In the dynamic and ever-changing domain of Unmanned Aerial Vehicles (UAVs), the utmost importance lies in guaranteeing resilient and lucid security measures. This study highlights the necessity of implementing a Zero Trust Architecture (ZTA) to enhance the security of unmanned aerial vehicles (UAVs), hence departing from conventional perimeter defences that may expose vulnerabilities. The Zero Trust Architecture (ZTA) paradigm requires a rigorous and continuous process of authenticating all network entities and communications. The accuracy of our methodology in detecting and identifying unmanned aerial vehicles (UAVs) is 84.59\%. This is achieved by utilizing Radio Frequency (RF) signals within a Deep Learning framework, a unique method. Precise identification is crucial in Zero Trust Architecture (ZTA), as it determines network access. In addition, the use of eXplainable Artificial Intelligence (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) contributes to the improvement of the model s transparency and interpretability. Adherence to Zero Trust Architecture (ZTA) standards guarantees that the classifications of unmanned aerial vehicles (UAVs) are verifiable and comprehensible, enhancing security within the UAV field.

Explainable AI (XAI) is a topic of intense activity in the research community today. However, for AI models deployed in the critical infrastructure of communications networks, explainability alone is not enough to earn the trust of network operations teams comprising human experts with many decades of collective experience. In the present work we discuss some use cases in communications networks and state some of the additional properties, including accountability, that XAI models would have to satisfy before they can be widely deployed. In particular, we advocate for a human-in-the-Ioop approach to train and validate XAI models. Additionally, we discuss the use cases of XAI models around improving data preprocessing and data augmentation techniques, and refining data labeling rules for producing consistently labeled network datasets.

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 proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of “why one instance is an anomaly?” and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f-score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model\_1 and OPT\_Model) and compared. The overall accuracy and F-score of OPT\_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.

This research emphasizes its main contribution in the context of applying Black Box Models in Knowledge-Based Systems. It elaborates on the fundamental limitations of these models in providing internal explanations, leading to non-compliance with prevailing regulations such as GDPR and PDP, as well as user needs, especially in high-risk areas like credit evaluation. Therefore, the use of Explainable Artificial Intelligence (XAI) in such systems becomes highly significant. However, its implementation in the credit granting process in Indonesia is still limited due to evolving regulations. This study aims to demonstrate the development of a knowledge-based credit granting system in Indonesia with local explanations. The development is carried out by utilizing credit data in Indonesia, identifying suitable machine learning models, and implementing user-friendly explanation algorithms. To achieve this goal, the final system s solution is compared using Decision Tree and XGBoost models with LIME, SHAP, and Anchor explanation algorithms. Evaluation criteria include accuracy and feedback from domain experts. The research results indicate that the Decision Tree explanation method outperforms other tested methods. However, this study also faces several challenges, including limited data size due to time constraints on expert data provision and the simplicity of important features, stemming from limitations on expert authorization to share privacy-related data.

Forest fire is a problem that cannot be overlooked as it occurs every year and covers many areas. GISTDA has recognized this problem and created the model to detect burn scars from satellite imagery. However, it is effective only to some extent with additional manual correction being often required. An automated system is enriched with learning capacity is the preferred tool to support this decision-making process. Despite the improved predictive performance, the underlying model may not be transparent or explainable to operators. Reasoning and annotation of the results are essential for this problem, for which the XAI approach is appropriate. In this work, we use the SHAP framework to describe predictive variables of complex neural models such as DNN. This can be used to optimize the model and provide overall accuracy up to 99.85 \% for the present work. Moreover, to show stakeholders the reason and the contributed factors involved such as the various indices that use the reflectance of the wavelength (e.g. NIR and SWIR).

Despite intensive research, survival rate for pancreatic cancer, a fatal and incurable illness, has not dramatically improved in recent years. Deep learning systems have shown superhuman ability in a considerable number of activities, and recent developments in Artificial Intelligence (AI) have led to its widespread use in predictive analytics of pancreatic cancer. However, the improvement in performance is the result of model complexity being raised, which transforms these systems into “black box” methods and creates uncertainty about how they function and, ultimately, how they make judgements. This ambiguity has made it difficult for deep learning algorithms to be accepted in important field like healthcare, where their benefit may be enormous. As a result, there has been a significant resurgence in recent years of scholarly interest in the topic of Explainable Artificial Intelligence (XAI), which is concerned with the creation of novel techniques for interpreting and explaining deep learning models. In this study, we utilize Computed Tomography (CT) images and Clinical data to predict and analyze pancreatic cancer and survival rate respectively. Since pancreatic tumors are small to identify, the region marking through XAI will assist medical professionals to identify the appropriate region and determine the presence of cancer. Various features are taken into consideration for survival prediction. The most prominent features can be identified with the help of XAI, which in turn aids medical professionals in making better decisions. This study mainly focuses on the XAI strategy for deep and machine learning models rather than prediction and survival methodology.

Explainable AI (XAI) techniques are used for understanding the internals of the AI algorithms and how they produce a particular result. Several software packages are available implementing XAI techniques however, their use requires a deep knowledge of the AI algorithms and their output is not intuitive for non-experts. In this paper we present a framework, (XAI4PublicPolicy), that provides customizable and reusable dashboards for XAI ready to be used both for data scientists and general users with no code. The models, and data sets are selected dragging and dropping from repositories While dashboards are generated selecting the type of charts. The framework can work with structured data and images in different formats. This XAI framework was developed and is being used in the context of the AI4PublicPolicy European project for explaining the decisions made by machine learning models applied to the implementation of public policies.

Forest fire is a problem that cannot be overlooked as it occurs every year and covers many areas. GISTDA has recognized this problem and created the model to detect burn scars from satellite imagery. However, it is effective only to some extent with additional manual correction being often required. An automated system is enriched with learning capacity is the preferred tool to support this decision-making process. Despite the improved predictive performance, the underlying model may not be transparent or explainable to operators. Reasoning and annotation of the results are essential for this problem, for which the XAI approach is appropriate. In this work, we use the SHAP framework to describe predictive variables of complex neural models such as DNN. This can be used to optimize the model and provide overall accuracy up to 99.85 \% for the present work. Moreover, to show stakeholders the reason and the contributed factors involved such as the various indices that use the reflectance of the wavelength (e.g. NIR and SWIR).

Recently, Deep learning (DL) model has made remarkable achievements in image processing. To increase the accuracy of the DL model, more parameters are used. Therefore, the current DL models are black-box models that cannot understand the internal structure. This is the reason why the DL model cannot be applied to fields where stability and reliability are important despite its high performance. In this paper, We investigated various Explainable artificial intelligence (XAI) techniques to solve this problem. We also investigated what approaches exist to make multi-modal deep learning models transparent.

In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) methods for the interpretation of two Convolutional Neural Network (CNN) classifiers in the field of remote sensing (RS). Specifically, the SegNet and Unet architectures for RS building information extraction and segmentation are evaluated using a comprehensive array of primary- and layer-attributions XAI methods. The attribution methods are quantitatively evaluated using the sensitivity metric. Based on the visualization of the different XAI methods, Deconvolution and GradCAM results in many of the study areas show reliability. Moreover, these methods are able to accurately interpret both Unet s and SegNet s decisions and managed to analyze and reveal the internal mechanisms in both models (confirmed by the low sensitivity scores). Overall, no single method stood out as the best one.

Machine learning models have become increasingly complex, making it difficult to understand how they make predictions. Explainable AI (XAI) techniques have been developed to enhance model interpretability, thereby improving model transparency, trust, and accountability. In this paper, we present a comparative analysis of several XAI techniques to enhance the interpretability of machine learning models. We evaluate the performance of these techniques on a dataset commonly used for regression or classification tasks. The XAI techniques include SHAP, LIME, PDP, and GAM. We compare the effectiveness of these techniques in terms of their ability to explain model predictions and identify the most important features in the dataset. Our results indicate that XAI techniques significantly improve model interpretability, with SHAP and LIME being the most effective in identifying important features in the dataset. Our study provides insights into the strengths and limitations of different XAI techniques and their implications for the development and deployment of machine learning models. We conclude that XAI techniques have the potential to significantly enhance model interpretability and promote trust and accountability in the use of machine learning models. The paper emphasizes the importance of interpretability in medical applications of machine learning and highlights the significance of XAI techniques in developing accurate and reliable models for medical applications.

This research emphasizes its main contribution in the context of applying Black Box Models in Knowledge-Based Systems. It elaborates on the fundamental limitations of these models in providing internal explanations, leading to non-compliance with prevailing regulations such as GDPR and PDP, as well as user needs, especially in high-risk areas like credit evaluation. Therefore, the use of Explainable Artificial Intelligence (XAI) in such systems becomes highly significant. However, its implementation in the credit granting process in Indonesia is still limited due to evolving regulations. This study aims to demonstrate the development of a knowledge-based credit granting system in Indonesia with local explanations. The development is carried out by utilizing credit data in Indonesia, identifying suitable machine learning models, and implementing user-friendly explanation algorithms. To achieve this goal, the final system s solution is compared using Decision Tree and XGBoost models with LIME, SHAP, and Anchor explanation algorithms. Evaluation criteria include accuracy and feedback from domain experts. The research results indicate that the Decision Tree explanation method outperforms other tested methods. However, this study also faces several challenges, including limited data size due to time constraints on expert data provision and the simplicity of important features, stemming from limitations on expert authorization to share privacy-related data.

This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores current explanation techniques, providing insights for researchers, practitioners, and policymakers interested in enhancing AI transparency. Notably, the increasing number of systematic literature reviews (SLRs) in XAI publications indicates a growing interest in the field. The review offers an annotated catalogue for human-computer interaction-focused XAI stakeholders, emphasising practitioner guidelines. Automated searches across ACM, IEEE, and Science Direct databases identified SLRs published between 2019 and May 2023, covering diverse application domains and topics. While adhering to methodological guidelines, the SLRs often lack primary study quality assessments. The review highlights ongoing challenges and future opportunities related to XAI evaluation standardisation, its impact on users, and interdisciplinary research on ethics and GDPR aspects. The 29 SLRs, analysing over two thousand papers, include five directly relevant to practitioners. Additionally, references from the SLRs were analysed to compile a list of frequently cited papers, serving as recommended foundational readings for newcomers to the field.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of “why one instance is an anomaly?” and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f-score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model\_1 and OPT\_Model) and compared. The overall accuracy and F-score of OPT\_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.

Nowadays, anomaly-based network intrusion detection system (NIDS) still have limited real-world applications; this is particularly due to false alarms, a lack of datasets, and a lack of confidence. In this paper, we propose to use explainable artificial intelligence (XAI) methods for tackling these issues. In our experimentation, we train a random forest (RF) model on the NSL-KDD dataset, and use SHAP to generate global explanations. We find that these explanations deviate substantially from domain expertise. To shed light on the potential causes, we analyze the structural composition of the attack classes. There, we observe severe imbalances in the number of records per attack type subsumed in the attack classes of the NSL-KDD dataset, which could lead to generalization and overfitting regarding classification. Hence, we train a new RF classifier and SHAP explainer directly on the attack types. Classification performance is considerably improved, and the new explanations are matching the expectations based on domain knowledge better. Thus, we conclude that the imbalances in the dataset bias classification and consequently also the results of XAI methods like SHAP. However, the XAI methods can also be employed to find and debug issues and biases in the data and the applied model. Furthermore, the debugging results in higher trustworthiness of anomaly-based NIDS.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

In the evolving landscape of Internet of Things (IoT) security, the need for continuous adaptation of defenses is critical. Class Incremental Learning (CIL) can provide a viable solution by enabling Machine Learning (ML) and Deep Learning (DL) models to ( i) learn and adapt to new attack types (0-day attacks), ( ii) retain their ability to detect known threats, (iii) safeguard computational efficiency (i.e. no full re-training). In IoT security, where novel attacks frequently emerge, CIL offers an effective tool to enhance Intrusion Detection Systems (IDS) and secure network environments. In this study, we explore how CIL approaches empower DL-based IDS in IoT networks, using the publicly-available IoT-23 dataset. Our evaluation focuses on two essential aspects of an IDS: ( a) attack classification and ( b) misuse detection. A thorough comparison against a fully-retrained IDS, namely starting from scratch, is carried out. Finally, we place emphasis on interpreting the predictions made by incremental IDS models through eXplainable AI (XAI) tools, offering insights into potential avenues for improvement.

Attacks against computer system are viewed to be the most serious threat in the modern world. A zero-day vulnerability is an unknown vulnerability to the vendor of the system. Deep learning techniques are widely used for anomaly-based intrusion detection. The technique gives a satisfactory result for known attacks but for zero-day attacks the models give contradictory results. In this work, at first, two separate environments were setup to collect training and test data for zero-day attack. Zero-day attack data were generated by simulating real-time zero-day attacks. Ranking of the features from the train and test data was generated using explainable AI (XAI) interface. From the collected training data more attack data were generated by applying time series generative adversarial network (TGAN) for top 12 features. The train data was concatenated with the AWID dataset. A hybrid deep learning model using Long short-term memory (LSTM) and Convolutional neural network (CNN) was developed to test the zero-day data against the GAN generated concatenated dataset and the original AWID dataset. Finally, it was found that the result using the concatenated dataset gives better performance with 93.53\% accuracy, where the result from only AWID dataset gives 84.29\% accuracy.

Zero-day attacks, which are defined by their abrupt appearance without any previous detection mechanisms, present a substantial obstacle in the field of network security. To address this difficulty, a wide variety of machine learning and deep learning models have been used to identify and minimize zeroday assaults. The models have been assessed for both binary and multi-class classification situations, The objective of this work is to do a thorough comparison and analysis of these models, including the impact of class imbalance and utilizing SHAP (SHapley Additive exPlanations) explainability approaches. Class imbalance is a prevalent problem in cybersecurity datasets, characterized by a considerable disparity between the number of attack cases and non-attack instances. By equalizing the dataset, we guarantee equitable depiction of both categories, so preventing prejudice towards the dominant category throughout the training and assessment of the model. Moreover, the application of SHAP XAI facilitates a more profound comprehension of model predictions, empowering analysts to analyze the fundamental aspects that contribute to the detection of zero-day attacks.

Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.

The rapid advancement in Deep Learning (DL) proposes viable solutions to various real-world problems. However, deploying DL-based models in some applications is hindered by their black-box nature and the inability to explain them. This has pushed Explainable Artificial Intelligence (XAI) research toward DL-based models, aiming to increase the trust by reducing their opacity. Although many XAI algorithms were proposed earlier, they lack the ability to explain certain tasks, i.e. image captioning (IC). This is caused by the IC task nature, e.g. the presence of multiple objects from the same category in the captioned image. In this paper we propose and investigate an XAI approach for this particular task. Additionally, we provide a method to evaluate XAI algorithms performance in the domain1.

This paper delves into the nascent paradigm of Explainable AI (XAI) and its pivotal role in enhancing the acceptability of growing AI systems that are shaping the Digital Management 5.0 era. XAI holds significant promise, promoting compliance with legal and ethical standards and offering transparent decision-making tools. The imperative of interpretable AI systems to counter the black box effect and adhere to data protection laws like GDPR is highlighted. This paper aims to achieve a dual objective. Firstly, it provides an indepth understanding of the emerging XAI paradigm, helping practitioners and academics project their future research trajectories. Secondly, it proposes a new taxonomy of XAI models with potential applications that could facilitate AI acceptability. Although the academic literature reflects a crucial lack of exploration into the full potential of XAI, existing models remain mainly theoretical and lack practical applications. By bridging the gap between abstract models and the pragmatic implementation of XAI in management, this paper breaks new ground by launching the scientific foundations of XAI in the upcoming era of Digital Management 5.0.