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AbstractCyber-attacks have become a serious global cybersecurity issue, affecting sectors such as petroleum, finance, health, and industry. The financial and banking sectors are particularly vulnerable, experiencing disproportionate harm due to the escalating complexity of digital threats. Recent academic efforts have focused on developing countermeasures, but these have primarily concentrated on technical solutions, which alone are insufficient. The persistent success of cyber-attacks highlights the need for a better understanding of human susceptibility to cyber scams. This research addresses the gap by investigating the psychological and social factors that lead individuals to fall for scam emails and links. It also explores how hackers exploit social engineering tactics to target victims and seeks to reverse these tactics to enhance protection. By understanding why people fall for cyber scams, the research aims to develop more effective cybersecurity measures, contributing significantly to the ongoing battle against cyber-attacks. [ABSTRACT FROM AUTHOR]

The objective of this systematic review was to analyze pre-existing programs that address a responsible use of the Internet with the aim of managing the diverse challenges and risks of the digital space. Five databases were used to obtain studies from 2017 to 2022. The following variables were analyzed: target population, duration, content, and evaluation. Programmes included the following online risks: cyberbullying, sexting, digital privacy, grooming, identity theft, false information, problematic Internet use, phishing and social engineering, digital identity and online reputation, excessive information sharing, spam and and fraud. The need for the design and implementation of programs that involve the entire educational community and address the issues of pornography and online safety has been identified. [ABSTRACT FROM AUTHOR]

The text discusses the importance of cybersecurity and the need for organizational scientists to focus on the "human factor" in cybersecurity research. It highlights recent examples of cyberattacks and data breaches, emphasizing the impact on organizations and individuals. The text also mentions a special issue on cybersecurity in the Journal of Business and Psychology, summarizing three articles accepted for publication. It concludes by discussing patterns in the citing articles and suggesting areas for future research in the intersection of organizational science and cybersecurity. [Extracted from the article]

South Africa has a complex history of racialized (im)mobilities. During the social engineering of apartheid, the city became a racialized white space that was dependent on black migrant workers who were forced to live in the marginal spaces on the edge of the city. This daily commute was enabled through the construction of public transportation systems and commuter railways. This mundane and banal form of mobility became a key site of both repression and resistance during the apartheid era. The present article explores how these racialized railway mobilities were represented during the 'Drum decade' of the 1950s in South Africa. It explores how the short story, as a liminal genre positioned within local and transnational literary cultures, is mobilized to narrate railway mobilities during this transitional decade. Texts analyzed include the news reports and short stories by Can Themba, Es'kia Mphahlele, and Nat Nakasa. This article explores how the mobile public spaces of the railways and the mobile figures of railway passengers were mobilized in the Anglophone South African short story as a form of resistance to the repression of the apartheid era. [ABSTRACT FROM AUTHOR]

As nations worldwide diversify, societal institutions are increasingly faced with the challenging task to resolve issues regarding ethnic, cultural, and linguistic matters. In the present contribution, we review evidence for a theoretical model that highlights the relevance of procedural fairness for dealing with such ethnic-cultural issues. Our collective model of procedural fairness (CPF) explains the reactions to fairness enactment of different stakeholders: Minority groups that receive fair treatment, third-party minority groups, and the majority. For minority groups, ethnic-cultural procedural fairness effects emerge through self-categorisation processes, leading to positive leader evaluations and decision acceptance, as well as increased feelings of societal inclusion, well-being, and social trust. For the majority group, CPF holds that responses to ethnic-cultural procedural fairness are driven by higher-order moral standards of rightful conduct towards disadvantaged group members. Taken together, the present contribution accentuates the usefulness of ethnic-cultural procedural fairness as a social engineering tool in diverse societies. [ABSTRACT FROM AUTHOR]

The number of registered SIM cards and active mobile phone subscribers in Zambia in 2020 surpassed the population of the country. This development and the integration of mobile phone systems with financial payment systems has not come without a cost. Cyberattackers, using various social engineering techniques have jumped onto the bandwagon to defraud unsuspecting users. Considering the aforesaid, this paper presents a high-order analytical approach towards mobile phone-based social engineering cyberattacks (phishing, SMishing, and vishing) in Zambia which seek to defraud benign victims. This paper presents a baseline study to reiterate the problem at hand. The research used a mixed-methods approach, combining quantitative and qualitative data, and adopted a hybrid descriptive research design. Furthermore, an attack model and an evaluation framework were devised to ascertain the most prevalent types of mobile phone-based cyberattacks. Based on logistic regression analysis, the findings indicate that the most prevalent type of mobile phone-based social engineering cyberattack in Zambia is SMishing. Based on the results and observed insights, recommendations to mitigate these emergent social engineering cyberattacks were suggested. This research serves as a valuable baseline for understanding and addressing the growing challenges posed by mobile phone-based cyberattacks in Zambia's evolving technological landscape. [ABSTRACT FROM AUTHOR]

This study aims to identify the patterns of change in contemporary resistance formation in the West Bank, focusing on «Jenin Brigade» and the «Lion's Den». The study suggests that there are signs of transformation in Palestinian activism that have produced new patterns of resistance, influenced by the Palestinian environment, Israeli policies, and the success of such contemporary patterns influencing Palestinian activism. The study hypothesizes that Palestinian activism is undergoing a maturation phase, which inturn led to the maturation of Palestinian resistance that is strongly tied to ideological transcendence thought. The study concludes that there is a growth in resistance in the West Bank and an Israeli failure to subdue the new Palestinian identity [ABSTRACT FROM AUTHOR]

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 paper examines social engineering, a technique that capitalizes on human vulnerabilities to launch cybercrimes in open and distance learning (ODL) systems. A survey has been done, examining how individuals and corporations can become the most vulnerable point in a security system, enabling unauthorized access to sensitive data or services. The paper analyzes the level of awareness and susceptibility of ODL users to social engineering attacks. The goal is to raise individual and corporate awareness. A questionnaire was designed and administered to 1,000 participants from the National Open University of Nigeria (NOUN). It was found that 95.50% were technologically proficient and 75.10% used email for communication. The findings revealed that most participants had 0-5 years level of experience in the system; social engineering attacks were known by 47.70% of participants, and 67.10% admitted to suspicious behavior. Despite the fact that 51.0% were unfamiliar with social engineering assaults, only 39.80% of the participants had ever attended a cybersecurity training, while 94.80% were willing to participate in security awareness events. These results underline the need for comprehensive awareness initiatives to decrease ODL security concerns. [ABSTRACT FROM AUTHOR]

Purpose: Scams are indeed malicious attempts to influence people and can take many forms, including online scams. With the increasing availability of technology, scammers have more tools at their disposal to create convincing and sophisticated communications that appear to come from legitimate sources, such as emails, text messages or social media posts. These scams can be designed to trick individuals into clicking on malicious links, downloading malware or ransomware or providing sensitive information such as login information, financial information or personal details. Scammers often use social engineering techniques to persuade their targets to take specific actions, such as creating a sense of urgency, offering a reward or prize or posing as a trusted authority. These tactics can be highly effective, particularly if the target is unaware of the warning signs of a scam or is unfamiliar with how to protect themselves from online fraud. This paper aims to explore the extent and nature of this problem and evokes the concern that the general public is vulnerable and susceptible to scams if they are not resilient and aware. This paper also explores why victims fall for online scams and uncovers preventive measures to enable a direction in tacitly strategizing ways to create more impactful and effective awareness campaigns. Design/methodology/approach: This study explores these aspects through a holistic qualitative approach. Using in-depth interview techniques with six victims, six non-victims, four law enforcement officers, four scammers and seven stakeholders from various agencies such as banks, telecommunication agencies and the Malaysian Communications and Multimedia Commission involved in combating the issue of scams. Findings: The findings generally revealed that participants who were victims of scams felt Malaysians were susceptible to scams, easily fooled and had a nonchalant attitude. Most participants also highlighted that public safety is important for ensuring a high quality of life for citizens that should work closely between the government and non-government agencies, including effective law enforcement and crime prevention strategies. Originality/value: The uniqueness of this study is the feedback from scammers themselves and their input towards authority and victims. Overall, the respondents provide their views drawing strength from the ever-changing technological background as well as the susceptibility of security features and vulnerability of human engagement. [ABSTRACT FROM AUTHOR]

Financial crime is on the rise. The interconnectedness of global payments is making illicit cross-border transactions increasingly frictionless, while digitisation has not only increased the attack surface but also enabled criminals to employ increasingly sophisticated methods to evade detection, such as social engineering. Compounding the threat is the ever-growing mountain of data being generated, which is threatening to drown detection and prevention efforts. It is clear that a new level of defence is required in the fight against financial crime. This paper argues that the implementation of data-driven technologies, with a focus on artificial intelligence (AI), is a near-term imperative for effective risk-based anti-money laundering (AML) compliance programmes in the payments industry, where legacy AML programmes over-rely on a rule-based approach with a checklist of predetermined rules and indicators to flag potentially suspicious activity. To support the development and implementation of such data-driven enhancements, this paper hones in on the current hurdles, and explains the benefits and technical aspects of AI integration, emphasising the foundational importance of ISO/IEC AI-related standards. Finally, the paper discusses future opportunities for broader collaboration to build stronger collective defences. [ABSTRACT FROM AUTHOR]

Social engineering, or the use of deception to circumvent information security measures, has become a significant concern for organizations. Many organizations have implemented information security policies to mitigate the risks posed by social engineering attacks. This study uses a grounded theory-based approach to examine qualitative interviews with security auditors, IT security professionals, and social engineers (n = 54) to thematically catalog their insights on developing and supporting security policies. Results indicate that effective IT security policies are (1) properly communicated, (2) tested to find gaps in policy directives and their implementation, (3) buttressed by tools to facilitate good security decision-making among members, (4) written simply and concisely while being kept up-to-date, (5) supported through adequate staffing and expertise, (6) supported by organizational leadership, and (7) accompanied by an organizational structure which allows for policy to be overseen and implemented consistently. [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]

Mankind has always relied on "practical wisdom" in the sense of Aristotle to change people and society. However, practical wisdom is difficult to accumulate and pass on from generation to generation. Therefore, although "natural engineering" that is consistent with "natural technology" can be used effectively to transform the natural world, using "practical wisdom" to change people and society remains difficult. Solving this dilemma requires an effective means to separate "social science", "social technology", and "social engineering" to accumulate and inherit "knowledge of changing people and society" similar to other types of scientific knowledge. Today, the prevailing evidence-based social science is the opposition and unity of laws of nature and free will, truth-seeking and goodness-pursuing, individual wisdom and collective wisdom, autonomy and supervision, the universal and the special in practice. The emergence of this concept allows for the possibility to enhance the effectiveness of social science practice. [ABSTRACT FROM AUTHOR]

This research examines the ‘dirty politics’ between opposing and antagonistic poles during Chile’s 2021 presidential election. It analyses data from public messaging groups on WhatsApp, using computational methods, qualitative analysis, and an interview with a key informant from one of the candidates’ campaigns. The results point to ‘dirty’ tactics, such as disinformation and social engineering, but also to technical sabotage, with deliberate obstruction of campaign groups, mainly from the right against the left. [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]

In this article, we follow the trails of 20th-century psychologist Henry Herbert Goddard's influential study of the Kallikak family. Goddard's study is treated as a scientific story with two interlocking dimensions: One is the actual story of the Kallikak family, with literary elements such as setting, plot, and characters. The other dimension is the broader eugenic discourse, a powerful scientific narrative that calls for action in relation to society and the population. The purpose of the article is twofold. Firstly, to analyze the forming and articulations of this story and to explore some of the consequences for governing the population that it has made possible. Secondly, to explore some aspects of what a Foucauldian analytics of government can contribute with in relation to Goddard's work and the eugenic discourse from the early 20th century to today. [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]

Nationalism calls for one’s loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalist’s individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical events – the exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers’ opinion in favour of the dominant socio-political class. [ABSTRACT FROM AUTHOR]

The classic programme of conceptual engineering (Cappelen, Herman. 2018. Fixing Language: An Essay on Conceptual Engineering. Oxford: Oxford University Press; Eklund, Matti. 2021. "Conceptual Engineering." In The Routledge Handbook of Social and Political Philosophy of Language, edited by Justin Khoo, and Rachel Sterken, 15–30. London: Routledge) envisages a two-stage ameliorating process. First, we assess 'F' and determine what the term should express. Second, we bring it about that 'F' expresses what it should express. The second stage gives rise to a practical challenge: the implementation challenge. Engineering advocates need to explain by what means they can implement specific conceptual changes in the natural language shared by a community – a feat Herman Cappelen (2018. Fixing Language: An Essay on Conceptual Engineering. Oxford: Oxford University Press, 2020; "Conceptual Engineering: The Master Argument." In Conceptual Engineering and Conceptual Ethics, edited by Alexis Burgess, Herman Cappelen, and David Plunkett, 132–151. Oxford: Oxford University Press) argues to be beyond our understanding and control both on an externalist and on an internalist meta-semantics. I devise a new answer to the implementation challenge. Enlisting the influential theory of social norms by Cristina Bicchieri, I argue that engineering social norms in Bicchieri's technical sense amounts to an effective, specific, and feasible means to implement specific conceptual change, at least on internalist premises. I also argue that Bicchieri's social norms are an essential addition to the more familiar conventions and moral norms when it comes to conceptual engineering. [ABSTRACT FROM AUTHOR]

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Copyright of Safety & Security Sciences Review / Biztonságtudományi Szemle is the property of Obuda University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The text explores the evolution of the Chief Information Security Officer (CISO) role in the cybersecurity field. It emphasizes the expanding responsibilities of CISOs, including technological defense strategies, regulatory compliance, and strategic alignment with business objectives. The text also provides best practices and strategies for modern CISOs, such as aligning cyberrisk management with strategic objectives and embedding it in the organization's governance structure. It concludes by highlighting the ongoing challenges faced by CISOs, including the sophistication of cyberthreat actors and diverse regulatory frameworks. [Extracted from the article]

Phishing, an Internet fraud where individuals are deceived into revealing critical personal and account information, poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them more challenging to identify. Hence, it is imperative to utilize sophisticated algorithms to address this issue. Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning (ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets. After that, we used the normalization technique on the dataset to transform the range of all the features into the same range. The findings of this paper for all algorithms are as follows in the first dataset based on accuracy, precision, recall, and F1-score, respectively: Decision Tree (DT) (0.964, 0.961, 0.976, 0.968), Random Forest (RF) (0.970, 0.964, 0.984, 0.974), Gradient Boosting (GB) (0.960, 0.959, 0.971, 0.965), XGBoost (XGB) (0.973, 0.976, 0.976, 0.976), AdaBoost (0.934, 0.934, 0.950, 0.942), Multi Layer Perceptron (MLP) (0.970, 0.971, 0.976, 0.974) and Voting (0.978, 0.975, 0.987, 0.981). So, the Voting classifier gave the best results. While in the second dataset, all the algorithms gave the same results in four evaluation metrics, which indicates that each of them can effectively accomplish the prediction process. Also, this approach outperformed the previous work in detecting phishing websites with high accuracy, a lower false negative rate, a shorter prediction time, and a lower false positive rate. [ABSTRACT FROM AUTHOR]

The article discusses the cyber attack tactics of a hacker group called Muddled Libra, which targets IT help desks and uses social engineering techniques to gain access to organizations' systems and data. The group has been linked to high-profile attacks on companies such as MGM Resorts and Caesars Entertainment. The article emphasizes the importance of vigilance, educating employees on cybersecurity, and implementing strong password policies and multifactor authentication. It also highlights the need for organizations to take proactive measures to protect their networks and integrate social engineering awareness training into employee security education programs. [Extracted from the article]

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]

Ilya Kasavin's book, "A Social Philosophy of Science: An Introduction," explores the role of philosophy in society and its impact on science and technology studies (STS) and social epistemology. Kasavin argues that philosophy should not be confined to academia but should have a broader influence on society. He also identifies conceptual gaps in social epistemology and STS, suggesting that they could benefit from a philosophical approach. The book is divided into three parts, covering topics such as epistemology, the social turn in the history and philosophy of science, and the intersections of science, technology, and policy. Kasavin's work emphasizes the importance of interdisciplinary communication and the need for a political philosophy of science. Overall, the book offers a thought-provoking and timely perspective on the societal and political role of philosophy in the context of science and technology. [Extracted from the article]

In information security context, social engineering is defined as malicious activities caused by cybercriminals by means of human interactions. It is mainly a psychological manipulation technique which gets benefit of human error to reach private information. This study used machine learning algorithms to predict individuals' susceptibility to be tricked by social engineering attacks. Simulated scenarios were presented to study participants, and they were asked to identify whether each scenario was a social engineering attack or not. Different kinds of attacks related to various industries were integrated to social engineering simulations. For each participant, different types of social engineering scores were calculated according to their responses. Besides simulations, questionnaires related to demographics, technology usage, and personality traits were filled out by the participants. All of these collected data were used in building predictive classification and regression machine learning models. Through regression and classification models, it was aimed to proactively predict individuals' social engineering risk levels and classify them into different risk groups in terms of different attack types. This research revealed that it is possible to predetermine the social engineering risk levels of individuals. This important finding means that possible attacks can be prevented by raising awareness before the attack occurs. Within the scope of this study, a social engineering risk detection mobile application has also been developed to give practitioners and policy makers an idea of what kind of systems can be developed in order to determine the risk levels of individuals and then to educate them about various attacks. The ones who need to take action against social engineering attacks will get benefit from findings of this research. [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]

The object of the research is the channels of information transmission during the control of the information-analytical decision-making system. The development of high technologies and computing capabilities ensures the evolutionary development of smart technologies and socio-cyber-physical systems on the one hand. On the other hand, it forms the integration of targeted (mixed) attacks with the possibility of integration with social engineering methods. In addition, mobile technologies significantly increase the possibilities of data transmission speed. However, this only provides the authentication service, which does not provide a full range of security services. Under such conditions, an urgent task at the decision-making stage is the use of auxiliary systems that ensure the adequacy of decisions and the promptness of their adoption. The proposed mathematical model of the information and analytical system allows to calculate the main technical characteristics of information transmission channels and identify their possible failures. The use of an information and analytical system simplifies the decision-making process, allows to increase the reliability of such decisions due to an increase in the level of automation. Increasing the level of automation in the decision-making process removes subjective factors and the decision depends on the availability of information. Therefore, the reliability of the information transmission channels of the information and analytical system significantly affects the quality of the decisions made. The developed model allows to ensure the required level of reliability of information transmission channels. The obtained results are explained by determining the dependence between the parameters of the information and analytical system and their influence on the quality of information transmission through channels. The results of the study can be used in practice when considering systems with a limited number of states during operation. [ABSTRACT FROM AUTHOR]