The widespread propagation of misinformation on social media platforms poses a significant concern, prompting substantial endeavors within the research community to develop robust detection solutions. Individuals often place unwavering trust in social networks, often without discerning the origins and authenticity of the information disseminated through these platforms. Hence, the identification of media-rich fake news necessitates an approach that adeptly leverages multimedia elements and effectively enhances detection accuracy. The ever-changing nature of cyberspace highlights the need for measures that may effectively resist the spread of media-rich fake news while protecting the integrity of information systems. This study introduces a robust approach for fake news detection, utilizing three publicly available datasets: WELFake, FakeNewsNet, and FakeNewsPrediction. We integrated FastText word embeddings with various Machine Learning and Deep Learning methods, further refining these algorithms with regularization and hyperparameter optimization to mitigate overfitting and promote model generalization. Notably, a hybrid model combining Convolutional Neural Networks and Long Short-Term Memory, enriched with FastText embeddings, surpassed other techniques in classification performance across all datasets, registering accuracy and F1-scores of 0.99, 0.97, and 0.99, respectively. Additionally, we utilized state-of-the-art transformer-based models such as BERT, XLNet, and RoBERTa, enhancing them through hyperparameter adjustments. These transformer models, surpassing traditional RNN-based frameworks, excel in managing syntactic nuances, thus aiding in semantic interpretation. In the concluding phase, explainable AI modeling was employed using Local Interpretable Model-Agnostic Explanations, and Latent Dirichlet Allocation to gain deeper insights into the model’s decision-making process.

This study proposes a novel approach leveraging Large Language Models (LLMs) to generate dynamic and complex adaptable cybersecurity exercise scenarios. Motivated by Turing’s seminal exploration into machine cognition, which questions the ability of machines to mimic human thought and intelligence. By exploiting the generative potential of LLMs, our methodology simulates a wide range of cyber threats, both known and novel, thereby enhancing cybersecurity training and awareness. This approach transforms the potential for ’hallucination’ inherent in LLMs into a potential advantage, enabling the creation of complex exercise scenarios that push the boundaries of traditional cybersecurity training. The innovation lies in the sophisticated application of AI, aiming to advance the preparedness of security professionals against diverse cyber threats. The scenarios generated through this method were subject to meticulous testing and a rigorous evaluation process involving (Generated Pre-Trained Transformer) GPT models and expert review to ensure their realism and applicability. In this paper, we introduce ’CyExec,’ a novel approach leveraging GPT to dynamically generate cybersecurity training scenarios. Furthermore, the prompts provided to the LLMs were meticulously designed to adopt a Retrieval-Augmented Generation (RAG) approach, enriching the complexity and relevance of the scenarios. This incorporation of RAG, alongside the inspiration drawn from Turing’s exploration of machine intelligence, showcases an advanced application of AI in cybersecurity training, reflecting a deep understanding of how machines can augment our capabilities to anticipate and mitigate cyber threats.

In recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.

This survey paper explores the transformative role of Artificial Intelligence (AI) in information security. Traditional methods, especially rule-based approaches, faced significant challenges in protecting sensitive data from ever-changing cyber threats, particularly with the rapid increase in data volume. This study thoroughly evaluates AI’s application in information security, discussing its strengths and weaknesses. It provides a detailed review of AI’s impact on information security, examining various AI algorithms used in this field, such as supervised, unsupervised, and reinforcement learning, and highlighting their respective strengths and limitations. The study identifies key areas for future AI research in information security, focusing on improving algorithms, strengthening information security, addressing ethical issues, and exploring safety and security-related concerns. It emphasizes significant security risks, including vulnerability to adversarial attacks, and aims to enhance the robustness and reliability of AI systems in protecting sensitive information by proposing solutions for potential threats. The findings aim to benefit cybersecurity professionals and researchers by offering insights into the intricate relationship between AI, information security, and emerging technologies.

In the ever-changing world of business, gaining valuable insights from customer perspectives is crucial. Consumer evaluations are crucial performance indicators for businesses seeking to enhance their impact. Cyberspace is expanding with an increasing volume of reviews, making it challenging to extract relevant information for desired products. This research explores sentiment analysis for Amazon product reviews in the domain of communication technology, utilizing four publicly available datasets. Sentiment analysis is frequently employed to support E-Commerce platforms in monitoring customer feedback on their products and striving to understand customer needs and preferences. Acknowledging that solely relying on user reviews is insufficient to achieve the best performance, we enhance our approach by incorporating additional context from product titles and headlines for a more comprehensive understanding of the learning algorithm. This paper utilizes three distinct embedding methods, including TF-IDF, Word2Vec, and FastText. FastText outperformed other embeddings when stacked with XGBoost and CatBoost, resulting in the FastXCatStack model. This model achieved accuracy scores of 0.93, 0.93, and 0.94 on mobile electronics, major appliances, and personal care appliances datasets respectively, and linear SVM showed an accuracy score of 0.91 on software reviews when combined with FastText. This research study also provides a comprehensive analysis of deep learning-based models, including approaches like LSTM, GRU, and convolutional neural networks as well as transformer-based models such as BERT, RoBERTa, and XLNET. In the concluding phase, interpretability modeling was applied using Local Interpretable Model-Agnostic Explanations and Latent Dirichlet Allocation to gain deeper insights into the model’s decision-making process.

The growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people’s reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model’s decision-making mechanisms.