With the use of AI and digital forensics, this paper outlines a complete strategy for handling security incidents in the cloud. The research is meant to improve cloud-based security issue detection and response. The results indicate the promise of this integrated strategy, with AI models improving the accuracy of issue detection and digital forensics speeding incident triage. Improved cloud security, proactive threat detection, optimized resource allocation, and conformity with legal and regulatory standards are only some of the practical consequences discussed in the paper. Advanced AI models, automated incident response, human-machine cooperation, threat intelligence integration, adversarial machine learning, compliance and legal issues, and cross-cloud security are all areas the report suggests further investigation into. In sum, this study aids in developing a more proactive and resilient strategy for handling cloud security incidents in a dynamic digital environment

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 boundaries between the real world and the virtual world are going to be blurred by Metaverse. It is transforming every aspect of humans to seamlessly transition from one virtual world to another. It is connecting the real world with the digital world by integrating emerging tech like 5G, 3d reconstruction, IoT, Artificial intelligence, digital twin, augmented reality (AR), and virtual reality (VR). Metaverse platforms inherit many security \& privacy issues from underlying technologies, and this might impede their wider adoption. Emerging tech is easy to target for cybercriminals as security posture is in its infancy. This work elaborates on current and potential security, and privacy risks in the metaverse and put forth proposals and recommendations to build a trusted ecosystem in a holistic manner.

Cloud computing has become increasingly popular in the modern world. While it has brought many positives to the innovative technological era society lives in today, cloud computing has also shown it has some drawbacks. These drawbacks are present in the security aspect of the cloud and its many services. Security practices differ in the realm of cloud computing as the role of securing information systems is passed onto a third party. While this reduces managerial strain on those who enlist cloud computing it also brings risk to their data and the services they may provide. Cloud services have become a large target for those with malicious intent due to the high density of valuable data stored in one relative location. By soliciting help from the use of honeynets, cloud service providers can effectively improve their intrusion detection systems as well as allow for the opportunity to study attack vectors used by malicious actors to further improve security controls. Implementing honeynets into cloud-based networks is an investment in cloud security that will provide ever-increasing returns in the hardening of information systems against cyber threats.

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.

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.

The financial sector is such a world that is constantly under evolution, always with searches for the balance between strong security and the exponential increase of digital customer-centric operations. Here is where Albiometric recognition steps in, possibly contributing at the same time to efficiency and security for bank transactions. Therefore, this paper will help in studying the incorporation of AI in biometric authentication for banking activities and its impact from these dual perspectives. From the efficiency front, the paper will study how AI can make the user experience effective. This biometric recognition can be, for example, through fingerprint scanning or facial recognition, which will, at the These operations could be further streamlined with AI learning and adapting to user behavior, and even predicting actions and pre-populating transaction details. The traditional means of authentication, including passwords, are easily susceptible to phishing attacks and bruteforce hacking. On the other hand, this biometric data is unique for every individual and hence is more secure. AI can further cement this security by keeping an eye all the time for any anomaly or spoofing attempts in the biometric data. It means that machine-learning algorithms can identify even the slightest difference in fingerprints, facial features, or voice patterns, which human experts might oversee, and would provide a great help for security.

Generative AI technology is being applied in various fields. However, the advancement of these technologies also raises cybersecurity issues. In fact, there are cases of cyber attack using Generative AI, and the number is increasing. Therefore, this paper analyzes the potential cybersecurity issues associated with Generative AI. First, we looked at the fields where Generative AI is used. Representatively, Generative AI is being used in text, image, video, audio, and code. Based on these five fields, cybersecurity issues that may occur in each field were analyzed. Finally, we discuss the obligations necessary for the future development and use of Generative AI.

With the rapid advancement of technology and the expansion of available data, AI has permeated many aspects of people s lives. Large Language Models(LLMs) such as ChatGPT are increasing the accuracy of their response and achieving a high level of communication with humans. These AIs can be used in business to benefit, for example, customer support and documentation tasks, allowing companies to respond to customer inquiries efficiently and consistently. In addition, AI can generate digital content, including texts, images, and a wide range of digital materials based on the training data, and is expected to be used in business. However, the widespread use of AI also raises ethical concerns. The potential for unintentional bias, discrimination, and privacy and security implications must be carefully considered. Therefore, While AI can improve our lives, it has the potential to exacerbate social inequalities and injustices. This paper aims to explore the unintended outputs of AI and assess their impact on society. Developers and users can take appropriate precautions by identifying the potential for unintended output. Such experiments are essential to efforts to minimize the potential negative social impacts of AI transparency, accountability, and use. We will also discuss social and ethical aspects with the aim of finding sustainable solutions regarding AI.

The 2023 CS curriculum by ACM, IEEE, and AAAI identifies security as an independent knowledge area that develops the “security mindset” so that students are ready for the “continual changes” in computing. Likewise, the curriculum emphasises the coverage of “uses”, and “shortcomings/pitfalls” of practical AI-tools like ChatGPT. This paper presents our endeavors to approach those goals with the design of an Information Security course. Our course design bears the following distinct features: Certificate-readiness, where we align the knowledge areas with major security/ethical hacking certificates; Coverage of ChatGPT, where the uses of ChatGPT for assisting security tasks and security issues caused by ChatGPT usage are both addressed for the first time in the teaching; “Learn defending from attackers perspective”, where labs of both offensive and defensive natures are developed to equally sharpen ethical hacking and hardening skills, and to facilitate the discussion on legal/ethical implications; Current and Representative, where ajust-enough set of representative and/or current security topics are selected in order and covered in respective modules in the most current form. In addition, we generalize our design principles and strategies, with the hope to shed lights on similar efforts in other institutions.

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 network of smart physical object has a significant impact on the growth of urban civilization. The evidence has been cited from the digital sources such as scientific journals, conferences and publications, etc. Along with other security services, these kinds of structured, sophisticated data have addressed a number of security-related challenges. Here, many forms of cutting-edge machine learning and AI techniques are used to research how merging two or more algorithms with AI and ML might make the internet of things more safe. The main objective of this paper is it explore the applications of how ML and AI that can be used to improve IOT security.

Artificial Intelligence (AI) and Machine Learning (ML) models, while powerful, are not immune to security threats. These models, often seen as mere data files, are executable code, making them susceptible to attacks. Serialization formats like .pickle, .HDF5, .joblib, .ONNX etc. commonly used for model storage, can inadvertently allow arbitrary code execution, a vulnerability actively exploited by malicious actors. Furthermore, the execution environment for these models, such as PyTorch and TensorFlow, lacks robust sandboxing, enabling the creation of computational graphs that can perform I/O operations, interact with files, communicate over networks, and even spawn additional processes, underscoring the importance of ensuring the safety of the code executed within these frameworks. The emergence of Software Development Kits (SDKs) like ClearML, designed for tracking experiments and managing model versions, adds another layer of complexity and risk. Both open-source and enterprise versions of these SDKs have vulnerabilities that are just beginning to surface, posing additional challenges to the security of AI/ML systems. In this paper, we delve into these security challenges, exploring attacks, vulnerabilities, and potential mitigation strategies to safeguard AI and ML deployments.

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.

With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59\% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model s identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.

6G networks are beginning to take shape, and it is envisaged that they will be made up of networks from different vendors, and with different technologies, in what is known as the network-of-networks. The topology will be constantly changing, allowing it to adapt to the capacities available at any given moment. 6G networks will be managed automatically and natively by AI, but allowing direct management of learning by technical teams through Explainable AI. In this context, security becomes an unprecedented challenge. In this paper we present a flexible architecture that integrates the necessary modules to respond to the needs of 6G, focused on managing security, network and services through choreography intents that coordinate the capabilities of different stakeholders to offer advanced services.

The effective use of artificial intelligence (AI) to enhance cyber security has been demonstrated in various areas, including cyber threat assessments, cyber security awareness, and compliance. AI also provides mechanisms to write cybersecurity training, plans, policies, and procedures. However, when it comes to cyber security risk assessment and cyber insurance, it is very complicated to manage and measure. Cybersecurity professionals need to have a thorough understanding of cybersecurity risk factors and assessment techniques. For this reason, artificial intelligence (AI) can be an effective tool for producing a more thorough and comprehensive analysis. This study focuses on the effectiveness of AI-driven mechanisms in enhancing the complete cyber security insurance life cycle by examining and implementing a demonstration of how AI can aid in cybersecurity resilience.

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.

The use of artificial intelligence (AI) in cyber security [1] has proven to be very effective as it helps security professionals better understand, examine, and evaluate possible risks and mitigate them. It also provides guidelines to implement solutions to protect assets and safeguard the technology used. As cyber threats continue to evolve in complexity and scope, and as international standards continuously get updated, the need to generate new policies or update existing ones efficiently and easily has increased [1] [2].The use of (AI) in developing cybersecurity policies and procedures can be key in assuring the correctness and effectiveness of these policies as this is one of the needs for both private organizations and governmental agencies. This study sheds light on the power of AI-driven mechanisms in enhancing digital defense procedures by providing a deep implementation of how AI can aid in generating policies quickly and to the needed level.

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.

Artificial intelligence (AI) has emerged as one of the most formative technologies of the century and further gains importance to solve the big societal challenges (e.g. achievement of the sustainable development goals) or as a means to stay competitive in today’s global markets. The role as a key enabler in many areas of our daily life leads to a growing dependence, which has to be managed accordingly to mitigate negative economic, societal or privacy impacts. Therefore, the European Union is working on an AI Act, which defines concrete governance, risk and compliance (GRC) requirements. One of the key demands of this regulation is the operation of a risk management system for High-Risk AI systems. In this paper, we therefore present a detailed analysis of relevant literature in this domain and introduce our proposed approach for an AI Risk Management System (AIRMan).

Despite the tremendous impact and potential of Artificial Intelligence (AI) for civilian and military applications, it has reached an impasse as learning and reasoning work well for certain applications and it generally suffers from a number of challenges such as hidden biases and causality. Next, “symbolic” AI (not as efficient as “sub-symbolic” AI), offers transparency, explainability, verifiability and trustworthiness. To address these limitations, neuro-symbolic AI has been emerged as a new AI field that combines efficiency of “sub-symbolic” AI with the assurance and transparency of “symbolic” AI. Furthermore, AI (that suffers from aforementioned challenges) will remain inadequate for operating independently in contested, unpredictable and complex multi-domain battlefield (MDB) environment for the foreseeable future and the AI enabled autonomous systems will require human in the loop to complete the mission in such a contested environment. Moreover, in order to successfully integrate AI enabled autonomous systems into military operations, military operators need to have assurance that these systems will perform as expected and in a safe manner. Most importantly, Human-Autonomy Teaming (HAT) for shared learning and understanding and joint reasoning is crucial to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed with high assurance and trust. In this paper, we present a rough guide to key research challenges and perspectives of neuro symbolic AI for assured and trustworthy HAT.

Unsupervised cross-domain NER task aims to solve the issues when data in a new domain are fully-unlabeled. It leverages labeled data from source domain to predict entities in unlabeled target domain. Since training models on large domain corpus is time-consuming, in this paper, we consider an alternative way by introducing syntactic dependency structure. Such information is more accessible and can be shared between sentences from different domains. We propose a novel framework with dependency-aware GNN (DGNN) to learn these common structures from source domain and adapt them to target domain, alleviating the data scarcity issue and bridging the domain gap. Experimental results show that our method outperforms state-of-the-art methods.

In the context of increasing digitalization and the growing reliance on intelligent systems, the importance of network information security has become paramount. This study delves into the exploration of network information security technologies within the framework of a digital intelligent security strategy. The aim is to comprehensively analyze the diverse methods and techniques employed to ensure the confidentiality, integrity, and availability of digital assets in the contemporary landscape of cybersecurity challenges. Key methodologies include the review and analysis of encryption algorithms, intrusion detection systems, authentication protocols, and anomaly detection mechanisms. The investigation also encompasses the examination of emerging technologies like blockchain and AI-driven security solutions. Through this research, we seek to provide a comprehensive understanding of the evolving landscape of network information security, equipping professionals and decision-makers with valuable insights to fortify digital infrastructure against ever-evolving threats.

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.