Around the world there has been an advancement of IoT edge devices, that in turn have enabled the collection of rich datasets as part of the Mobile Crowd Sensing (MCS) paradigm, which in practice is implemented in a variety of safety critical applications. In spite of the advantages of such datasets, there exists an inherent data trustworthiness challenge due to the interference of malevolent actors. In this context, there has been a great body of proposed solutions which capitalize on conventional machine algorithms for sifting through faulty data without any assumptions on the trustworthiness of the source. However, there is still a number of open issues, such as how to cope with strong colluding adversaries, while in parallel managing efficiently the sizable influx of user data. In this work we suggest that the usage of explainable artificial intelligence (XAI) can lead to even more efficient performance as we tackle the limitation of conventional black box models, by enabling the understanding and interpretation of a model s operation. Our approach enables the reasoning of the model s accuracy in the presence of adversaries and has the ability to shun out faulty or malicious data, thus, enhancing the model s adaptation process. To this end, we provide a prototype implementation coupled with a detailed performance evaluation under different scenarios of attacks, employing both real and synthetic datasets. Our results suggest that the use of XAI leads to improved performance compared to other existing schemes.

Sixth generation (6G)-enabled massive network MANO orchestration, alongside distributed supervision and fully reconfigurable control logic that manages dynamic arrangement of network components, such as cell-free, Open-Air Interface (OAI) and RIS, is a potent enabler for the upcoming pervasive digitalization of the vertical use cases. In such a disruptive domain, artificial intelligence (AI)-driven zero-touch “Network of Networks” intent-based automation shall be able to guarantee a high degree of security, efficiency, scalability, and sustainability, especially in cross-domain and interoperable deployment environments (i.e., where points of presence (PoPs) are non-independent and identically distributed (non-IID)). To this extent, this paper presents a novel breakthrough, open, and fully reconfigurable networking architecture for 6G cellular paradigms, named 6G-BRICKS. To this end, 6G-BRICKS will deliver the first open and programmable O-RAN Radio Unit (RU) for 6G networks, termed as the OpenRU, based on an NI USRP-based platform. Moreover, 6G-BRICKS will integrate the RIS concept into the OAI alongside Testing as a Service (TaaS) capabilities, multi-tenancy, disaggregated Operations Support Systems (OSS) and Deep Edge adaptation at the forefront. The overall ambition of 6G-BRICKS is to offer evolvability, granularity, while, at the same time, tackling big challenges such as interdisciplinary efforts and big investments in 6G integration.

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.

Explainable AI is an emerging field that aims to address how black-box decisions of AI systems are made, by attempting to understand the steps and models involved in this decision-making. Explainable AI in manufacturing is supposed to deliver predictability, agility, and resiliency across targeted manufacturing apps. In this context, large amounts of data, which can be of high sensitivity and various formats need to be securely and efficiently handled. This paper proposes an Asset Management and Secure Sharing solution tailored to the Explainable AI and Manufacturing context in order to tackle this challenge. The proposed asset management architecture enables an extensive data management and secure sharing solution for industrial data assets. Industrial data can be pulled, imported, managed, shared, and tracked with a high level of security using this design. This paper describes the solution´s overall architectural design and gives an overview of the functionalities and incorporated technologies of the involved components, which are responsible for data collection, management, provenance, and sharing as well as for overall security.

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 pervasive proliferation of digital technologies and interconnected systems has heightened the necessity for comprehensive cybersecurity measures in computer technological know-how. While deep gaining knowledge of (DL) has turn out to be a effective tool for bolstering security, its effectiveness is being examined via malicious hacking. Cybersecurity has end up an trouble of essential importance inside the cutting-edge virtual world. By making it feasible to become aware of and respond to threats in actual time, Deep Learning is a important issue of progressed security. Adversarial assaults, interpretability of models, and a lack of categorized statistics are all obstacles that want to be studied further with the intention to support DL-based totally security solutions. The protection and reliability of DL in our on-line world relies upon on being able to triumph over those boundaries. The present studies presents a unique method for strengthening DL-based totally cybersecurity, known as name dynamic adverse resilience for deep learning-based totally cybersecurity (DARDL-C). DARDL-C gives a dynamic and adaptable framework to counter antagonistic assaults by using combining adaptive neural community architectures with ensemble learning, real-time threat tracking, risk intelligence integration, explainable AI (XAI) for version interpretability, and reinforcement getting to know for adaptive defense techniques. The cause of this generation is to make DL fashions more secure and proof against the constantly transferring nature of online threats. The importance of simulation evaluation in determining DARDL-C s effectiveness in practical settings with out compromising genuine safety is important. Professionals and researchers can compare the efficacy and versatility of DARDL-C with the aid of simulating realistic threats in managed contexts. This gives precious insights into the machine s strengths and regions for improvement.

Internet of Things (IoT) and Artificial Intelligence (AI) systems have become prevalent across various industries, steering to diverse and far-reaching outcomes, and their convergence has garnered significant attention in the tech world. Studies and reviews are instrumental in supplying industries with the nuanced understanding of the multifaceted developments of this joint domain. This paper undertakes a critical examination of existing perspectives and governance policies, adopting a contextual approach, and addressing not only the potential but also the limitations of these governance policies. In the complex landscape of AI-infused IoT systems, transparency and interpretability are pivotal qualities for informed decision-making and effective governance. In AI governance, transparency allows for scrutiny and accountability, while interpretability facilitates trust and confidence in AI-driven decisions. Therefore, we also evaluate and advocate for the use of two very popular eXplainable AI (XAI) techniques-SHAP and LIME-in explaining the predictive results of AI models. Subsequently, this paper underscores the imperative of not only maximizing the advantages and services derived from the incorporation of IoT and AI but also diligently minimizing possible risks and challenges.

The stock market is a topic that is of interest to all sorts of people. It is a place where the prices change very drastically. So, something needs to be done to help the people risking their money on the stock market. The public s opinions are crucial for the stock market. Sentiment is a very powerful force that is constantly changing and having a significant impact. It is reflected on social media platforms, where almost the entire country is active, as well as in the daily news. Many projects have been done in the stock prediction genre, but since sentiments play a big part in the stock market, making predictions of prices without them would lead to inefficient predictions, and hence Sentiment analysis is very important for stock market price prediction. To predict stock market prices, we will combine sentiment analysis from various sources, including News and Twitter. Results are evaluated for two different cryptocurrencies: Ethereum and Solana. Random Forest achieved the best RMSE of 13.434 and MAE of 11.919 for Ethereum. Support Vector Machine achieved the best RMSE of 2.48 and MAE of 1.78 for Solana.

Peer-to-peer (P2P) lenders face regulatory, compliance, application, and data security risks. A complete methodology that includes more than statistical and economic methods is needed to conduct credit assessments effectively. This study uses systematic literature network analysis and artificial intelligence to comprehend risk management in P2P lending financial technology. This study suggests that explainable AI (XAI) is better at identifying, analyzing, and evaluating financial industry risks, including financial technology. This is done through human agency, monitoring, transparency, and accountability. The LIME Framework and SHAP Value are widely used machine learning frameworks for data integration to speed up and improve credit score analysis using bank-like criteria. Thus, machine learning is expected to be used to develop a precise and rational individual credit evaluation system in peer-to-peer lending to improve credit risk supervision and forecasting while reducing default risk.

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.

Security applications use machine learning (ML) models and artificial intelligence (AI) to autonomously protect systems. However, security decisions are more impactful if they are coupled with their rationale. The explanation behind an ML model s result provides the rationale necessary for a security decision. Explainable AI (XAI) techniques provide insights into the state of a model s attributes and their contribution to the model s results to gain the end user s confidence. It requires human intervention to investigate and interpret the explanation. The interpretation must align system s security profile(s). A security profile is an abstraction of the system s security requirements and related functionalities to comply with them. Relying on human intervention for interpretation is infeasible for an autonomous system (AS) since it must self-adapt its functionalities in response to uncertainty at runtime. Thus, an AS requires an automated approach to extract security profile information from ML model XAI outcomes. The challenge is unifying the XAI outcomes with the security profile to represent the interpretation in a structured form. This paper presents a component to facilitate AS information extraction from ML model XAI outcomes related to predictions and generating an interpretation considering the security profile.

Over the past two decades, Cyber-Physical Systems (CPS) have emerged as critical components in various industries, integrating digital and physical elements to improve efficiency and automation, from smart manufacturing and autonomous vehicles to advanced healthcare devices. However, the increasing complexity of CPS and their deployment in highly dynamic contexts undermine user trust. This motivates the investigation of methods capable of generating explanations about the behavior of CPS. To this end, Explainable Artificial Intelligence (XAI) methodologies show potential. However, these approaches do not consider contextual variables that a CPS may be subjected to (e.g., temperature, humidity), and the provided explanations are typically not actionable. In this article, we propose an Actionable Contextual Explanation System (ACES) that considers such contextual influences. Based on a user query about a behavioral attribute of a CPS (for example, vibrations and speed), ACES creates contextual explanations for the behavior of such a CPS considering its context. To generate contextual explanations, ACES uses a context model to discover sensors and actuators in the physical environment of a CPS and obtains time-series data from these devices. It then cross-correlates these time-series logs with the user-specified behavioral attribute of the CPS. Finally, ACES employs a counterfactual explanation method and takes user feedback to identify causal relationships between the contextual variables and the behavior of the CPS. We demonstrate our approach with a synthetic use case; the favorable results obtained, motivate the future deployment of ACES in real-world scenarios.

Conventional approaches to analyzing industrial control systems have relied on either white-box analysis or black-box fuzzing. However, white-box methods rely on sophisticated domain expertise, while black-box methods suffers from state explosion and thus scales poorly when analyzing real ICS involving a large number of sensors and actuators. To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set of actuators critical to ICS safety, which result in significant reduction of state space without relying on domain expertise. Experiment results show that our method accurately explains the ICS model and significantly speeds-up fuzzing by 64x when compared to conventional black-box methods.

Many forms of machine learning (ML) and artificial intelligence (AI) techniques are adopted in communication networks to perform all optimizations, security management, and decision-making tasks. Instead of using conventional blackbox models, the tendency is to use explainable ML models that provide transparency and accountability. Moreover, Federate Learning (FL) type ML models are becoming more popular than the typical Centralized Learning (CL) models due to the distributed nature of the networks and security privacy concerns. Therefore, it is very timely to research how to find the explainability using Explainable AI (XAI) in different ML models. This paper comprehensively analyzes using XAI in CL and FL-based anomaly detection in networks. We use a deep neural network as the black-box model with two data sets, UNSW-NB15 and NSLKDD, and SHapley Additive exPlanations (SHAP) as the XAI model. We demonstrate that the FL explanation differs from CL with the client anomaly percentage.

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers, and adversaries, who utilize XAI for 4 distinct objectives within an ML pipeline, namely 1) XAI-enabled user assistance, 2) XAI-enabled model verification, 3) explanation verification \& robustness, and 4) offensive use of explanations. Our analysis of the literature indicates that many of the XAI applications are designed with little understanding of how they might be integrated into analyst workflows – user studies for explanation evaluation are conducted in only 14\% of the cases. The security literature sometimes also fails to disentangle the role of the various stakeholders, e.g., by providing explanations to model users and designers while also exposing them to adversaries. Additionally, the role of model designers is particularly minimized in the security literature. To this end, we present an illustrative tutorial for model designers, demonstrating how XAI can help with model verification. We also discuss scenarios where interpretability by design may be a better alternative. The systematization and the tutorial enable us to challenge several assumptions, and present open problems that can help shape the future of XAI research within cybersecurity.

Deep learning models are being utilized and further developed in many application domains, but challenges still exist regarding their interpretability and consistency. Interpretability is important to provide users with transparent information that enhances the trust between the user and the learning model. It also gives developers feedback to improve the consistency of their deep learning models. In this paper, we present a novel architectural design to embed interpretation into the architecture of the deep learning model. We apply dynamic pixel-wised weights to input images and produce a highly correlated feature map for classification. This feature map is useful for providing interpretation and transparent information about the decision-making of the deep learning model while keeping full context about the relevant feature information compared to previous interpretation algorithms. The proposed model achieved 92\% accuracy for CIFAR 10 classifications without finetuning the hyperparameters. Furthermore, it achieved a 20\% accuracy under 8/255 PGD adversarial attack for 100 iterations without any defense method, indicating extra natural robustness compared to other Convolutional Neural Network (CNN) models. The results demonstrate the feasibility of the proposed architecture.

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.

Explainable AI is an emerging field that aims to address how black-box decisions of AI systems are made, by attempting to understand the steps and models involved in this decision-making. Explainable AI in manufacturing is supposed to deliver predictability, agility, and resiliency across targeted manufacturing apps. In this context, large amounts of data, which can be of high sensitivity and various formats need to be securely and efficiently handled. This paper proposes an Asset Management and Secure Sharing solution tailored to the Explainable AI and Manufacturing context in order to tackle this challenge. The proposed asset management architecture enables an extensive data management and secure sharing solution for industrial data assets. Industrial data can be pulled, imported, managed, shared, and tracked with a high level of security using this design. This paper describes the solution´s overall architectural design and gives an overview of the functionalities and incorporated technologies of the involved components, which are responsible for data collection, management, provenance, and sharing as well as for overall security.

The interest in metaverse applications by existing industries has seen massive growth thanks to the accelerated pace of research in key technological fields and the shift towards virtual interactions fueled by the Covid-19 pandemic. One key industry that can benefit from the integration into the metaverse is healthcare. The potential to provide enhanced care for patients affected by multiple health issues, from standard afflictions to more specialized pathologies, is being explored through the fabrication of architectures that can support metaverse applications. In this paper, we focus on the persistent issues of lung cancer detection, monitoring, and treatment, to propose MetaLung, a privacy and integrity-preserving architecture on the metaverse. We discuss the use cases to enable remote patient-doctor interactions, patient constant monitoring, and remote care. By leveraging technologies such as digital twins, edge computing, explainable AI, IoT, and virtual/augmented reality, we propose how the system could provide better assistance to lung cancer patients and suggest individualized treatment plans to the doctors based on their information. In addition, we describe the current implementation state of the AI-based Decision Support System for treatment selection, I3LUNG, and the current state of patient data collection.

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.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

As cloud computing continues to evolve, the security of cloud-based systems remains a paramount concern. This research paper delves into the intricate realm of intrusion detection systems (IDS) within cloud environments, shedding light on their diverse types, associated challenges, and inherent limitations. In parallel, the study dissects the realm of Explainable AI (XAI), unveiling its conceptual essence and its transformative role in illuminating the inner workings of complex AI models. Amidst the dynamic landscape of cybersecurity, this paper unravels the synergistic potential of fusing XAI with intrusion detection, accentuating how XAI can enrich transparency and interpretability in the decision-making processes of AI-driven IDS. The exploration of XAI s promises extends to its capacity to mitigate contemporary challenges faced by traditional IDS, particularly in reducing false positives and false negatives. By fostering an understanding of these challenges and their ram-ifications this study elucidates the path forward in enhancing cloud-based security mechanisms. Ultimately, the culmination of insights reinforces the imperative role of Explainable AI in fortifying intrusion detection systems, paving the way for a more robust and comprehensible cybersecurity landscape in the cloud.

Conventional approaches to analyzing industrial control systems have relied on either white-box analysis or black-box fuzzing. However, white-box methods rely on sophisticated domain expertise, while black-box methods suffers from state explosion and thus scales poorly when analyzing real ICS involving a large number of sensors and actuators. To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set of actuators critical to ICS safety, which result in significant reduction of state space without relying on domain expertise. Experiment results show that our method accurately explains the ICS model and significantly speeds-up fuzzing by 64x when compared to conventional black-box methods.