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.

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.

The rise of social media has enabled individuals with biased perspectives to spread hate speech, directing it toward individuals based on characteristics such as race, gender, religion, or sexual orientation. Constructive interactions in varied communities can greatly enhance self-esteem, yet it is vital to consider that adverse comments may affect individuals’ social standing and emotional health. The crucial task of detecting and addressing this type of content is imperative for reducing its negative effects on communities and individuals alike. The rising occurrence highlights the urgency for enhanced methods and robust regulations on digital platforms to protect humans from such prejudicial and damaging conduct. Hate speech typically appears as a deliberate hostile action aimed at a particular group, often with the intent to demean or isolate them based on various facets of their identity. Research on hate speech predominantly targets resource-aware languages like English, German, and Chinese. Conversely, resource-limited languages, including European languages such as Italian, Spanish, and Portuguese, alongside Asian languages like Roman Urdu, Korean, and Indonesian, present obstacles. These challenges arise from a lack of linguistic resources, making the extraction of information a more strenuous task. This study is focused on the detection and improvement of multilingual hate speech detection across 13 different languages. To conduct a thorough analysis, we carried out a series of experiments that ranged from classical machine learning techniques and mainstream deep learning approaches to recent transformer-based methods. Through hyperparameter tuning, optimization techniques, and generative configurations, we achieved robust and generalized performance capable of effectively identifying hate speech across various dialects. Specifically, we achieved a notable enhancement in detection performance, with precision and recall metrics exceeding baseline models by up to 10% across several lesser-studied languages. Additionally, our work extends the capabilities of explainable AI within this context, offering deeper insights into model decisions, which is crucial for regulatory and ethical considerations in AI deployment. Our study presents substantial performance improvements across various datasets and languages through meticulous comparisons. For example, our model significantly outperformed existing benchmarks: it achieved F1-scores of 0.90 in German (GermEval-2018), up from the baseline score of 0.72, and 0.93 in German (GermEval-2021), a substantial increase from 0.58. Additionally, it scored 0.95 in Roman Urdu HS, surpassing the previous peak of 0.91. Furthermore, for mixed-language datasets such as Italian and English (AMI 2018), our accuracy rose dramatically from 0.59 to 0.96. These outcomes emphasize the robustness and versatility of our model, establishing a new standard for hate speech detection systems across diverse linguistic settings.

Internet of Things (IoT) devices are becoming a part of our daily life; from health monitors to critical infrastructure, they are used everywhere. This makes them ideal targets for malicious actors to exploit for nefarious purposes. Recent attacks like the Mirai botnet are just examples in which default credentials were used to exploit thousands of devices. This raises major concerns about IoT device security. In this work, we aimed to investigate security of IoT devices through performing automatic penetration test on IoT devices. A penetration test is a way of detecting security problems, but manually testing billions of IoT devices is infeasible. This work has therefore examined autonomous penetration testing on IoT devices. In recent studies, automated attack execution models were developed for modeling automated attacks in cyber ranges. We have (1) investigated how such models can be applied for performing autonomous IoT penetration testing. Furthermore, we have (2) investigated if some well known and severe Wi-Fi related vulnerabilities still exist in IoT devices. Through a case study, we have shown that the such models can be used to model and design autonomous penetration testing agents for IoT devices. In addition, we have demonstrated that well-known vulnerabilities are present in deployed and currently sold products used in IoT devices, and that they can be both autonomously revealed through our developed system.