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.

This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted attention because of the emergence of brain-computer interface (BCI) technology, it is difficult to create efficient learning models for EEG analysis because of the distributed nature of EEG data and related privacy and security concerns. To address these challenges, the proposed defending framework leverages the Federated Learning paradigm to preserve privacy by collaborative model training with localized data from dispersed sources and introduces a reputation-based mechanism to mitigate the influence of data poisoning attacks and identify compromised participants. To assess the efficiency of the proposed reputation-based federated learning defense framework, data poisoning attacks based on the risk level of training data derived by Explainable Artificial Intelligence (XAI) techniques are conducted on both publicly available EEG signal datasets and the self-established EEG signal dataset. Experimental results on the poisoned datasets show that the proposed defense methodology performs well in EEG signal classification while reducing the risks associated with security threats.

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.

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

In this work, a novel framework for detecting mali-cious networks in the IoT-enabled Metaverse networks to ensure that malicious network traffic is identified and integrated to suit optimal Metaverse cybersecurity is presented. First, the study raises a core security issue related to the cyberthreats in Metaverse networks and its privacy breaching risks. Second, to address the shortcomings of efficient and effective network intrusion detection (NIDS) of dark web traffic, this study employs a quantization-aware trained (QAT) 1D CNN followed by fully con-nected networks (ID CNNs-GRU-FCN) model, which addresses the issues of and memory contingencies in Metaverse NIDS models. The QAT model is made interpretable using eXplainable artificial intelligence (XAI) methods namely, SHapley additive exPlanations (SHAP) and local interpretable model-agnostic ex-planations (LIME), to provide trustworthy model transparency and interpretability. Overall, the proposed method contributes to storage benefits four times higher than the original model without quantization while attaining a high accuracy of 99.82 \%.

IoT and AI created a Transportation Management System, resulting in the Internet of Vehicles. Intelligent vehicles are combined with contemporary communication technologies (5G) to achieve automated driving and adequate mobility. IoV faces security issues in the next five (5) areas: data safety, V2X communication safety, platform safety, Intermediate Commercial Vehicles (ICV) safety, and intelligent device safety. Numerous types of AI models have been created to reduce the outcome infiltration risks on ICVs. The need to integrate confidence, transparency, and repeatability into the creation of Artificial Intelligence (AI) for the safety of ICV and to deliver harmless transport systems, on the other hand, has led to an increase in explainable AI (XAI). Therefore, the space of this analysis protected the XAI models employed in ICV intrusion detection systems (IDSs), their taxonomies, and available research concerns. The study s findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs. The paper also demonstrates that XAI s greater transparency will help it gain acceptance in the vehicle industry.

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.