This study addresses the critical need to secure VR network communication from non-immersive attacks, employing an intrusion detection system (IDS). While deep learning (DL) models offer advanced solutions, their opacity as "black box" models raises concerns. Recognizing this gap, the research underscores the urgency for DL-based explainability, enabling data analysts and cybersecurity experts to grasp model intricacies. Leveraging sensed data from IoT devices, our work trains a DL-based model for attack detection and mitigation in the VR network, Importantly, we extend our contribution by providing comprehensive global and local interpretations of the model’s decisions post-evaluation using SHAP-based explanation.

Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications to predict disease diagnosis and reduce health risks in a predetermined way. In addition to Artificial Intelligence (AI) techniques for processing data and understanding the results of this data, Explainable Artificial Intelligence (XAI) techniques have also gained an important place in the healthcare sector. In this study, reliable and explainable artificial intelligence studies in the field of healthcare were investigated and the blockchain framework, one of the latest technologies in the field of reliability, was examined. Many researchers have used blockchain technology in the healthcare industry to exchange information between laboratories, hospitals, pharmacies, and doctors and to protect patient data. In our study, firstly, the studies whose keywords were XAI and Trustworthy Artificial Intelligence were examined, and then, among these studies, priority was given to current articles using Blockchain technology. Combining the existing methods and results of previous studies and organizing these studies, our study presented a general framework obtained from the reviewed articles. Obtaining this framework from current studies will be beneficial for future studies of both academics and scientists.

The Zero-trust security architecture is a paradigm shift toward resilient cyber warfare. Although Intrusion Detection Systems (IDS) have been widely adopted within military operations to detect malicious traffic and ensure instant remediation against attacks, this paper proposed an explainable adversarial mitigation approach specifically designed for zero-trust cyber warfare scenarios. It aims to provide a transparent and robust defense mechanism against adversarial attacks, enabling effective protection and accountability for increased resilience against attacks. The simulation results show the balance of security and trust within the proposed parameter protection model achieving a high F1-score of 94\%, a least test loss of 0.264, and an adequate detection time of 0.34s during the prediction of attack types.

This study addresses the critical need to secure VR network communication from non-immersive attacks, employing an intrusion detection system (IDS). While deep learning (DL) models offer advanced solutions, their opacity as "black box" models raises concerns. Recognizing this gap, the research underscores the urgency for DL-based explainability, enabling data analysts and cybersecurity experts to grasp model intricacies. Leveraging sensed data from IoT devices, our work trains a DL-based model for attack detection and mitigation in the VR network, Importantly, we extend our contribution by providing comprehensive global and local interpretations of the model’s decisions post-evaluation using SHAP-based explanation.

At present, technological solutions based on artificial intelligence (AI) are being accelerated in various sectors of the economy and social relations in the world. Practice shows that fast-developing information technologies, as a rule, carry new, previously unidentified threats to information security (IS). It is quite obvious that identification of vulnerabilities, threats and risks of AI technologies requires consideration of each technology separately or in some aggregate in cases of their joint use in application solutions. Of the wide range of AI technologies, data preparation, DevOps, Machine Learning (ML) algorithms, cloud technologies, microprocessors and public services (including Marketplaces) have received the most attention. Due to the high importance and impact on most AI solutions, this paper will focus on the key AI assets, the attacks and risks that arise when implementing AI-based systems, and the issue of building secure AI.

In recent years, with the accelerated development of social informatization, digital economy has gradually become the core force of economic growth in various countries. As the carrier for the digital economy, the number of IDCs is also increasing day by day, and their construction volume and scale are expanding. Energy consumption and carbon emissions are growing rapidly as IDCs require large amounts of electricity to run servers, storage, backup, cooling systems and other infrastructure. IDCs are facing serious challenges of energy saving and greenhouse gas emission. How to achieve green, low-carbon and high-quality development is of particular concern. This paper summarizes and classifies all the current green energy-saving technologies in IDCs, introduces AI-based energy-saving solutions for IDC cooling systems in detail, compares and analyzes the energy-saving effects of AI energy-saving technologies and traditional energy-saving technologies, and points out the advantages of AI energy-saving solutions applied in green IDCs.

The objective of this study is to examine the key factors that contribute to the enhancement of financial network security through the utilization of blockchain technology and artificial intelligence (AI) tools. In this study, we utilize Google Trend Analytics and VOSviewer to examine the interrelationships among significant concepts in the domain of financial security driven by blockchain technology. The findings of the study provide significant insights and recommendations for various stakeholders, such as government entities, policymakers, regulators, and professionals in the field of information technology. Our research aims to enhance the comprehension of the intricate relationship between blockchain technology and AI tools in bolstering financial network security by revealing the network connections among crucial aspects. The aforementioned findings can be utilized as a valuable resource for facilitating future joint endeavors with the objective of enhancing financial inclusion and fostering community well-being. Through the utilization of blockchain technology and artificial intelligence (AI), it is possible to collaboratively strive towards the establishment of a financial ecosystem that is both more secure and inclusive. This endeavor aims to guarantee the well-being and stability of both individuals and enterprises.

As the world becomes increasingly interconnected, new AI technologies bring new opportunities while also giving rise to new network security risks, while network security is critical to national and social security, enterprise as well as personal information security. This article mainly introduces the empowerment of AI in network security, the development trend of its application in the field of network security, the challenges faced, and suggestions, providing beneficial exploration for effectively applying artificial intelligence technology to computer network security protection.

This article presents two main objectives: (1) To synthesize the digital asset management process using AI TRiSM. (2) To study the results of the digital asset management process using AI TRiSM. Consequently, the administration of digital assets will bring about an increase in the organization s overall efficiency through the implementation of technology that utilizes artificial intelligence to drive the management system. On the other hand, having a vast volume of information within an organization may result in management issues and a lack of transparency. A multitude of organizations are making preparations to put AI TRiSM ideas into practice. The analysis revealed that the mean value is 4.91, while the standard deviation is 0.14. A digital asset management platform that can be used to track usage inside an organization can be developed with the help of the AI TRiSM model. This will help establish trust, decrease risk, and guarantee workplace security.

The objective of this study is to examine the key factors that contribute to the enhancement of financial network security through the utilization of blockchain technology and artificial intelligence (AI) tools. In this study, we utilize Google Trend Analytics and VOSviewer to examine the interrelationships among significant concepts in the domain of financial security driven by blockchain technology. The findings of the study provide significant insights and recommendations for various stakeholders, such as government entities, policymakers, regulators, and professionals in the field of information technology. Our research aims to enhance the comprehension of the intricate relationship between blockchain technology and AI tools in bolstering financial network security by revealing the network connections among crucial aspects. The aforementioned findings can be utilized as a valuable resource for facilitating future joint endeavors with the objective of enhancing financial inclusion and fostering community well-being. Through the utilization of blockchain technology and artificial intelligence (AI), it is possible to collaboratively strive towards the establishment of a financial ecosystem that is both more secure and inclusive. This endeavor aims to guarantee the well-being and stability of both individuals and enterprises.

The integration of IoT with cellular wireless networks is expected to deepen as cellular technology progresses from 5G to 6G, enabling enhanced connectivity and data exchange capabilities. However, this evolution raises security concerns, including data breaches, unauthorized access, and increased exposure to cyber threats. The complexity of 6G networks may introduce new vulnerabilities, highlighting the need for robust security measures to safeguard sensitive information and user privacy. Addressing these challenges is critical for 5G networks massively IoT-connected systems as well as any new ones that that will potentially work in the 6G environment. Artificial Intelligence is expected to play a vital role in the operation and management of 6G networks. Because of the complex interaction of IoT and 6G networks, Explainable Artificial Intelligence (AI) is expected to emerge as an important tool for enhancing security. This study presents an AI-powered security system for the Internet of Things (IoT), utilizing XGBoost, Shapley Additive, and Local Interpretable Model-agnostic explanation methods, applied to the CICIoT 2023 dataset. These explanations empowers administrators to deploy more resilient security measures tailored to address specific threats and vulnerabilities, improving overall system security against cyber threats and attacks.

The development of 5G, cloud computing, artificial intelligence (AI) and other new generation information technologies has promoted the rapid development of the data center (DC) industry, which directly increase severe energy consumption and carbon emissions problem. In addition to traditional engineering based methods, AI based technology has been widely used in existing data centers. However, the existing AI model training schemes are time-consuming and laborious. To tackle this issues, we propose an automated training and deployment platform for AI modes based on cloud-edge architecture, including the processes of data processing, data annotation, model training optimization, and model publishing. The proposed system can generate specific models based on the room environment and realize standardization and automation of model training, which is helpful for large-scale data center scenarios. The simulation and experimental results show that the proposed solution can reduce the time required of single model training by 76.2\%, and multiple training tasks can run concurrently. Therefore, it can adapt to the large-scale energy-saving scenario and greatly improve the model iteration efficiency, which improves the energy-saving rate and help green energy conservation for data centers.

At present, technological solutions based on artificial intelligence (AI) are being accelerated in various sectors of the economy and social relations in the world. Practice shows that fast-developing information technologies, as a rule, carry new, previously unidentified threats to information security (IS). It is quite obvious that identification of vulnerabilities, threats and risks of AI technologies requires consideration of each technology separately or in some aggregate in cases of their joint use in application solutions. Of the wide range of AI technologies, data preparation, DevOps, Machine Learning (ML) algorithms, cloud technologies, microprocessors and public services (including Marketplaces) have received the most attention. Due to the high importance and impact on most AI solutions, this paper will focus on the key AI assets, the attacks and risks that arise when implementing AI-based systems, and the issue of building secure AI.

This paper proposes an AI-based intrusion detection method for the ITRI AI BOX information security application. The packets captured by AI BOX are analyzed to determine whether there are network attacks or abnormal traffic according to AI algorithms. Adjust or isolate some unnatural or harmful network data transmission behaviors if detected as abnormal. AI models are used to detect anomalies and allow or restrict data transmission to ensure the information security of devices. In future versions, it will also be able to intercept packets in the field of information technology (IT) and operational technology (OT). It can be applied to the free movement between heterogeneous networks to assist in data computation and transformation. This paper uses the experimental test to realize the intrusion detection method, hoping to add value to the AI BOX information security application. When IT and OT fields use AI BOX to detect intrusion accurately, it will protect the smart factory or hospital from abnormal traffic attacks and avoid causing system paralysis, extortion, and other dangers. We have built the machine learning model, packet sniffing functionality, and the operating system setting of the AI BOX environment. A public dataset has been used to test the model, and the accuracy has achieved 99\%, and the Yocto Project environment has been available in the AI Box and tested successfully.

With the rapid growth in information technology and being called the Digital Era, it is very evident that no one can survive without internet or ICT advancements. The day-to-day life operations and activities are dependent on these technologies. The latest technology trends in the market and industry are computing power, Smart devices, artificial intelligence, Robotic process automation, metaverse, IOT (Internet of things), cloud computing, Edge computing, Block chain and much more in the coming years. When looking at all these aspect and advancements, one common thing is cloud computing and data which must be protected and safeguarded which brings in the need for cyber/cloud security. Hence cloud security challenges have become an omnipresent concern for organizations or industries of any size where it has gone from a small incident to threat landscape. When it comes to data and cyber/ cloud security there are lots of challenges seen to safeguard these data. Towards that it is necessary that everyone must be aware of the latest technological advancements, evolving cyber threats, data as a valuable asset, Human Factor, Regulatory compliance, Cyber resilience. To handle all these challenges, security and risk prediction framework is proposed in this paper. This framework PRCSAM (Predictive Risk and Complexity Score Assessment Model) will consider factors like impact and likelihood of the main risks, threats and attacks that is foreseen in cloud security and the recommendation of the Risk management framework with automatic risk assessment and scoring option catering to Information security and privacy risks. This framework will help management and organizations in making informed decisions on the cyber security strategy as this is a data driven, dynamic \& proactive approach to cyber security and its complexity calculation. This paper also discusses on the prediction techniques using Generative AI techniques.

Artificial Intelligence (AI) holds great potential for enhancing Risk Management (RM) through automated data integration and analysis. While the positive impact of AI in RM is acknowledged, concerns are rising about unintended consequences. This study explores factors like opacity, technology and security risks, revealing potential operational inefficiencies and inaccurate risk assessments. Through archival research and stakeholder interviews, including chief risk officers and credit managers, findings highlight the risks stemming from the absence of AI regulations, operational opacity, and information overload. These risks encompass cybersecurity threats, data manipulation uncertainties, monitoring challenges, and biases in algorithms. The study emphasizes the need for a responsible AI framework to address these emerging risks and enhance the effectiveness of RM processes. By advocating for such a framework, the authors provide practical insights for risk managers and identify avenues for future research in this evolving field.

The integration of IoT with cellular wireless networks is expected to deepen as cellular technology progresses from 5G to 6G, enabling enhanced connectivity and data exchange capabilities. However, this evolution raises security concerns, including data breaches, unauthorized access, and increased exposure to cyber threats. The complexity of 6G networks may introduce new vulnerabilities, highlighting the need for robust security measures to safeguard sensitive information and user privacy. Addressing these challenges is critical for 5G networks massively IoT-connected systems as well as any new ones that that will potentially work in the 6G environment. Artificial Intelligence is expected to play a vital role in the operation and management of 6G networks. Because of the complex interaction of IoT and 6G networks, Explainable Artificial Intelligence (AI) is expected to emerge as an important tool for enhancing security. This study presents an AI-powered security system for the Internet of Things (IoT), utilizing XGBoost, Shapley Additive, and Local Interpretable Model-agnostic explanation methods, applied to the CICIoT 2023 dataset. These explanations empowers administrators to deploy more resilient security measures tailored to address specific threats and vulnerabilities, improving overall system security against cyber threats and attacks.

With the increasing number and types of APP vulnerabilities, the detection technology and methods need to be enriched and personalized according to different types of security vulnerabilities. Therefore, a single detection technology can no longer meet the needs of business security diversity. First of all, the new detection method needs to clarify the relevant features of APP business security; Secondly, the new detection method needs to re-adapt the features related to APP business security; Thirdly, the new detection method needs to be trained and applied according to different AI algorithms. In view of this, we designed an APP privacy information leakage detection scheme based on deep learning. This scheme specifically selects business security-related features for the type of privacy information leakage vulnerability of APP, and then performs feature processing and adaptation to become the input parameters of CNN network algorithm. Finally, we train and call the CNN network algorithm. We selected the APP of the Telecom Tianyi Space App Store for experiment to evaluate the effectiveness of our APP privacy information leakage detection system based on CNN network. The experimental results show that the detection accuracy of our proposed detection system has achieved the desired effect.

In the past two years, technology has undergone significant changes that have had a major impact on healthcare systems. Artificial intelligence (AI) is a key component of this change, and it can assist doctors with various healthcare systems and intelligent health systems. AI is crucial in diagnosing common diseases, developing new medications, and analyzing patient information from electronic health records. However, one of the main issues with adopting AI in healthcare is the lack of transparency, as doctors must interpret the output of the AI. Explainable AI (XAI) is extremely important for the healthcare sector and comes into play in this regard. With XAI, doctors, patients, and other stakeholders can more easily examine a decision s reliability by knowing its reasoning due to XAI s interpretable explanations. Deep learning is used in this study to discuss explainable artificial intelligence (XAI) in medical image analysis. The primary goal of this paper is to provide a generic six-category XAI architecture for classifying DL-based medical image analysis and interpretability methods.The interpretability method/XAI approach for medical image analysis is often categorized based on the explanation and technical method. In XAI approaches, the explanation method is further sub-categorized into three types: text-based, visual-based, and examples-based. In interpretability technical method, it was divided into nine categories. Finally, the paper discusses the advantages, disadvantages, and limitations of each neural network-based interpretability method for medical imaging analysis.

This work examines whether the resolution of a programming guide is related to academic success in a introductory programming course at the Andrés Bello University (Chile). We investigated whether the guide, which consists of 52 exercises which are not mandatory to solve, helps predict the failure of the first test of this course by first-year students. Furthermore, the use of the unified SHAP and XAI framework is proposed to analyze and understand how programming guides influence student performance. The study includes a literature review of previous related studies, a descriptive analysis of the data collected, and a discussion of the practical and theoretical implications of the study. The results obtained will be useful to improve student support strategies and decision making related to the use of guides as an educational tool.

As deep-learning based image and video manipulation technology advances, the future of truth and information looks bleak. In particular, Deepfakes, wherein a person’s face can be transferred onto the face of someone else, pose a serious threat for potential spread of convincing misinformation that is drastic and ubiquitous enough to have catastrophic real-world consequences. To prevent this, an effective detection tool for manipulated media is needed. However, the detector cannot just be good, it has to evolve with the technology to keep pace with or even outpace the enemy. At the same time, it must defend against different attack types to which deep learning systems are vulnerable. To that end, in this paper, we review various methods of both attack and defense on AI systems, as well as modes of evolution for such a system. Then, we put forward a potential system that combines the latest technologies in multiple areas as well as several novel ideas to create a detection algorithm that is robust against many attacks and can learn over time with unprecedented effectiveness and efficiency.

With the increasing complexity of network attacks, traditional firewall technologies are facing challenges in effectively detecting and preventing these attacks. As a result, AI technology has emerged as a promising approach to enhance the capabilities of firewalls in detecting and mitigating network attacks. This paper aims to investigate the application of AI firewalls in network attack detection and proposes a testing method to evaluate their performance. An experiment was conducted to verify the feasibility of the proposed testing method. The results demonstrate that AI firewalls exhibit higher accuracy in detecting network attacks, thereby highlighting their effectiveness. Furthermore, the testing method can be utilized to compare different AI firewalls.

Zero Day Threats (ZDT) are novel methods used by malicious actors to attack and exploit information technology (IT) networks or infrastructure. In the past few years, the number of these threats has been increasing at an alarming rate and have been costing organizations millions of dollars to remediate. The increasing expansion of network attack surfaces and the exponentially growing number of assets on these networks necessitate the need for a robust AI-based Zero Day Threat detection model that can quickly analyze petabyte-scale data for potentially malicious and novel activity. In this paper, the authors introduce a deep learning based approach to Zero Day Threat detection that can generalize, scale, and effectively identify threats in near real-time. The methodology utilizes network flow telemetry augmented with asset-level graph features, which are passed through a dual-autoencoder structure for anomaly and novelty detection respectively. The models have been trained and tested on four large scale datasets that are representative of real-world organizational networks and they produce strong results with high precision and recall values. The models provide a novel methodology to detect complex threats with low false positive rates that allow security operators to avoid alert fatigue while drastically reducing their mean time to response with near-real-time detection. Furthermore, the authors also provide a novel, labelled, cyber attack dataset generated from adversarial activity that can be used for validation or training of other models. With this paper, the authors’ overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.

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).

Cloud computing is a nascent paradigm in the field of data technology and computer science which is predicated on the use of the Internet, often known as the World Wide Web. One of the prominent concerns within this field is the security aspects of cloud computing. Contrarily, ensuring the preservation of access to the protection of sensitive and confidential information inside financial organizations, banks and other pertinent enterprises holds significant significance. This holds significant relevance. The efficacy of the security measures in providing assurance is not infallible and can be compromised by malevolent entities. In the current study, our objective is to examine the study about the security measures through the use of a novel methodology. The primary objective of this research is to investigate the subject of data access in the realm of cloud computing, with a particular emphasis on its ramifications for corporations and other pertinent organizations. The implementation of locationbased encryption facilitates the determination of accurate geographical coordinates. In experiment apply Integrated Location Based Security using Multi objective Optimization (ILBS-MOO) on different workflows and improve performance metrics significantly. Time delay averagely approximates improvement 6-7\%, storage 10-12\% and security 8-10\%.