The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

IoT and AI created a Transportation Management System, resulting in the Internet of Vehicles. Intelligent vehicles are combined with contemporary communication technologies (5G) to achieve automated driving and adequate mobility. IoV faces security issues in the next five (5) areas: data safety, V2X communication safety, platform safety, Intermediate Commercial Vehicles (ICV) safety, and intelligent device safety. Numerous types of AI models have been created to reduce the outcome infiltration risks on ICVs. The need to integrate confidence, transparency, and repeatability into the creation of Artificial Intelligence (AI) for the safety of ICV and to deliver harmless transport systems, on the other hand, has led to an increase in explainable AI (XAI). Therefore, the space of this analysis protected the XAI models employed in ICV intrusion detection systems (IDSs), their taxonomies, and available research concerns. The study s findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs. The paper also demonstrates that XAI s greater transparency will help it gain acceptance in the vehicle industry.

The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

The fixed security solutions and related security configurations may no longer meet the diverse requirements of 6G networks. Open Radio Access Network (O-RAN) architecture is going to be one key entry point to 6G where the direct user access is granted. O-RAN promotes the design, deployment and operation of the RAN with open interfaces and optimized by intelligent controllers. O-RAN networks are to be implemented as multi-vendor systems with interoperable components and can be programmatically optimized through centralized abstraction layer and data driven closed-loop control. However, since O-RAN contains many new open interfaces and data flows, new security issues may emerge. Providing the recommendations for dynamic security policy adjustments by considering the energy availability and risk or security level of the network is something lacking in the current state-of-the-art. When the security process is managed and executed in an autonomous way, it must also assure the transparency of the security policy adjustments and provide the reasoning behind the adjustment decisions to the interested parties whenever needed. Moreover, the energy consumption for such security solutions are constantly bringing overhead to the networking devices. Therefore, in this paper we discuss XAI based green security architecture for resilient open radio access networks in 6G known as XcARet for providing cognitive and transparent security solutions for O-RAN in a more energy efficient manner.

With the continuous enrichment of intelligent applications, it is anticipated that 6G will evolve into a ubiquitous intelligent network. In order to achieve the vision of full-scenarios intelligent services, how to collaborate AI capabilities in different domains is an urgent issue. After analyzing potential use cases and technological requirements, this paper proposes an endto-end (E2E) cross-domain artificial intelligence (AI) collaboration framework for next-generation mobile communication systems. Two potential technical solutions, namely cross-domain AI management and orchestration and RAN-CN convergence, are presented to facilitate intelligent collaboration in both E2E scenarios and the edge network. Furthermore, we have validated the performance of a cross-domain federated learning algorithm in a simulated environment for the prediction of received signal power. While ensuring the security and privacy of terminal data, we have analyzed the communication overhead caused by cross-domain training.

Currently, research on 5G communication is focusing increasingly on communication techniques. The previous studies have primarily focused on the prevention of communications disruption. To date, there has not been sufficient research on network anomaly detection as a countermeasure against on security aspect. 5g network data will be more complex and dynamic, intelligent network anomaly detection is necessary solution for protecting the network infrastructure. However, since the AI-based network anomaly detection is dependent on data, it is difficult to collect the actual labeled data in the industrial field. Also, the performance degradation in the application process to real field may occur because of the domain shift. Therefore, in this paper, we research the intelligent network anomaly detection technique based on domain adaptation (DA) in 5G edge network in order to solve the problem caused by data-driven AI. It allows us to train the models in data-rich domains and apply detection techniques in insufficient amount of data. For Our method will contribute to AI-based network anomaly detection for improving the security for 5G edge network.

With the rapid development of cloud computing services and big data applications, the number of data centers is proliferating, and with it, the problem of energy consumption in data centers is becoming more and more serious. Data center energy-saving has received more and more attention as a way to reduce carbon emissions and power costs. The main energy consumption of data centers lies in IT equipment energy consumption and end air conditioning energy consumption. In this paper, we propose a data center energy-saving application system based on fog computing architecture to reduce air conditioning energy consumption, and thus reduce data center energy consumption. Specifically, the intelligent module is placed in the fog node to take advantage of the low latency, proximal computing, and proximal storage of fog computing to shorten the network call link and improve the stability of acquiring energy-saving policies and the frequency of energy-saving regulation, thus solving the disadvantages of high latency and instability in the energy-saving approach of cloud computing architecture. The AI technology is used in the intelligent module to generate energy-saving strategies and remotely regulate the end air conditioners to achieve better energy-saving effects. This solves the shortcomings of the traditional manual regulation based on expert experience with low adjustment frequency and serious loss of cooling capacity of the terminal air conditioner. According to the experimental results, statistics show that compared with the traditional manual regulation based on expert experience, the data center energy-saving application based on fog computing can operate safely and efficiently, and reduce the PUE to 1.04. Compared with the AI energy-saving strategy based on cloud computing, the AI energy-saving strategy based on fog computing generates strategies faster and with lower latency, and the speed is increased by 29.84\%.

AI systems face potential hardware security threats. Existing AI systems generally use the heterogeneous architecture of CPU + Intelligent Accelerator, with PCIe bus for communication between them. Security mechanisms are implemented on CPUs based on the hardware security isolation architecture. But the conventional hardware security isolation architecture does not include the intelligent accelerator on the PCIe bus. Therefore, from the perspective of hardware security, data offloaded to the intelligent accelerator face great security risks. In order to effectively integrate intelligent accelerator into the CPU’s security mechanism, a novel hardware security isolation architecture is presented in this paper. The PCIe protocol is extended to be security-aware by adding security information packaging and unpacking logic in the PCIe controller. The hardware resources on the intelligent accelerator are isolated in fine-grained. The resources classified into the secure world can only be controlled and used by the software of CPU’s trusted execution environment. Based on the above hardware security isolation architecture, a security isolation spiking convolutional neural network accelerator is designed and implemented in this paper. The experimental results demonstrate that the proposed security isolation architecture has no overhead on the bandwidth and latency of the PCIe controller. The architecture does not affect the performance of the entire hardware computing process from CPU data offloading, intelligent accelerator computing, to data returning to CPU. With low hardware overhead, this security isolation architecture achieves effective isolation and protection of input data, model, and output data. And this architecture can effectively integrate hardware resources of intelligent accelerator into CPU’s security isolation mechanism.

Using Intrusion Detection Systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system s superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today.

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

Active cyber defense mechanisms are necessary to perform automated, and even autonomous operations using intelligent agents that defend against modern/sophisticated AI-inspired cyber threats (e.g., ransomware, cryptojacking, deep-fakes). These intelligent agents need to rely on deep learning using mature knowledge and should have the ability to apply this knowledge in a situational and timely manner for a given AI-inspired cyber threat. In this paper, we describe a ‘domain-agnostic knowledge graph-as-a-service’ infrastructure that can support the ability to create/store domain-specific knowledge graphs for intelligent agent Apps to deploy active cyber defense solutions defending real-world applications impacted by AI-inspired cyber threats. Specifically, we present a reference architecture, describe graph infrastructure tools, and intuitive user interfaces required to construct and maintain large-scale knowledge graphs for the use in knowledge curation, inference, and interaction, across multiple domains (e.g., healthcare, power grids, manufacturing). Moreover, we present a case study to demonstrate how to configure custom sets of knowledge curation pipelines using custom data importers and semantic extract, transform, and load scripts for active cyber defense in a power grid system. Additionally, we show fast querying methods to reach decisions regarding cyberattack detection to deploy pertinent defense to outsmart adversaries.

This paper introduces a novel AI-driven ontology-based framework for disease diagnosis and prediction, leveraging the advancements in machine learning and data mining. We have constructed a comprehensive ontology that maps the complex relationships between a multitude of diseases and their manifested symptoms. Utilizing Semantic Web Rule Language (SWRL), we have engineered a set of robust rules that facilitate the intelligent prediction of diseases, embodying the principles of NLP for enhanced interpretability. The developed system operates in two fundamental stages. Initially, we define a sophisticated class hierarchy within our ontology, detailing the intricate object and data properties with precision—a process that showcases our application of computer vision techniques to interpret and categorize medical imagery. The second stage focuses on the application of AI-powered rules, which are executed to systematically extract and present detailed disease information, including symptomatology, adhering to established medical protocols. The efficacy of our ontology is validated through extensive evaluations, demonstrating its capability to not only accurately diagnose but also predict diseases, with a particular emphasis on the AI methodologies employed. Furthermore, the system calculates a final risk score for the user, derived from a meticulous analysis of the results. This score is a testament to the seamless integration of AI and ML in developing a user-centric diagnostic tool, promising a significant impact on future research in AI, ML, NLP, and robotics within the medical domain.

A decentralized and secure architecture made possible by blockchain technology is what Web 3.0 is known for. By offering a secure and trustworthy platform for transactions and data storage, this new paradigm shift in the digital world promises to transform the way we interact with the internet. Data is the new oil, thus protecting it is equally crucial. The foundation of the web 3.0 ecosystem, which provides a secure and open method of managing user data, is blockchain technology. With the launch of Web 3.0, demand for seamless communication across numerous platforms and technologies has increased. Blockchain offers a common framework that makes it possible for various systems to communicate with one another. The decentralized nature of blockchain technology almost precludes hacker access to the system, ushering in a highly secure Web 3.0. By preserving the integrity and validity of data and transactions, blockchain helps to build trust in online transactions. AI can be integrated with blockchain to enhance its capabilities and improve the overall user experience. We can build a safe and intelligent web that empowers users, gives them more privacy, and gives them more control over their online data by merging blockchain and AI. In this article, we emphasize the value of blockchain and AI technologies in achieving Web 3.0 s full potential for a secure internet and propose a Blockchain and AI empowered framework. The future of technology is now driven by the power of blockchain, AI, and web 3.0, providing a secure and efficient way to manage digital assets and data.

As artificial intelligent models continue to grow in their capacity and sophistication, they are often trusted with very sensitive information. In the sub-field of adversarial machine learning, developments are geared solely towards finding reliable methods to systematically erode the ability of artificial intelligent systems to perform as intended. These techniques can cause serious breaches of security, interruptions to major systems, and irreversible damage to consumers. Our research evaluates the effects of various white box adversarial machine learning attacks on popular computer vision deep learning models leveraging a public X-ray dataset from the National Institutes of Health (NIH). We make use of several experiments to gauge the feasibility of developing deep learning models that are robust to adversarial machine learning attacks by taking into account different defense strategies, such as adversarial training, to observe how adversarial attacks evolve over time. Our research details how a variety white box attacks effect different components of InceptionNet, DenseNet, and ResNeXt and suggest how the models can effectively defend against these attacks.

A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.

The world has seen a quick transition from hard devices for local storage to massive virtual data centers, all possible because of cloud storage technology. Businesses have grown to be scalable, meeting consumer demands on every turn. Cloud computing has transforming the way we do business making IT more efficient and cost effective that leads to new types of cybercrimes. Securing the data in cloud is a challenging task. Cloud security is a mixture of art and science. Art is to create your own technique and technologies in such a way that the user should be authenticated. Science is because you have to come up with ways of securing your application. Data security refers to a broad set of policies, technologies and controls deployed to protect data application and the associated infrastructure of cloud computing. It ensures that the data has not been accessed by any unauthorized person. Cloud storage systems are considered to be a network of distributed data centers which typically uses cloud computing technologies like virtualization and offers some kind of interface for storing data. Virtualization is the process of grouping the physical storage from multiple network storage devices so that it looks like a single storage device.Storing the important data in the cloud has become an essential argument in the computer territory. The cloud enables the user to store the data efficiently and access the data securely. It avoids the basic expenditure on hardware, software and maintenance. Protecting the cloud data has become one of the burdensome tasks in today’s environment. Our proposed scheme "Certificateless Compressed Data Sharing in Cloud through Partial Decryption" (CCDSPD) makes use of Shared Secret Session (3S) key for encryption and double decryption process to secure the information in the cloud. CC does not use pairing concept to solve the key escrow problem. Our scheme provides an efficient secure way of sharing data to the cloud and reduces the time consumption nearly by 50 percent as compared to the existing mCL-PKE scheme in encryption and decryption process.Distributed Cloud Environment (DCE) has the ability to store the da-ta and share it with others. One of the main issues arises during this is, how safe the data in the cloud while storing and sharing. Therefore, the communication media should be safe from any intruders residing between the two entities. What if the key generator compromises with intruders and shares the keys used for both communication and data? Therefore, the proposed system makes use of the Station-to-Station (STS) protocol to make the channel safer. The concept of encrypting the secret key confuses the intruders. Duplicate File Detector (DFD) checks for any existence of the same file before uploading. The scheduler as-signs the work of generating keys to the key manager who has less task to complete or free of any task. By these techniques, the proposed system makes time-efficient, cost-efficient, and resource efficient compared to the existing system. The performance is analysed in terms of time, cost and resources. It is necessary to safeguard the communication channel between the entities before sharing the data. In this process of sharing, what if the key manager’s compromises with intruders and reveal the information of the user’s key that is used for encryption. The process of securing the key by using the user’s phrase is the key concept used in the proposed system "Secure Storing and Sharing of Data in Cloud Environment using User Phrase" (S3DCE). It does not rely on any key managers to generate the key instead the user himself generates the key. In order to provide double security, the encryption key is also encrypted by the public key derived from the user’s phrase. S3DCE guarantees privacy, confidentiality and integrity of the user data while storing and sharing. The proposed method S3DCE is more efficient in terms of time, cost and resource utilization compared to the existing algorithm DaSCE (Data Security for Cloud Environment with Semi Trusted Third Party) and DACESM (Data Security for Cloud Environment with Scheduled Key Managers).For a cloud to be secure, all of the participating entities must be secure. The security of the assets does not solely depend on an individual s security measures. The neighbouring entities may provide an opportunity to an attacker to bypass the user s defences. The data may compromise due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. Cloudsim allows to create a network that contains a set of Intelligent Sense Point (ISP) spread across an area. Each ISPs will have its own unique position and will be different from other ISPs. Cloud is a cost-efficient solution for the distribution of data but has the challenge of a data breach. The data can be compromised of attacks of ISPs. Therefore, in OSNQSC (Optimized Selection of Nodes for Enhanced in Cloud Environment), an optimized method is proposed to find the best ISPs to place the data fragments that considers the channel quality, distance and the remaining energy of the ISPs. The fragments are encrypted before storing. OSNQSC is more efficient in terms of total upload time, total download time, throughput, storage and memory consumption of the node with the existing Betweenness centrality, Eccentricity and Closeness centrality methods of DROPS (Division and Replication of Data in the Cloud for Optimal Performance and Security).

Computer networks are increasingly vulnerable to security disruptions such as congestion, malicious access, and attacks. Intrusion Detection Systems (IDS) play a crucial role in identifying and mitigating these threats. However, many IDSs have limitations, including reduced performance in terms of scalability, configurability, and fault tolerance. In this context, we aim to enhance intrusion detection through a cooperative approach. To achieve this, we propose the implementation of ICIDS-BB (Intelligent Cooperative Intrusion Detection System based on Blockchain). This system leverages Blockchain technology to secure data exchange among collaborative components. Internally, we employ two machine learning algorithms, the decision tree and random forest, to improve attack detection.

The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

The purpose of this article is to explore the use of wireless communication technology for network connectivity in ocean liner environments, which is different from the data security system of wired networks. The key work is based on data security practices in the ocean liner environment, including building a data security classification system and developing different security strategies in data collection, storage, transmission, processing, and other aspects. In addition, machine learning methods are introduced into security warning strategies to intelligently analyze data security risks and make decisions.

With increased connectivity and the application of intelligent technologies, intelligent and connected vehicles are evolving rapidly, which offers new opportunities for vehicle data security risks. However, there are currently insufficient studies to comprehensively map the security risks throughout the life cycle of intelligent and connected vehicle data. The object of this paper is to identify the main data security risks at different data life cycle phases in the field of intelligent and connected vehicles, and the data security problems those risks may bring. The following are some of the techniques used to protect the security of data against risks. The test verification is implemented by using functional reproduction and data packet capture analysis. The results indicate that there are vehicle data security risks to personal information, including location and biometric information. This paper is useful for intelligent and connected vehicle data processors in their targeted application of technical and managerial measures to mitigate data security risks in the whole data life cycle.

This paper proposes a secure data storage scheme for protecting network privacy. In the system hardware design, it is divided into interface module, basic service module and storage module. The three functional modules work together to improve the security of personal privacy data on the Internet. Establish a personal privacy database in software to ensure the security of personal privacy data. Asymmetric cryptography is used to encrypt and decrypt the data. Finally, the encrypted privacy information data is processed centrally to realize the combined storage of privacy information in the computer network. By comparing the safety and operation effect of the system, it is proved that the system has great advantages in safety and efficiency. The simulation results show that the method is effective.

Online Social Network is a network communication platform where users have profiles that can be uniquely identified by the content sent. This content can be produced, consumed, and interacted with by other users. To connect with other users on social media, users must register by providing Personally Identifiable Information (PII) to social media platforms. PII is specific information that can identify or track individuals directly. This specific information may include your name, address, social security number, or other identifying code numbers such as telephone numbers, email addresses, and others. Personal identifiable information leakage is a problem in data security. Basically, every individual does not want their personal data to be known by anyone. Utilizing a sample size of 50 respondents, this study aims to ascertain the percentage of individuals who are aware of PII security on social media. This research will use quantitative methods by distributing questionnaires. The questionnaire in this study uses a social media attribute design. The results of the survey indicate that many respondents are unaware of the security of their data and have a limited understanding of how their personal data is managed by technology companies, particularly the 80\% of non-IT respondents.

Heterogeneous wireless networks (HWNs) have security risks and challenges, and traditional network security monitoring methods are difficult to meet the security needs. This paper analyzes and researches the security monitoring algorithm of HWNs based on big data intelligent information technology, analyzes the security monitoring algorithm of HWNs based on big data intelligent information technology, which is able to dig out potential security threats from the massive network data and carry out real-time monitoring and early warning through the use of big data correlation algorithm and network security management algorithm. The experimental tests on HWNs show that big data intelligent information technology can reduce the risk of HWN environment. the accuracy and precision of HWNs events are improved, the accuracy rate is increased by about 1.2\% and the precision rate is increased by about 1.1\%. The feasibility and effectiveness of the HWNs safety monitoring algorithm based on big data intelligent information technology is verified, which lays the foundation for more research in this field

Problems such as the increase in the number of private vehicles with the population, the rise in environmental pollution, the emergence of unmet infrastructure and resource problems, and the decrease in time efficiency in cities have put local governments, cities, and countries in search of solutions. These problems faced by cities and countries are tried to be solved in the concept of smart cities and intelligent transportation by using information and communication technologies in line with the needs. While designing intelligent transportation systems (ITS), beyond traditional methods, big data should be designed in a state-of-the-art and appropriate way with the help of methods such as artificial intelligence, machine learning, and deep learning. In this study, a data-driven decision support system model was established to help the business make strategic decisions with the help of intelligent transportation data and to contribute to the elimination of public transportation problems in the city. Our study model has been established using big data technologies and business intelligence technologies: a decision support system including data sources layer, data ingestion/ collection layer, data storage and processing layer, data analytics layer, application/presentation layer, developer layer, and data management/ data security layer stages. In our study, the decision support system was modeled using ITS data supported by big data technologies, where the traditional structure could not find a solution. This paper aims to create a basis for future studies looking for solutions to the problems of integration, storage, processing, and analysis of big data and to add value to the literature that is missing within the framework of the model. We provide both the lack of literature, eliminate the lack of models before the application process of existing data sets to the business intelligence architecture and a model study before the application to be carried out by the authors.

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.