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Email phishing is a social engineering scheme that uses spoofed emails intended to trick the user into disclosing legitimate business and personal credentials. Many phishing email detection techniques exist based on machine learning, deep learning, and word embedding. In this paper, we propose a new technique for the detection of phishing emails using word embedding (Word2Vec, FastText, and TF-IDF) and deep learning techniques (DNN and BiLSTM network). Our proposed technique makes use of only four header based (From, Returnpath, Subject, Message-ID) features of the emails for the email classification. We applied several word embeddings for the evaluation of our models. From the experimental evaluation, we observed that the DNN model with FastText-SkipGram achieved an accuracy of 99.52% and BiLSTM model with FastText-SkipGram achieved an accuracy of 99.42%. Among these two techniques, DNN outperformed BiLSTM using the same word embedding (FastText-SkipGram) techniques with an accuracy of 99.52%. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet has led to an increased demand for online services. However, this surge in online activity has also brought about a new threat: phishing attacks. Phishing is a type of cyberattack that utilizes social engineering techniques and technological manipulations to steal crucial information from unsuspecting individuals. Consequently, there is a rising necessity to create dependable phishing URL detection models that can effectively identify phishing URLs with enhanced accuracy and reduced prediction overhead. This study introduces XAI-PhD, an innovative phishing detection method that utilizes machine learning (ML) and Shapley additive explanation (SHAP) capabilities. Specifically, XAI-PhD utilizes SHAP to thoroughly analyze the significance of each feature in influencing the decision-making process of the classifier. By selectively incorporating input characteristics based on their SHAP values, only the most crucial attributes are assessed, enabling the development of a highly adaptable and generalized model. XAI-PhD utilizes a lightweight gradient boosting machine as its classifier, and a series of rigorous tests are conducted to assess its performance compared to established baseline methods. The empirical findings unequivocally demonstrate the exceptional effectiveness of XAI-PhD, as evidenced by its remarkable accuracy and F1-score of 99.8% and 99%, respectively. Moreover, XAI-PhD exhibits high computational efficiency, requiring only 1.47 milliseconds and 18.5 microseconds per record to generate accurate predictions. [ABSTRACT FROM AUTHOR]

Mobile texting has increased social engineering assaults like phishing. Because spam, or unsolicited text messages, spread phishing attempts that steal personal information. Traditional methods of spam detection, often based on statistical models or human rule-based systems, have difficulties in keeping up with the growing complexity of spamming strategies. Gathering pertinent data from social networks is a challenging task, mostly due to the limits imposed by privacy concerns and time constraints. The inefficiency and time-consuming nature of conventional frequency-based techniques to word encoding are generally recognized. Text classification has shown promising outcomes with the use of word embeddings and deep learning techniques. The proposed approach involves integrating deep learning with the Remora optimization algorithm framework (DL–ROA) to autonomously extract intricate patterns and nuanced information from text messages. The system's capacity to adapt to new spamming strategies enhances the DL–ROA. The proposed technique improves the accuracy of detection while reducing the inefficiency and time required to create contextual word vectors based on word frequency. Spam detection is achieved by using a hybrid deep model that combines long short-term memory (LSTM) and deep convolutional neural networks (DCNN) architectures. Empirical data demonstrate that the DL–ROA technique surpasses existing deep learning models in terms of accuracy, f1-score, and recall. In addition, the DL–ROA achieved an unprecedented accuracy rate of 98.25%. [ABSTRACT FROM AUTHOR]

Phishing attacks are increasing every year, both in terms of number and technique. Using only human weaknesses, an attacker can easily obtain the victim's credentials or access their network. The problem persists despite many approaches offered by researchers, due to its dynamic nature, in which new phishing tactics are created every time. We, therefore, need more robust and effective methods to detect phishing emails. In this paper, we aim to detect phishing emails using the body text of the email with the hybrid approach combining case-based reasoning (CBR) and a deep learning model. Our proposed model, called DL-CBR, consists of a Bidirectional Long Short-Term Memory (Bi-LSTM) + Temporal Convolutional Network (TCN) network with an attention mechanism followed by a CBR classifier. The deep learning model is used for email representation, where it is trained using the N -pair loss function. To demonstrate the performance of DL-CBR, evaluation metrics, such as precision, accuracy, recall, and F-measure, were used, where we obtained an accuracy of 98.28%. The results show that our model outperformed other CBRs that utilize classical text representations like TF-IDF and Bag-of-Words. Additionally, while our model's performance is slightly below that of the state-of-the-art models, it offers several advantages inherent to CBR. For instance, it can learn from new cases and update their database accordingly. [ABSTRACT FROM AUTHOR]

As technology advances and develops, the need for strong and simple authentication mechanisms that can help protect data intensifies. The contemporary approach to giving access control is through graphical passwords comprising images, patterns, or graphical items. The objective of this review was to determine the documented security risks that are related to the use of graphical passwords, together with the measures that have been taken to prevent them. The review was intended to present an extensive literature review of the subject matter on graphical password protection and to point toward potential future research directions. Many attacks, such as shoulder surfing attacks, SQL injection attacks, and spyware attacks, can easily exploit the graphical password scheme, which is one of the most widely used. To counter these security threats, several measures have been suggested, but none of the security attacks can be completely overcome. Each of the proposed measures has its pros and cons. This study begins by elucidating some of the graphical password schemes studied between 2012 and 2023, delving into potential threats and defense mechanisms associated with these schemes. Following a thorough identification and selection process, five of the reviewed papers explain the threat of shoulder surfing and spyware attacks on graphical password schemes, while two explain the threat of brute force attacks. One paper focuses on dictionary attacks, while four other papers address social engineering, SQL injection attacks, and guessing attacks as potential threats to graphical password schemes. In addition, the papers recognize other forms of attacks, such as video recording attacks, filtering attacks, reverse engineering attacks, multiple observation attacks, key/mouse logger attacks, insider attacks, computer vision attacks, image gallery attacks, sonar attacks, reply attacks, data interception attacks, and histogram manipulation attacks. These attacks are examined in three, three, eight, one, four, one, one, one, one, one, one, and one papers, respectively. Moreover, out of all such countermeasures, most of them are based on three categories—randomization, obfuscation, and password space complexity—which are the most commonly employed strategies for improving graphical password schemes. [ABSTRACT FROM AUTHOR]

The so-called "Moldavian language", invented by the Soviets in the 1920s, was just another instrument for the Soviet effort trying to assimilate people and to erase national identities, in order to make room for the "Soviet identity". The Gulags, the massacres, the deportations and assassinations were not enough to "convince" people to embrace the new Soviet identity, the Soviet Communists needed pseudo-science as well. But the so-called "Moldavian language" was more than just another Soviet social engineering, it was an integrate part of a geopolitical strategy aimed at dismembering Romania and at conquering more of Central and South-Eastern Europe. This explicit anti-Romanian and geopolitical objective was written down by the Soviets in the very birth certificate of the "Moldavian language" in 1924, and it was copied by Serbian authorities after WW2, trying to invent a "Vlach language" for the Romanian minority in Serbia. Neither Russia, nor Serbia have yet abandoned these efforts. [ABSTRACT FROM AUTHOR]

Given the rapid and significant increase in email data, it is crucial for both individuals and organisations to prioritise the implementation of strong cybersecurity measures to combat attacks such as phishing emails. While continuous research has been made to find the most efficient approach to combating phishing emails, cybercriminals on the other hand continue to be determined, and their techniques become more and more sophisticated. In this paper, we present a novel approach for email classification using a Siamese deep learning network in order to verify authorship. We used the writing style and behavioural characteristics of the authors in this approach to identify the true source of emails and classify them as phishing, legitimate, harassment, or suspicious. This model was evaluated on the SeFACED dataset, where it received an accuracy of 90,12%, showcasing its efficacy in classifying emails, which enhanced email security and contributed to a safer online environment. [ABSTRACT FROM AUTHOR]

In this systematic literature review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Intention recognition is a subfield of artificial intelligence that deals with the identification of agents' intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a PRISMA systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, artificial intelligence black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses a challenge in the digital forensics landscape. However, it is imperative that models developed for digital forensics possess intrinsic attributes of explainability and logical coherence, thereby fostering judicial confidence, mitigating biases, and upholding accountability for their determinations. To this end, we advocate for hybrid solutions that blend explainability, reasonableness, efficiency, and accuracy. Furthermore, we propose the creation of a taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field. [ABSTRACT FROM AUTHOR]

The weaponization of artificial intelligence (AI) and machine learning (ML) models in cybersecurity is a growing concern, with cybercriminal organizations and nation-states exploiting their weaknesses. The Microsoft "Tay" chatbot incident exemplifies the risks of weaponized AI, as it displayed sexist and racist behaviors due to malicious data inputs. The researcher examined 21 academic studies on AI weaponization and AI-driven cyberattacks in this systematic review. This work, a systematic review, concludes with an in-depth understanding of the scale and scope of using AI as a cyber weapon. The findings revealed that more research is necessary on weaponizing AI for offensive cybersecurity applications and the following key observations (a) a connection between AI weaponization and countermeasures, (b) AI's role in enhancing cybersecurity defenses, (c) AI weaponization offering mitigation strategies for protecting digital assets and infrastructure, and (d) AI-driven attacks exploiting vulnerabilities, enabling automation and scalability, facilitating data poisoning and manipulation, improving social engineering, and augmenting evasion obfuscation. This study contributes to a better understanding of AI weaponization and aids researchers in synthesizing current literature on the topic. [ABSTRACT FROM AUTHOR]

Phishing emails, which masquerade as legitimate correspondence, are a significant modern hazard that cybercriminals use to deceive people into disclosing personal information. This paper presents the complex tactics used by cyber adversaries in the always changing field of phishing detection by using a real-time dataset and sophisticated classification tools to thoroughly examine email properties. In addition to detection, this paper highlights the need for constant watchfulness, delves into the psychological and social engineering elements included in phishing emails, and promotes continuous user awareness. Furthermore, the research broadens its scope to encompass the wider implications for enterprises, clarifying possible financial and reputational fallout. It offers helpful advice on strengthening defences and acts as a crucial manual for protecting private and corporate data against the ever-changing and persistent danger landscape that phishing attempts provide. This research gives an exploration of new insights by cybercriminals used for insertion of malicious software using emails. By using attributes like email text, Email type, the research identifies the signs of fraud emails. [ABSTRACT FROM AUTHOR]

Distribution companies (DISCOs) aim to maximize their annual profits by performing the optimal planning of distributed generators (DGs) or energy storage systems (ESSs) in the deregulated electricity markets. Some previous studies have focused on the simultaneous planning of DGs and ESSs for DISCO profit maximization but have rarely considered the reactive powers of DGs and ESSs. In addition, the optimization methods used for solving this problem are either traditional or outdated, which may not yield superior results. To address these issues, this paper simultaneously performs the optimal planning of DGs and ESSs in distribution networks for DISCO profit maximization. The utilized model not only takes into account the revenues of trading active and reactive powers but also addresses the active and reactive powers of DGs and ESSs. To solve the optimization problem, a new hybrid evolutionary algorithm (EA) called the oppositional social engineering differential evolution with Lévy flights (OSEDE/LFs) is proposed. The OSEDE/LFs is applied to optimize the planning model using the 30-Bus and IEEE 69-Bus networks as test systems. The results of the two case studies are compared with several other EAs. The results confirm the significance of the planning model in achieving higher profits and demonstrate the effectiveness of the proposed approach when compared with other EAs. [ABSTRACT FROM AUTHOR]

The article explores the current aspects of social engineering in the digital age. Social engineering is considered as a strategic technology of constructing new meanings, principles, rules and facts of social interaction. The socio-philosophical concepts of K. Popper, P. Sorokin, and R. Silverstone are analyzed in the context of constructive proposals of social engineering. The application of historical and philosophical intellectual constructs to the practices of social transformations is described in the article. The article reveals the possibilities and limitations of digital technologies in social engineering. The risks of creating new tools and algorithms for manipulation, disorientation of users of virtual technologies by social engineering methods are shown within the framework of the digital security problem. The diversity of views on the essence of social engineering and the analysis of its spheres of application problematize the interpretation of its social role and meaning. The methods of constructing social events and interfering in people's lives require critical evaluation. The implementation of AI in social engineering developments leads to the new risks, which are systematized in the article. They are related to the manipulation of public consciousness, distortion of identification and personalisation methods, financial fraud, and violation of human security [ABSTRACT FROM AUTHOR]

This article proposes an intersectional approach to studying non-Russian queer experiences in the Soviet empire. While earlier applications of intersectionality focused on race, gender, and class, other regional perspectives may highlight ethnicity, citizenship, and language. The text approaches non-Russian queer subjects in the USSR as a heterogeneous multiplicity via a contextualised reading of a diary fragment written by the Latvian Kaspars Irbe in Minsk in 1952. It highlights the prominence of communication between queers and people from the "centre" but also notes the hindrance of immediate contact between ethnicised subalterns due to Soviet social engineering. Tracing contingencies of Belarusian history, the article reveals how national identity and queer emancipation projects can evolve together. [ABSTRACT FROM AUTHOR]

This essay contends that emotional oscillations between subordination and emancipation are responsible for the inconclusivity and evasiveness of Le Fanu’s plots, which critics often emphasise but rarely try to explain. The parallels between, on the one hand, the dual structures that pervade Le Fanu’s works and, on the other, the binaries of latent dream thoughts and manifest dream content, the familiar and the uncanny, which Sigmund Freud employs in his explorations of the unconscious, shed significant light on the motivational forces at work in Le Fanu’s oeuvre. Le Fanu uses displacement and condensation, the major operations Freud attributes to dream work, to create an outside world, which reveals itself as an upended world within. This essay tries to demonstrate the psychological significance of central motifs in Le Fanu's works, such as the double and the closed room. In light of later psychoanalytical insights into the influence of childhood traumatisation on the creativity of writers, Le Fanu appears absorbed by the incorporation of his innermost mortifications and yearnings into the defamiliarisations of his fiction. On the basis of Gothic patterns, he produces psychological fiction avant la lettre. He anticipates George Orwell’s dissection of mental processes in terms of manipulative social engineering, H.P. Lovecraft’s fantastic evocations of a Jungian collective unconscious, and literary interiorisation by modernist techniques, developed not long after Le Fanu’s death by the likes of James Joyce and Virginia Woolf. [ABSTRACT FROM AUTHOR]

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The imminent threat that phishing websites poses is a major concern for internet users worldwide. These fraudulent websites are crafted by cyber attackers to appear trustworthy and deceive vulnerable users into divulging confidential data like medical health records, credit card details, passwords, and Personal Identifiable information (PII). To bait their victims, cybercriminals employ tactics such as social engineering, spear-phishing attacks, and email phishing scams. As a result, unsuspecting individuals may be enticed to visit these websites, putting their sensitive information at risk. This work presents an application designed to predict phishing attacks after comparing polynomial and radial basis function of support vector machine (SVM). The proposed application leverages a dataset of known legitimate, suspicious and phishing attacks stored in a database and employs an SVM algorithm for classification based on user input. The application provides a user-friendly graphical user interface (GUI) that allows reporting of new phishing incidents based on the features that have strong relationship in determining if a website is phishing or not. The proposed application utilizes the inherent scalability of database technology to support record expansion whenever there is an instance of a user initiating phishing prediction thereby, making it suitable for use in a wide range of organizational settings. [ABSTRACT FROM AUTHOR]

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions. [ABSTRACT FROM AUTHOR]

Cyber-attacks which is known as Computer Network Attack (CAN). It is a threat created by cybercriminals by more than one computer against networks. There are various types of cyberattacks among them Phishing Attack is one of them. Phishing is a technique of gathering sensitive information of a target such as username, password, bank details etc. There are various ways to perform phishing attacks, among them URL PHISHING attack is one way to gather user's information. Mainly hackers create a fake website regarding bank details, shopping websites, etc. using social engineering tool which looks like legitimate website. This fake URL will be sent to user via email or through some other resources. In this paper, we will be performing data analysis, data preprocessing, data exploring, training and predicting through machine learning and optimization techniques on dataset which contains two attributes (URL, Label). We will be introducing performance metrics like confusion matrix accuracy, precision, recall and F1 score to know the performance of the model. An optimization technique which is Stochastic gradient descent performed better than logistic regression. [ABSTRACT FROM AUTHOR]

This article can be broken down into two parts, perhaps somewhat unequally as far as its orientation is concerned. In the first part, the author takes the reader on a kind of exploration of the history of moral education, a subject which, at first glance and from the perspective of the years he spent studying educational historiography, appears to be rather undeveloped terrain. Since the piece is related to the awarding of the Comenius Medal, Comenius himself already provides a good starting point for this round of studies. As in the case of Herbart, another classic within the canon of educational history, Comenius held the opinion that morality plays a key role in upbringing of young people, which became increasingly scintillating in appearance from the Enlightenment onwards. For in a well-regulated society, it was by means of upbringing and education that individual freedom could be created. However, in the course of the 20th century and to the shame of humanity, people were forced to witness how the idea that people and society could be shaped by social engineering could equally give rise to a lack of freedom, as the aberrations of Nazism, fascism and ultimately Marxism-Leninism unequivocally demonstrated. So can such "reversals" of modern-day thought ultimately teach us any overall lessons about the content, manner and results with which moral curricula are imposed? Or must we first set out to identify the "abnormal" cases that society first branded as such and only subsequently extract those lessons? In other words, can extraordinary situations and events teach us something about the everyday reality of moral education as manifested in the so-called "civilising offensive" that took place from the end of the 18th century onwards? As far as the Low Countries are concerned, the author is, for that matter, setting foot on familiar ground. By utilising previous research on Belgium, Flanders and the (Belgian) Congo, the second part of the article wastes no time in examining what moral education meant in more specific terms in the 20th century. In that regard, the focus not only lies on contextualising the insights and questions raised by the first part, as a "tour d'horizon", but equally on analysing them in greater depth. After all, the author's years of research already provide three interesting points of reference: 1) the strong continuity of the patronising perspective; 2) the problematic nature of thinking about educational innovations and didactic innovations in binary terms, such as "old" and "new", and 3) the lack of a straightforward link between parenting and educational goals on the one hand and their results and effects (including and especially in the long term) on the other. Which leads inevitably to the conclusion that education, important as it is, must not be overestimated. Nor should history for that matter. Perhaps both are nothing more than an opportunity to partake of a meaningful encounter that may be effective, but whose outcome one can never be sure of. Which in turn does not take away from the fact that we must still place our hopes on it. For hope is probably the most positive thing that human beings carry within them, just as Comenius himself proved in his lifetime, by the way. [ABSTRACT FROM AUTHOR]

The social engineering cyber-attack is where culprits mislead the users by getting the login details which provides the information to the evil server called phishing. The deep learning approaches and the machine learning are compared in the proposed system for presenting the methodology that can detect phishing websites via Uniform Resource Locator (URLs) analysis. The legal class is composed of the home pages with no inclusion of login forms in most of the present modern solutions, which deals with the detection of phishing. Contrarily, the URLs in both classes from the login page due, considering the representation of a real case scenario and the demonstration for obtaining the rate of false-positive with the existing approaches during the legal login pages provides the test having URLs. In addition, some model reduces the accuracy rather than training the base model and testing the latest URLs. In addition, a feature analysis is performed on the present phishing domains to identify various approaches to using the phishers in the campaign. A new dataset called the MUPD dataset is used for evaluation. Lastly, a prediction model, the Dense forward-backwards Long Short Term Memory (LSTM) model (d - FBLSTM), is presented for combining the forward and backward propagation of LSMT to obtain the accuracy of 98.5% on the initiated login URL dataset. [ABSTRACT FROM AUTHOR]

Social engineering attacks are considered one of themost hazardous cyberattacks in cybersecurity, as human vulnerabilities are often the weakest link in the entire network. Such vulnerabilities are becoming increasingly susceptible to network security risks. Addressing the social engineering attack defense problem has been the focus of many studies. However, two main challenges hinder its successful resolution. Firstly, the vulnerabilities in social engineering attacks are unique due to multistage attacks, leading to incorrect social engineering defense strategies. Secondly, social engineering attacks are real-time, and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions. This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning (IQL) reinforcement learning method on human vulnerability attributes. The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy. Furthermore, the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks. The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning (QL) and deep Q-network (DQN) approaches in terms of time efficiency, taking 9.1% and 19.4% less time, respectively. Moreover, the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes. This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences on "Federated and Transfer Learning Applications." The issue includes 11 accepted papers out of 24 submissions, with a focus on innovative trends in federated learning and transfer learning. The federated learning approaches discussed in the papers cover topics such as aggregation algorithms, layer regularization, cluster analysis, blockchain-based incentive mechanisms, and the influence of unreliable participants. The transfer learning approaches explore topics such as Arabic handwriting recognition, literature-based drug-drug interaction, anomaly detection in 5G networks, and chat-based social engineering attack recognition. The document provides a brief overview of each paper's contribution and findings. [Extracted from the article]

Cyberattacks capitalize on human behaviors. The prevalence of cyberattacks surged during the COVID-19 pandemic, fueled by the increased interconnectivity of individuals on online platforms and shifts in their psychological dynamics due to the pandemic's context. The enhancement of human factors becomes imperative in formulating a robust cybersecurity strategy against social engineering in the post-COVID-19 era and in anticipation of analogous pandemics. This study aims to propose a model for delineating strategies across various phases of cyberattacks, grounded in the cyber kill chain model, while also encompassing cognitive mechanisms for adaptive responses. This approach aims to cultivate defensive cognitive factors like resilience and self-efficacy. To achieve this objective, we conducted an exploratory study adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Subsequently, we pursued a descriptive and correlational study based on prevalent attacks during the pandemic. The intention was to pinpoint proactive factors conducive to the development of cognitive capabilities to counter cyberattacks. These insights could pave the way for the creation of training programs and technological solutions aimed at mitigating the impact of such cyberattacks. [ABSTRACT FROM AUTHOR]

This article recounts the experiences of an AuDHD researcher who relocated from the United States to Germany for a postdoc. The researcher discusses the challenges they faced in accessing mental health support and medication in Germany, as well as the differences in attitudes towards mental health and neurodiversity between the two countries. Despite these challenges, the researcher has found support from friends, peers, mentors, and their supervisor. They emphasize the importance of empathy and understanding towards neurodivergent individuals in academic spaces and advocate for better mental health and neurodiversity awareness. [Extracted from the article]

Lauren Hirshberg's book, "Suburban Empire," explores the U.S. occupation of Kwajalein in the Marshall Islands during the Cold War. The book focuses on the concept of Cold War colonialism and the containment of the Marshallese people, as well as the construction of a suburban lifestyle for U.S. civilian base workers. It examines the cultural legacy of the U.S. presence and the ways in which it sought to create a sense of Americanness in a far-away place. While the book provides valuable insights, it could have given more attention to racial differences among suburban residents and situated the study within the broader pattern of base colonization. [Extracted from the article]