The fake news phenomenon has a significant impact on societies, homeland security, democracy and the functioning of the public space. The spread of false information is becoming an increasing challenge in the context of the dynamic growth of the volume of content shared by news outlets and social media. The overwhelming amount of this information makes manual verification of every news item or press release practically impossible. The current development of technology in the field of natural language processing (NLP) opens up new possibilities for the development of automatic content verification systems. The automation of this process not only improves but also significantly speeds up the detection of unreliable information, which is a key tool in the fight against fake news. In this article, we propose an innovative approach that involves a multi-factor assessment of the content of documents, as opposed to the frequently used approach of binary classification into fake and non-fake. Our classification system is based on analysis using graph neural networks, which allows for a more complex and contextual understanding of the data. The obtained results indicate a significant improvement in effectiveness compared to the baseline approaches, which suggests a potential for enhanced mitigation of misinformation dissemination.

Deep neural networks have revolutionized artificial intelligence, solving complex issues in areas like healthcare or law enforcement and security. However, they are susceptible to adversarial attacks where small data manipulations can compromise system reliability and security. This paper conducts an umbrella review of the literature on these attacks, synthesizing results from various systematic reviews to assess attack strategies, defense effectiveness, and research gaps. Guided by the PICO framework, this review categorizes and examines adversarial attacks, identifying key challenges in the field. The review finds that even though adversarial vulnerabilities were first explored in computer vision, analogous threats have expanded to domains like graph neural networks, natural language processing, federated learning, and text-to-image models. Despite varied attack surfaces, commonalities can be found. • First umbrella review synthesising systematic reviews and meta-analyses of adversarial attacks on deep neural networks, including the emerging threat to diffusion-based generative models. • PICO-driven framework addressing three research questions: (1) mapping survey themes and methods, (2) comparing domain-specific attack strategies, (3) identifying universal adversarial characteristics. • Comprehensive taxonomy covering gradient-based, transfer-based, score-based, decision-based, black-box, poisoning, privacy, and universal adversarial attacks. • Domain-specific analysis across computer vision, natural language processing, graph neural networks, intrusion detection systems, federated learning, GANs/VAEs, and text-to-image models like Stable Diffusion.

This paper presents a study of the effect of the size of the time window from which network features are derived on the predictive ability of a Random Forest classifier implemented as a network intrusion detection component. The network data is processed using granular computing principles, gradually increasing the time windows to allow the detection algorithm to find patterns in the data at different levels of granularity. Experiments were conducted iteratively with time windows ranging in size from 2 to 1024 seconds. Each iteration involved time-based clustering of the data, followed by splitting into training and test sets at a ratio of 67% - 33%. The Random Forest algorithm was applied as part of a 10-fold cross-validation. Assessments included standard detection metrics: accuracy, precision, F1 score, BCC, MCC and recall. The results show a statistically significant improvement in the detection of cyber attacks in network traffic with a larger time window size (p-value 0.001953125). These results highlight the effectiveness of using longer time intervals in network data analysis, resulting in increased anomaly detection.

This paper introduces an innovative tool equipped with a multi-perspective, user-friendly dashboard designed to enhance the explainability of AI models, particularly in cybersecurity. By enabling users to select data samples and apply various xAI methods, the tool provides insightful views into the decision-making processes of AI systems. These methods offer diverse perspectives and deepen the understanding of how models derive their conclusions, thus demystifying the "black box" of AI. The tool’s architecture facilitates easy integration with existing ML models, making it accessible to users regardless of their technical expertise. This approach promotes transparency and fosters trust in AI applications by aligning decision-making with domain knowledge and mitigating potential biases.

The emergence and consecutive entrance of Generative AI (particularly ChatGPT) into the mainstream has provoked all kinds of reactions, from excitement to apprehension, but it has not been definitely decided whether it is a boon or a bane yet. We wish to voice the still unmentioned relation between AI accessibility and social injustice. So far, the initial access to tools such as ChatGPT has been free or low-cost. This is predicated on the availability of open-source or inexpensively sourced data. As the value of models hinges upon high quality, diverse data, the demand for it will increase, resulting in the rising costs of its procuration. We worry that the free models will then turn into expensive commodities, limiting their use only to the privileged entities. This potential shift causes major concerns about ethics and social equity, with the concept of unbundling being one of the potential solutions.

The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable and ethical AI solutions in numerous domains. xAI is now the subject of extensive research, from both the technical and social science perspectives. It is being received enthusiastically by legislative bodies and regular users of machine-learning-boosted applications alike. However, opening the black box of AI comes at a cost. This paper presents the results of the first study proving that xAI can enable successful adversarial attacks in the domain of fake news detection and lead to a decrease in AI security. We postulate the novel concept that xAI and security should strike a balance, especially in critical applications, such as fake news detection. An attack scheme against fake news detection methods is presented that employs an explainable solution. The described experiment demonstrates that the well-established SHAP explainer can be used to reshape the structure of the original message in such a way that the value of the model's prediction could be arbitrarily forced, whilst the meaning of the message stays the same. The paper presents various examples for which the SHAP values are used to point the adversary to the words and phrases that have to be changed to flip the label on the model prediction. To the best of the authors' knowledge, it has been the first research work to experimentally demonstrate the sinister side of xAI. As the generation and spreading of fake news has become a tool of modern warfare and a grave threat to democracy, the potential impact of explainable AI should be addressed as soon as possible.

This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial Intelligence, with a specific focus on its implications for cybersecurity. Firstly, the paper gives an overview of xAI techniques and their significance and benefits when applied in cybersecurity. Subsequently, the authors methodically delineate their systematic mapping study, which serves as an investigative tool for discerning the potential trajectory of the field. This strategic methodological framework lets one identify the future research directions and opportunities that underlie the integration of xAI within the realm of Deep Learning, Artificial Intelligence, and cybersecurity, which are described in-depth. Then, the paper brings together all the gathered insights from this extensive investigation and closes with final conclusions.

This paper investigates the specific properties of Explainable Artificial Intelligence (xAI), particularly when implemented in AI/ML models across high-stakes sectors, in this case cybersecurity. The authors execute a comprehensive systematic review of xAI properties, various evaluation metrics, and existing frameworks to assess their utility and relevance. Subsequently, the experimental sections evaluate selected xAI techniques against these metrics, delivering key insights into their practical utility and effectiveness. The findings highlight that the proliferation of metrics enhances the understanding of xAI systems but simultaneously exposes challenges such as metric duplication, inefficacy, and confusion. These issues underscore the pressing need for standardized evaluation frameworks to streamline their application and strengthen their effectiveness, thereby improving the overall utility of xAI in critical domains.

Recently, various artificial intelligence (AI)-based methods have been proposed to support humans in detecting disinformation and fake news. The goal of this article is to provide a meta-analysis, and formally evaluate, compare, and benchmark various classes of fake news detection approaches. To this end, the following paper performs a comprehensive analysis of the performance-related results of different models using a range of benchmark datasets. The performed and disclosed meta-analysis compares the statistical significance of differences in a range of performance metrics, including precision,$F1$-score, recall, and balanced accuracy (BACC). The utilized approach features the$5$$\times$$2$cross-validation methodology. The models undergoing the formal evaluation constitute state-of-the-art (SOTA) solutions meeting acceptance criteria. The evaluated approaches draw from the most recent advancements in natural language processing (NLP). The outcome of this work is the formal benchmarking and meta-analysis of fake news detection methods that can be further utilized by the research community, but more importantly by the practitioners and decision-makers that counter fake news on a daily basis, e.g., in press agencies, homeland security agencies, fact-checkers, and so on. This work is the natural extension of the authors’ previous systematic analysis of fake news detection methods and authors’ own fake news detection methods based on machine learning (ML)/artificial intelligence (AI) techniques.

It is important to realize that false news is more than just a deception. Sadly, it is impossible to confirm every bit of information we come across. A normal human impulse is to accept any information that looks sufficiently convincing, relevant, or exciting. In doing so, we often do not realize that we have just contributed to the misinformation of the community to which we belong. As a result, fake news happens to be our collective error. In this paper, we propose an architecture for combating the disinformation problem using a hybrid-based approach. We demonstrate our preliminary results on the health-related fake news dataset.

Nowadays, we can see how social media networks are developing. We must accept the fact that the opinion of an expert is frequently just as valuable and crucial as that of a non-expert. It is feasible to see how traditional media is undergoing changes and processes that diminish the importance of the traditional ”editing office” and place a growing focus on journalists’ remote labour.As a result, social media has evolved into a component of national security since fake news and disinformation spread by nefarious individuals can influence readers and spark pointless debates on social issues that are inherently unimportant. This has a domino effect, instils dread in the populace, and eventually puts the security of the state in jeopardy.Recently, deep machine learning techniques have proven to be one of the technologies thought to be an effective way to combat the false news problem. However, due to shortages of labelled data, these methods often have poor model generalization capabilities when applied in real-world cases.In this paper, we address this problem by utilizing lightweight model stitching, which serves as a foundation for a hybrid method for fake news detection. Six distinct benchmark datasets have been used in our varied experiments. The outcomes are promising and pave the way for additional studies.

Nowadays, cloud technology is assuming immense significance, being treated as a critical infrastructure, and is also a buzzword. Nevertheless, the technology has also brought about a number of new adverse phenomena and threats; it has attracted criminals, as well. Whenever the questions of “good” and “bad” arise, the ethical issues arise alongside them; the cybersecurity of cloud technology is no exception. This paper deals with the ethical dilemmas of cloud technology. It discusses a collection of the ethical issues of the cloud technology presented from the perspective of cybersecurity, based on the state-of-the-art literature. The main contribution of this work is that it gathers, synthesizes and organises the cybersecurity-related ethical dilemmas of cloud technology, thus offering the most extensive collection thereof. In addition, the work presents a comprehensive list of recommendations and suggestions which may help solve or prevent these ethical issues, and are a good starting point for anyone designing an ethical cybersecurity strategy.

Abstract The development of cyberspace has brought about innumerable advantages for the mankind. However, it also came with several serious drawbacks; as cyberspace evolves, so does cybercrime. Since the birth of cyberspace, individuals, groups and whole nations have been engaging in computer-related offences of various significance and impact, trying to exploit systems’ vulnerabilities, disseminate malicious software and steal data or funds. The concept of a hacker has entered the collective consciousness and become an intrinsic element of popular culture. However, there are hackers, or rather, cyberspace actors, who challenge this common view. This paper presents three types of such people, namely hacktivists, members of cyber militias and Internet trolls. Although they all use the Internet to break the laws or rules, their internal motivations are not always utterly sinister; actually, some of them firmly believe that their actions are for the greater good. This paper is structured as follows: Firstly, the general profile of a hacker is presented. Then, the state of the art is outlined, concerning other papers dealing with the motivations behind cyber threat actors. Following that, the three aforementioned groups of cyberspace actors are contrasted with the profile of a ‘typical’ hacker. Then, the profiles of a typical representative for each of the group and their motivations are indicated, followed by the final conclusions.

Despite its development having changed and improved citizens' lives, cyberspace has also become a new arena for competition among states, organizations and individuals, and various cyber threats to people's security are becoming more prevalent, damaging and complex. Although it is rather commonly known that cyberspace is a battlefield, and almost every individual, organization or even state may fall victim to malicious hackers or greedy cybercriminals, the members of the public rarely seem to think of other sources of threat. Thus, in an attempt to raise the general awareness, this paper presents an additional number of selected, often unsuspected actors that shape and influence the cyberspace of today: nation-state actors, cyberterrorists, hacktivists and trolls. The motives of each actor, their modus operandi and the most significant representatives have also been discussed. Being aware of the existence and nature of each actor helps one better understand the threat they pose, as well as grasp the significance of the cybersecurity measures.