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

The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service s architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.CCS CONCEPTS• Software and its engineering Software architectures; • Information systems World Wide Web; • Security and privacy Privacy protections; • Social and professional topics Copyrights; • Computing methodologies Machine learning.

This work introduces an innovative security system prototype tailored explicitly for paying guest accommodations or hostels, blending Internet of Things (IoT), artificial intelligence (AI), machine learning algorithms, and web crawling technologies. The core emphasis revolves around facial recognition, precisely distinguishing between known and unknown individuals to manage entry effectively. The system, integrating camera technology, captures visitor images and employs advanced face recognition algorithms for precise face classification. In instances where faces remain unrecognized, the system leverages web crawling to retrieve potential intruder details. Immediate notifications, featuring captured images, are swiftly dispatched to users through email and smartphone alerts, enabling prompt responses. Operated within a wireless infrastructure governed by a Raspberry Pi, this system prioritizes cost-effectiveness and user-friendliness. Rigorously tested across diverse environments encompassing homes, paying guest accommodations, and office spaces, this research establishes a remarkable balance between cutting-edge technology and pragmatic security applications. This solution offers an affordable and efficient security option tailored explicitly for the unique needs of contemporary hostels and paying guest accommodations, ensuring heightened security without exorbitant expenses.

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.

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly. CCS CONCEPTS • Security and privacy; • Computing methodologies → Artificial intelligence; Machine learning;

We propose a conceptual framework, named "AI Security Continuum," consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension.

A fast expanding topic of study on automated AI is focused on the prediction and prevention of cyber-attacks using machine learning algorithms. In this study, we examined the research on applying machine learning algorithms to the problems of strategic cyber defense and attack forecasting. We also provided a technique for assessing and choosing the best machine learning models for anticipating cyber-attacks. Our findings show that machine learning methods, especially random forest and neural network models, are very accurate in predicting cyber-attacks. Additionally, we discovered a number of crucial characteristics, such as source IP, packet size, and malicious traffic that are strongly associated with the likelihood of cyber-attacks. Our results imply that automated AI research on cyber-attack prediction and security planning has tremendous promise for enhancing cyber-security and averting cyber-attacks.

The authors clarified in 2020 that the relationship between AI and security can be classified into four categories: (a) attacks using AI, (b) attacks by AI itself, (c) attacks to AI, and (d) security measures using AI, and summarized research trends for each. Subsequently, ChatGPT became available in November 2022, and the various potential applications of ChatGPT and other generative AIs and the associated risks have attracted attention. In this study, we examined how the emergence of generative AI affects the relationship between AI and security. The results show that (a) the need for the four perspectives of AI and security remains unchanged in the era of generative AI, (b) The generalization of AI targets and automatic program generation with the birth of generative AI will greatly increase the risk of attacks by the AI itself, (c) The birth of generative AI will make it possible to generate easy-to-understand answers to various questions in natural language, which may lead to the spread of fake news and phishing e-mails that can easily fool many people and an increase in AI-based attacks. In addition, it became clear that (1) attacks using AI and (2) responses to attacks by AI itself are highly important. Among these, the analysis of attacks by AI itself, using an attack tree, revealed that the following measures are needed: (a) establishment of penalties for developing inappropriate programs, (b) introduction of a reporting system for signs of attacks by AI, (c) measures to prevent AI revolt by incorporating Asimov s three principles of robotics, and (d) establishment of a mechanism to prevent AI from attacking humans even when it becomes confused.

We propose a conceptual framework, named "AI Security Continuum," consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension.

In recent years, machine learning technology has been extensively utilized, leading to increased attention to the security of AI systems. In the field of image recognition, an attack technique called clean-label backdoor attack has been widely studied, and it is more difficult to detect than general backdoor attacks because data labels do not change when tampering with poisoning data during model training. However, there remains a lack of research on malware detection systems. Some of the current work is under the white-box assumption that requires knowledge of machine learning-based models which can be advantageous for attackers. In this study, we focus on clean-label backdoor attacks in malware detection systems and propose a new clean-label backdoor attack under the black-box assumption that does not require knowledge of machine learning-based models, which is riskier. The experimental evaluation of the proposed attack method shows that the attack success rate is up to 80.50\% when the poisoning rate is 14.00\%, demonstrating the effectiveness of the proposed attack method. In addition, we experimentally evaluated the effectiveness of the dimensionality reduction techniques in preventing clean-label backdoor attacks, and showed that it can reduce the attack success rate by 76.00\%.

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

AI is one of the most popular field of technologies nowadays. Developers implement these technologies everywhere forgetting sometimes about its robustness to unobvious types of traffic. This omission can be used by attackers, who are always seeking to develop new attacks. So, the growth of AI is highly correlates with the rise of adversarial attacks. Adversarial attacks or adversarial machine learning is a technique when attackers attempt to fool ML systems with deceptive data. They can use inconspicuous, natural-looking perturbations in input data to mislead neural networks without inferring into a model directly and often without the risk to be detected. Adversarial attacks usually are divided into three primary axes - the security violation, poisoning and evasion attacks, which further can be categorized on “targeted”, “untargeted”, “whitebox” and “blackbox” types. This research examines most of the adversarial attacks are known by 2023 relating to all these categories and some others.

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.

ChatGPT, a conversational Artificial Intelligence, has the capacity to produce grammatically accurate and persuasively human responses to numerous inquiry types from various fields. Both its users and applications are growing at an unbelievable rate. Sadly, abuse and usage often go hand in hand. Since the words produced by AI are nearly comparable to those produced by humans, the AI model can be used to influence people or organizations in a variety of ways. In this paper, we test the accuracy of various online tools widely used for the detection of AI-generated and Human generated texts or responses.

With the increasing deployment of machine learning models across various domains, ensuring AI security has become a critical concern. Model evasion, a specific area of concern, involves attackers manipulating a model s predictions by perturbing the input data. The Fast Gradient Sign Method (FGSM) is a well-known technique for model evasion, typically used in white-box settings where the attacker has direct access to the model s architecture. In this method, the attacker intelligently manipulates the inputs to cause mispredictions by accessing the gradients of the input. To address the limitations of FGSM in black-box settings, we propose an extension of this approach called FGSM on ZOO. This method leverages the Zeroth Order Optimization (ZOO) technique to intellectually manipulate the inputs. Unlike white-box attacks, black-box attacks rely solely on observing the model s input-output behavior without access to its internal structure or parameters. We conducted experiments using the MNIST Digits and CIFAR datasets to establish a baseline for vulnerability assessment and to explore future prospects for securing models. By examining the effectiveness of FGSM on ZOO in these experiments, we gain insights into the potential vulnerabilities and the need for improved security measures in AI systems

Software vulnerability detection (SVD) aims to identify potential security weaknesses in software. SVD systems have been rapidly evolving from those being based on testing, static analysis, and dynamic analysis to those based on machine learning (ML). Many ML-based approaches have been proposed, but challenges remain: training and testing datasets contain duplicates, and building customized end-to-end pipelines for SVD is time-consuming. We present Tenet, a modular framework for building end-to-end, customizable, reusable, and automated pipelines through a plugin-based architecture that supports SVD for several deep learning (DL) and basic ML models. We demonstrate the applicability of Tenet by building practical pipelines performing SVD on real-world vulnerabilities.

In various fields, such as medical engi-neering or aerospace engineering, it is difficult to apply the decisions of a machine learning (ML) or a deep learning (DL) model that do not account for the vast amount of human limitations which can lead to errors and incidents. Explainable Artificial Intelligence (XAI) comes to explain the results of artificial intelligence software (ML or DL) still considered black boxes to understand their decisions and adopt them. In this paper, we are interested in the deployment of a deep neural network (DNN) model able to predict the Remaining Useful Life (RUL) of a turbofan engine of an aircraft. Shapley s method was then applied in the explanation of the DL results. This made it possible to determine the participation rate of each parameter in the RUL and to identify the most decisive parameters for extending or shortening the RUL of the turbofan engine.

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.

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.

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.

The Internet of Things (IoT) heralds a innovative generation in communication via enabling regular gadgets to supply, receive, and percentage records easily. IoT applications, which prioritise venture automation, aim to present inanimate items autonomy; they promise increased consolation, productivity, and automation. However, strong safety, privateness, authentication, and recuperation methods are required to understand this goal. In order to assemble give up-to-quit secure IoT environments, this newsletter meticulously evaluations the security troubles and risks inherent to IoT applications. It emphasises the vital necessity for architectural changes.The paper starts by conducting an examination of security worries before exploring emerging and advanced technologies aimed at nurturing a sense of trust, in Internet of Things (IoT) applications. The primary focus of the discussion revolves around how these technologies aid in overcoming security challenges and fostering an ecosystem for IoT.

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.