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.

The introductory part of the research mainly focuses on the importance of using block chain facilities by using the 5G Network that can be useful for data privacy and security. It can be said that the research mainly focuses on all the benefits of using block chain technology in order to protect all the access of relevant data by implementing intelligent contracts for enhancing the security framework related to the use of 5G networks on the data protection activities. The Literature review of the research mainly concentrates on the benefits and merits of applying the block chain facilities for enhancing both the growth as well as the development of data protection and data privacy. All the merits, as well as demerits of using the block chain facility, have been also discussed throughout the overall research paper. On the other hand, various methods, as well as strategies for applying the block chain facilities, also have been analyzed throughout the literature review section of this research paper. A survey was conducted in this particular scenario to get a clear comprehension of the situation. A survey was conducted with fifty one random people that enable the researches to get a clear picture of the trend while fetching some real life data in this particular scenario.

The data of the government and enterprises, as the production factors are facing risks and problems of security violations, such as data leakage, data abuse and data tampering during quick circulation. This paper studies the security supervision architecture of data circulation (exchange, sharing, transaction) from the perspective of the whole life cycle, proposes and constructs the security supervision metadata model, which is used to represent the changes of users, behavior, data lineage, etc. during the whole life cycle of data; For massive data, based on the metadata model of security supervision, innovates the key technologies such as data security monitoring, tracing and ownership authentication; Per the verification need, a set of security supervision prototype showing security situation, tracing performance, ownership construction/authentication and low-level visual explorer is developed.

This paper explores the advantages and limitations of probabilistic and deterministic encryption schemes for securing sensitive data. While probabilistic encryption ensures high security for data encryption, it can pose limitations when filtering and querying data. On the other hand, deterministic encryption method is a more flexible and unchanging encryption scheme that allows for the benefits of filtering data while icing its security. Many platform encryptions use deterministic encryption to allow for filtering of translated data while minimizing exposure of plain values to cipher values. Still, deterministic encryption can still pose certain pitfalls and may reveal information to eavesdroppers. A promising variation of encryption for perfecting security in communication end is ‘Varying encryption’ which is grounded on factors such as distance and country of connection. This acclimatized approach offers increased speed and security and can confuse attackers, making it harder for them to gain access to information being transmitted. Though, careful analysis of the advantages and disadvantages of assigning a specific encryption standard to a given set of conditions is essential to achieve optimal results.

Connected, Cooperative, and Autonomous Mobility (CCAM) will take intelligent transportation to a new level of complexity. CCAM systems can be thought of as complex Systems-of-Systems (SoSs). They pose new challenges to security as consequences of vulnerabilities or attacks become much harder to assess. In this paper, we propose the use of a specific type of a trust model, called subjective trust network, to model and assess trustworthiness of data and nodes in an automotive SoS. Given the complexity of the topic, we illustrate the application of subjective trust networks on a specific example, namely Cooperative Intersection Management (CIM). To this end, we introduce the CIM use-case and show how it can be modelled as a subjective trust network. We then analyze how such trust models can be useful both for design time and run-time analysis, and how they would allow us a more precise quantitative assessment of trust in automotive SoSs. Finally, we also discuss the open research problems and practical challenges that need to be addressed before such trust models can be applied in practice.

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications.The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments.

Named Data Networking (NDN) has been considered a promising network architecture for Vehicular Ad Hoc Networks (VANETs), what became known as Vehicular Named-Data Networking (VNDN). This new paradigm brings the potential to improve Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) that are inefficient in urban intelligent transport scenarios. Despite the advantages, VNDN brings inherent problems, such as the routing interest packages on NDN, which causes serious problem in the vehicular environment. The broadcast storm attack results in a huge amount of packet loss, provoking transmission overload. In addition, the link disconnection caused by the highly dynamic topology leads to a low package delivery rate. In this article, we propose a strategy for forwarding packages of interest in VNDN networks, using fuzzy logic to mitigate the broadcast storm. The proposal also aims to avoid packet collision and efficient data recovery, which the approach is based on metrics such as the nodes distance, the link stability and the signal quality. The results show a reduction in the number of Interest and Data packets without disrupting network performance maintaining adequate Interest delays.

Entering the critical year of the 14th Five Year Plan, China s information security industry has entered a new stage of development. With the increasing importance of information security, its industrial development has been paid attention to, but the data fragmentation of China s information security industry is serious, and there are few corresponding summaries and predictions. To achieve the development prediction of the industry, this article studies the intelligent prediction of information security industry data based on machine learning and new adaptive weighted fusion, and deduces the system based on the research results to promote industry development. Firstly, collect, filter, integrate, and preprocess industry data. Based on the characteristics of the data, machine learning algorithms such as linear regression, ridge regression, logical regression, polynomial regression and random forest are selected to predict the data, and the corresponding optimal parameters are found and set in the model creation. And an improved adaptive weighted fusion model based on model prediction performance was proposed. Its principle is to adaptively select the model with the lowest mean square error (MSE) value for fusion based on the real-time prediction performance of multiple machine learning models, and its weight is also calculated adaptively to improve prediction accuracy. Secondly, using technologies such as Matplotlib and Pyecharts to visualize the data and predicted results, it was found that the development trend of the information security industry is closely related to factors such as national information security laws and regulations, the situation between countries, and social emergencies. According to the predicted results of the data, it is observed that both industry input and output have shown an upward trend in recent years. In the future, China s information security industry is expected to maintain stable and rapid growth driven by the domestic market.

The last decade witnessed a gradual shift from cloudbased computing towards ubiquitous computing, which has put at a greater security risk every element of the computing ecosystem including devices, data, network, and decision making. Indeed, emerging pervasive computing paradigms have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness, the underlying mechanics that enable intelligent functions, and the deeply integrated physical and cyber spaces. Furthermore, interconnected computing environments now enjoy many unconventional characteristics that mandate a radical change in security engineering tools. This need is further exacerbated by the rapid emergence of new Advanced Persistent Threats (APTs) that target critical infrastructures and aim to stealthily undermine their operations in innovative and intelligent ways. To enable system and network designers to be prepared to face this new wave of dangerous threats, this paper overviews recent APTs in emerging computing systems and proposes a new approach to APTs that is more tailored towards such systems compared to traditional IT infrastructures. The proposed APT lifecycle will inform security decisions and implementation choices in future pervasive networked systems.

Traditional Web application category recognition is implemented by fingerprint rule matching, which is difficult to extract fingerprint rules and has limited coverage. At present, many improved identification methods semi-automatically extract fingerprints through certain rules and identify Web application categories through clustering or classification algorithms, but still rely on fingerprint rules and human intervention, and the time complexity of classification is too high to process a large amount of data. This paper proposes Multi-layer Simhash Algorithm and combines DBSCAN clustering to realize intelligent identification of Web application types, pioneering the complete automation of fingerprint identification of Web applications. This method has the function of discovering unknown Web applications and predicting unknown application types, and solves the problems of fingerprint rule extraction and manual dependence of Web applications. This paper through the TF-IDF algorithm to extract the Web page text key words and weight, Then, Multi-layer Simhash Algorithm is used to transform text feature words and weights into binary characteristic hash value, at last, the hamming distance between the input Web page and the characteristic hash value of the known category is compared with the radius of the base class, which determines the category of the input Web application. The experimental results show that the accuracy of Web application category recognition and prediction is more than 97\% and 93\% respectively.

By analyzing the design requirements of a secure desktop virtualization information system, this paper proposes the security virtualization technology of "whitelist" security mechanism, the virtualization layer security technology of optimized design, and the virtual machine security technology of resource and network layer isolation. On this basis, this paper constructs the overall architecture of the secure desktop virtualization information system. This paper studies the desktop virtualization technology research based on VMware using VMware server virtualization solution to transform and upgrade the traditional intelligent desktop virtualization system, improve server resource utilization rate, and reduce operation and maintenance costs.