, and digital twin. Meanwhile, the potential applications are presented from the perspectives of immersive user experience, virtual power station, management, energy trading, new business, device maintenance. Finally, some challenges of metaverse in EI are concluded.

Metaverse opens up a new social networking paradigm where people can experience a real interactive feeling without physical space constraints. Social interactions are gradually evolving from text combined with pictures and videos to 3-dimensional virtual reality, making the social experience increasingly physical, implying that more metaverse applications with immersive experiences will be developed in the future. However, the increasing data dimensionality and volume for new metaverse applications present a significant challenge in data acquisition, security, and sharing. Furthermore, metaverse applications require high capacity and ultrareliability for the wireless system to guarantee the quality of user experience, which cannot be addressed in the current fifth-generation system. Therefore, reaching the metaverse is dependent on the revolution in the sixth-generation (6G) wireless communication, which is expected to provide low-latency, high-throughput, and secure services. This article provides a comprehensive view of metaverse applications and investigates the fundamental technologies for the 6G toward metaverse.

This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.

The advancement of modern multimedia and data-intensive classes of applications demands the development of hardware that delivers better performance. Due to the evolution of 5G, Edge-Computing, the Internet of Things, Software-Defined networks, etc., the data produced by the devices such as sensors are increasing. A software-Defined network is a powerful paradigm that is capable of automating networking and cloud computing. Software-Defined Network has controllers, devices, and applications which produce a huge amount of data. The processing of data inside the device as well as between the devices needs a better hardware architecture with more cores to ensure speedy performance. The System-on-Chip approach alone will not be capable to handle this dense core comprised of hardware. We have to blend Network-on-Chip along with System-on-Chip to increase the potential to include more cores capable to handle more threads. Artificial Intelligence, a key enabler in next-generation devices is capable of producing a better architecture design with optimized performance. In this paper, we are discussing and endeavouring how System-on-Chip, Network-on-Chip, Software-Defined Networks, and Artificial Intelligence can be physically, logically, and contextually incorporated to deliver improved computation and networking outcomes.

With the rapid development of artificial intelligence technology, deepfake technology based on deep learning is receiving more and more attention from society or the industry. While enriching people's cultural and entertainment life, in-depth fakes technology has also caused many social problems, especially potential risks to managing network credible identities. With the continuous advancement of deep fakes technology, the security threats and trust crisis caused by it will become more serious. It is urgent to take adequate measures to curb the abuse risk of deep fakes. The article first introduces the principles and characteristics of deep fakes technology and then deeply analyzes its severe challenges to network trusted identity management. Finally, it researches the supervision and technical level and puts forward targeted preventive countermeasures.

With the progress of cryptography computer science, designing cryptographic algorithms using deep learning is a very innovative research direction. Google Brain designed a communication model using generation adversarial network and explored the encrypted communication algorithm based on machine learning. However, the encrypted communication model it designed lacks quantitative evaluation. When some plaintexts and keys are leaked at the same time, the security of communication cannot be guaranteed. This model is optimized to enhance the security by adjusting the optimizer, modifying the activation function, and increasing batch normalization to improve communication speed of optimization. Experiments were performed on 16 bits and 64 bits plaintexts communication. With plaintext and key leak rate of 0.75, the decryption error rate of the decryptor is 0.01 and the attacker can't guess any valid information about the communication.

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.

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

Archival services are one of the main functions of an information security management system for archival management, and the conversion and updating of archival intelligence services is an important means to meet the increasing diversity and wisdom of the age of intelligence. The purpose of this paper is to study an information security management system for archival management based on embedded artificial intelligence. The implementation of an embedded control management system for intelligent filing cabinets is studied. Based on a configurable embedded system security model, the access control process and the functional modules of the system based on a secure call cache are analysed. Software for wireless RF communication was designed, and two remote control options were designed using CAN technology and wireless RF technology. Tests have shown that the system is easy to use, feature-rich and reliable, and can meet the needs of different users for regular control of file room management.

At present, cloud service providers control the direct management rights of cloud data, and cloud data cannot be effectively and assured deleted, which may easily lead to security problems such as data residue and user privacy leakage. This paper analyzes the related research work of cloud data assured deletion in recent years from three aspects: encryption key deletion, multi-replica association deletion, and verifiable deletion. The advantages and disadvantages of various deletion schemes are analysed in detail, and finally the prospect of future research on assured deletion of cloud data is given.

Steady advancement in Artificial Intelligence (AI) development over recent years has caused AI systems to become more readily adopted across industry and military use-cases globally. As powerful as these algorithms are, there are still gaping questions regarding their security and reliability. Beyond adversarial machine learning, software supply chain vulnerabilities and model backdoor injection exploits are emerging as potential threats to the physical safety of AI reliant CPS such as autonomous vehicles. In this work in progress paper, we introduce the concept of AI supply chain vulnerabilities with a provided proof of concept autonomous exploitation framework. We investigate the viability of algorithm backdoors and software third party library dependencies for applicability into modern AI attack kill chains. We leverage an autonomous vehicle case study for demonstrating the applicability of our offensive methodologies within a realistic AI CPS operating environment.

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients’ needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as “chatbots”, are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13–14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients’ needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients.

Vulnerability assessment is an important process for network security. However, most commonly used vulnerability assessment methods still rely on expert experience or rule-based automated scripts, which are difficult to meet the security requirements of increasingly complex network environment. In recent years, although scientists and engineers have made great progress on artificial intelligence in both theory and practice, it is a challenging to manufacture a mature high-quality intelligent products in the field of network security, especially in penetration testing based vulnerability assessment for enterprises. Therefore, in order to realize the intelligent penetration testing, Vul.AI with its rich experience in cyber attack and defense for many years has designed and developed a set of intelligent penetration and attack simulation system Ai.Scan, which is based on attack chain, knowledge graph and related evaluation algorithms. In this paper, the realization principle, main functions and application scenarios of Ai.Scan are introduced in detail.

The latest, modern security camera systems record numerous data at once. With the utilization of artificial intelligence, these systems can even compose an online attendance register of students present during the lectures. Data is primarily recorded on the hard disk of the NVR (Network Video Recorder), and in the long term, it is recommended to save the data in the blockchain. The purpose of the research is to demonstrate how university security cameras can be securely connected to the blockchain. This would be important for universities as this is sensitive student data that needs to be protected from unauthorized access. In my research, as part of the practical implementation, I therefore also use encryption methods and data fragmentation, which are saved at the nodes of the blockchain. Thus, even a DDoS (Distributed Denial of Service) type attack may be easily repelled, as data is not concentrated on a single, central server. To further increase security, it is useful to constitute a blockchain capable of its own data storage at the faculty itself, rather than renting data storage space, so we, ourselves may regulate the conditions of operation, and the policy of data protection. As a practical part of my research, therefore, I created a blockchain called UEDSC (Universities Data Storage Chain) where I saved the student's data.

Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.

Network intrusion detection technology has been a popular application technology for current network security, but the existing network intrusion detection technology in the application process, there are problems such as low detection efficiency, low detection accuracy and other poor detection performance. To solve the above problems, a new treatment combining artificial intelligence with network intrusion detection is proposed. Artificial intelligence-based network intrusion detection technology refers to the application of artificial intelligence techniques, such as: neural networks, neural algorithms, etc., to network intrusion detection, and the application of these artificial intelligence techniques makes the automatic detection of network intrusion detection models possible.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.

Since data security is an important branch of the wide concept of security, using simple and interpretable data security methods is deemed necessary. A considerable volume of data that is transferred through the internet is in the form of image. Therefore, several methods have focused on encrypting and decrypting images but some of the conventional algorithms are complex and time consuming. On the other hand, denial method or steganography has attracted the researchers' attention leading to more security for transferring images. This is because attackers are not aware of encryption on images and therefore they do not try to decrypt them. Here, one of the most effective and simplest operators (XOR) is employed. The received shares in destination only with XOR operation can recover original images. Users are not necessary to be familiar with computer programing, data coding and the execution time is lesser compared to chaos-based methods or coding table. Nevertheless, for designing the key when we have messy images, we use chaotic functions. Here, in addition to use the XOR operation, eliminating the pixel expansion and meaningfulness of the shared images is of interest. This method is simple and efficient and use both encryption and steganography; therefore, it can guarantee the security of transferred images.

Cloud service uses CAPTCHA to protect itself from malicious programs. With the explosive development of AI technology and the emergency of third-party recognition services, the factors that influence CAPTCHA’s security are going to be more complex. In such a situation, evaluating the security of mainstream CAPTCHAs in cloud services is helpful to guide better CAPTCHA design choices for providers. In this paper, we evaluate and analyze the security of 6 mainstream CAPTCHA image designs in public cloud services. According to the evaluation results, we made some suggestions of CAPTCHA image design choices to cloud service providers. In addition, we particularly discussed the CAPTCHA images adopted by Facebook and Twitter. The evaluations are separated into two stages: (i) using AI techniques alone; (ii) using both AI techniques and third-party services. The former is based on open source models; the latter is conducted under our proposed framework: CAPTCHAMix.

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.