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Science skeptics have spent years attacking climate science, but it has only been recently that post-COVID-19 conspiracy theorists have directed their attention to local city planning, with misinformation campaigns throwing vitriol at the idea of the 15-minute city and denouncing planning best practices as a global form of social engineering with hidden agendas to restrict private freedoms. In this context, urban planners and practitioners need to understand the nature of conspiracy claims and distinguish legitimate concerns about the 15-minute city model. As science skeptics and conspiracy theories gain ground in all sectors of our societies, researchers and city planners need to communicate evidence-based decision making and address science-driven concerns. [ABSTRACT FROM AUTHOR]

The article "A Neuroscience Perspective on AI and Cybersecurity" explores the integration of artificial intelligence (AI) in cybersecurity, highlighting the use of machine learning (ML) algorithms and neural networks to create sophisticated cyberthreats. By understanding the brain's capacity for learning and adaptation, organizations can refine AI models to mimic human decision-making patterns. The text also discusses the use of behavioral biometrics and cognitive biases in AI-driven cyberattacks, emphasizing the need for comprehensive training programs to enhance cybersecurity awareness and decision-making. Ultimately, insights from neuroscience can inform new approaches to cybersecurity, enabling organizations to build a more secure digital landscape against evolving AI-driven threats. [Extracted from the article]

The article discusses the challenges faced by cybersecurity leaders in an evolving threat landscape. It highlights the need for leaders with a rare combination of IT expertise, cybersecurity knowledge, and interpersonal skills. The shortage of qualified candidates with technical expertise and leadership capabilities is a growing concern. Additionally, the article emphasizes the importance of effective communication, building strong teams, and addressing mental health and stress management issues within cybersecurity teams. [Extracted from the article]

The aim of this article is to shed light on Berlusconi's political strategy to reshape the state by examining some of the incentives for discretionary changes in the tax-benefit system during his administrations. We argue that by exploiting the typically categorical and fragmented nature of the Italian welfare system, Berlusconi's governments engaged in the 'art of manipulation' within the tax-benefit framework, reflecting his efforts in social engineering to forge new electoral alliances. It is well established that redistributive programmes are the outcome of intense political decision-making, as they create redistributive advantages among different social groups. This article contends that, under the guise of economic liberalism, the Berlusconi governments devised redistributive strategies aimed at constructing new social constituencies to bolster and legitimize his dominant position. Although his governments promised a pact of 'From Decline to Growth' for all through tax-benefit redistribution, they ultimately introduced only incremental changes rather than long-term structural reforms. In conclusion, Berlusconi's neoliberal 'revolution' was more rhetoric than reality. [ABSTRACT FROM AUTHOR]

Extended Reality (XR) platforms can expose users to novel attacks including embodied abuse and/or AI attacks-at-scale. The expanded attack surfaces of XR technologies may expose users of shared online platforms to psychological/social and physiological harms via embodied interactions with potentially millions of other humans or artificial humans, causing what we define as an inter-reality attack. The past 20 years have demonstrated how social and other harms (e.g., bullying, assault and stalking) can and do shift to digital social media and gaming platforms. XR technologies becoming more mainstream has led to investigations of ethical and technical consequences of these expanded input surfaces. However, there is limited literature that investigates social attacks, particularly towards vulnerable communities, and how AI technologies may accelerate generative attacks-at-scale. This article employs human-centred research methods and a harms-centred Cybersecurity framework to co-design a testbed of socio-technical attack scenarios in XR social gaming platforms. It uses speculative fiction to further extrapolate how these could reach attacks-at-scale by applying generative AI techniques. It develops an Inter-Reality Threat Model to outline how actions in virtual environments can impact on the real-world. As AI capability continues to rapidly develop, this article articulates the urgent need to consider a future where XR-AI attacks-at-scale could become commonplace. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threats (APTs) are a prime concern in the formulation and implementation of national cybersecurity policies. These threats often also involve complex social engineering tactics which are undergoing a quantitative and qualitative revolution with burgeoning AI capabilities. However, the attribution of these APT activities can be mired with technical and political considerations. We analysed 142 APT groups' attributions along with their use of social interaction vectors to ascertain the nature of the risk environment and the operational threat landscape of AI and social automation. We discover that close to 80% of APT activities could be chalked up to merely 20% of key nation-state threat actors competing with each other. We further discuss the implications of this political threat distribution for national cybersecurity environments. [ABSTRACT FROM AUTHOR]

The evolving landscape of cyber threats necessitates continuous advancements in defensive strategies. This paper explores the potential of artificial intelligence (AI) as an emerging tool to enhance cybersecurity. While AI holds widespread applications across information technology, its integration within cybersecurity remains a recent development. We offer a comprehensive review of current AI applications in this domain, focusing particularly on their preventative capabilities against prevalent threats like phishing, social engineering, ransomware, and malware. To illustrate these concepts, the paper presents a case study showcasing a specific AI application in a cybersecurity context. This case study addresses a critical gap in securing communication within resource-constrained Internet of Things (IoT) networks using the IEEE 802.15.4 standard. We discussed the advantages and limitations of employing PN sequence encryption for this purpose. [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]

Oil and gas fields represent the backbone of industries. Such industries are exposed to many physical risks, such as sabotage and oil and gas leaks, theft of equipment, and technological hazards (e.g., exposure to hacking of security and sensitive information by attackers). In this regard, a variety of decentralized studies still suffer from storage limitations, long response times, high costs, and redundant or unnecessary data storage due to false positive transmission, as well as 51%, Sybil, Sniffing, Eavesdropping, Spoofing, Phishing, and Social engineering attacks. Our paper proposes the utilization of surveillance thermal CCTV cameras based on Hyberledger Fabric blockchain to enhance safety and security in oil and gas fields. The proposed scheme incorporates fog nodes to facilitate the authentication process and reduce the time response. Our system also adopts InterPlanetary File System (IPFS) storage that enables scalability and provides hash-based integrity to stored data. Moreover, IPFS integrates with cuckoo filters to reduce superfluous or unneeded data. Our work offers low-complexity authentication utilizing ECDSA, balancing high security and complexity, and resisting well-known malicious and cyberattacks. Furthermore, the proposed scheme has been verified its validation by proving its resistance to attacks. Compared with related literature that used the blockchain or fog computing technology, the performance analysis accomplished the lowest cost results (52,000736 ms) in the computation cost and (800 bits for each user, and CCTV) in the communication cost. It is worth noting that we relied on reliable sources to calculate these results. [ABSTRACT FROM AUTHOR]

To protect important data and files, people often use air gap isolation, also known as air gap separation, to block external threats. However, internal networks may still introduce pollution due to supply chain contamination, human error, or social engineering. Although internal devices cannot directly communicate with the outside world. This paper proposes a new technology called Bit Sufi-Dance that utilizes electricity meters and optical devices to detect exfiltrated data. Most electricity meters have power indicator mechanical turntables or LED lights which can be indirectly controlled by the device's power consumption oscillation. This allows for information encoding and the extraction of data from the air-gapped computer. It is important to note that this exfiltration channel does not require any hardware or firmware modifications and cannot be detected by existing Data Leakage Prevention (DLP) systems. The article discusses its design and implementation issues while evaluating it using different types of electricity meters. Our experiment demonstrates that data can be exfiltrated from the air-gap isolated computer through an electricity meter at a bit rate of 101 b/h. Finally, we assess this security threat and discuss defense mechanisms and preventive measures. [ABSTRACT FROM AUTHOR]

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]

Pre-trained large language models have demonstrated impressive text generation capabilities, including understanding, writing, and performing many tasks in natural language. Moreover, with time and improvements in training and text generation techniques, these models are proving efficient at generating increasingly human-like content. However, they can also be modified to generate persuasive, contextual content weaponized for malicious purposes, including disinformation and novel social engineering attacks. In this paper, we present a study on identifying human- and AI-generated content using different models. Precisely, we fine-tune different models belonging to the BERT family, an open-source version of the GPT model, ELECTRA, and XLNet, and then perform a text classification task using two different labeled datasets—the first one consisting of 25,000 sentences generated by both AI and humans and the second comprising 22,929 abstracts that are ChatGPT-generated and written by humans. Furthermore, we perform an additional phase where we submit 20 sentences generated by ChatGPT and 20 sentences randomly extracted from Wikipedia to our fine-tuned models to verify the efficiency and robustness of the latter. In order to understand the prediction of the models, we performed an explainability phase using two sentences: one generated by the AI and one written by a human. We leveraged the integrated gradients and token importance techniques, analyzing the words and subwords of the two sentences. As a result of the first experiment, we achieved an average accuracy of 99%, precision of 98%, recall of 99%, and F1-score of 99%. For the second experiment, we reached an average accuracy of 51%, precision of 50%, recall of 52%, and F1-score of 51%. [ABSTRACT FROM AUTHOR]

AbstractThe purpose of this integrative review is to describe latent or covert signals of cyber victimization and risky online behaviors in teens. This review was guided by Whittemore and Knafl’s integrative review method. A literature search of peer-reviewed, primary source studies published between January 2010 and February 2022 was conducted using the following databases: Cumulative Index of Nursing and Allied Health Literature Plus with Full Text (CINAHL), APA Social Work Abstracts, ERIC, PsycInfo, and APA PsycArticles. Initially, the search string was (cyber victimization OR cyberbullying) AND (risky) AND (internet OR online) AND (signs OR symptoms) AND (latent OR covert OR hidden OR cryptic) AND (lived experience OR perceived OR perception). Additional terms were added, including social engineering. A total of 20 articles were included in this review. Four themes emerged from the literature: (1) the reciprocal nature of cyber victimization and risky online behaviors, (2) latent or covert signs and symptoms, (3) new language surrounding teens’ online experiences, and (4) barriers to disclosing experiences. Latent or covert signs and symptoms were categorized into two sub-themes: (1) mood dysregulation and (2) online interaction and expression. Barriers to disclosing experiences were categorized into two subthemes: (1) reluctance to disclose and (2) minimization or disconnection of experience. Qualitative studies yielded richer information concerning the experiences of teens who experienced cyber victimization. Future research using qualitative methodologies and direct analysis of public social media posts are needed to effectively detect the latent or covert signs and symptoms of cyber victimization and risky online behaviors. [ABSTRACT FROM AUTHOR]

The article delves into the resettlement of Ukrainians and Belarusians from Poland to the Soviet Union between 1944-47, shedding light on the propaganda tactics and obstacles encountered by Soviet authorities. Initially framed as a voluntary exchange, the resettlement eventually turned coercive, met with skepticism and resistance from the local populace. Despite the propaganda's emphasis on national homeland and socialist benefits, many perceived the move as punitive rather than advantageous. The text underscores the challenges and complexities surrounding the forced deportations and manipulation of identities during this period. [Extracted from the article]

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]

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.)

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]

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]

Phishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns. [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]

Gaining unauthorized remote access to an environment is generally done either by exploiting a vulnerable service, or application that is internet-based; or by tricking a user into executing malicious codes. The former one is typically more simple since there is no need for any user interaction. The latter one, however, requires much more effort on the attackers' side since they must find a way to incite the victim into opening a malicious document and interacting with an HTML page in a web browser. In this paper, we will focus on the latter technique which falls into the social engineering category, as it will involve the use of a phishing attack. The reason for this selection is based on the fact that it is challenging to correct user behavior. Thus, it increases the attackers' chance of performing a successful attack, contrary to the former technique, where a simple patch, upgrade, or update can prevent the adversaries from being successful in their attacks. Since Microsoft Office is a very trusted and used software by many people (both in personal and commercial use), we will make use of its features to build our payloads and eventually to gain a remote code execution to a victim's system. Performing a successful phishing attack involves a lot of barriers that often need to be crossed such as the need for similarity, purchasing domains, the use of encoding, encryption, etc. Nowadays, companies frequently employ very aggressive antivirus software that will delete malicious files as soon as they land on their system. Therefore, bypassing the security protections will need to be taken into account, which will also be addressed in this paper. [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]

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]

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]

The threat of social engineering attacks is highly evident in Indonesia. This is reflected in the IDADX report, which shows a significant increase in phishing attack complaints in Indonesia during the first quarter of 2023, with 26,675 reports, up from 6,106 reports in the fourth quarter of 2022. The highest number of attacks occurred in February with 15,050 cases. This also resulted in Indonesia being the 8th country with the highest number of data breach cases globally, with 820,000 cases in the second quarter of 2022, according to Surfshark. The purpose of this research is to analyze the utilization of digital forensic technology in collecting digital evidence to reveal the identities of social engineering crime perpetrators. The writing of this article uses a qualitative analysis method supported by digital forensic theory. The results of the discussion indicate that forensics focuses on the identification, collection, analysis, and preservation of digital evidence that can be used in legal proceedings. Digital forensics leverages computer technology and digital devices to uncover and secure information relevant to a crime. In social engineering crimes, which involve psychological manipulation to obtain information or certain access from victims, digital forensics plays a crucial role in identifying, collecting, analyzing, and preserving digital evidence that can reveal the identities of the perpetrators and their methods. Digital forensic technology is used to analyze emails, text messages, activity logs, and other digital traces left by the perpetrators. [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 article discusses the evolution of the European Union's cybersecurity policy, highlighting its transition from reactive measures to a comprehensive strategy focused on resilience, cooperation, and technological advancement. The EU's cybersecurity policy has undergone three distinct phases: genesis, institutionalization, and regulatory, with a focus on regulatory mercantilism to gain control of cybersecurity governance. The article also explores the EU's efforts to export its cybersecurity norms internationally through cyber-diplomacy initiatives. Overall, the EU's cybersecurity policy is seen as a central pillar of its initiatives to provide leadership in a world perceived as presenting complex threats to its stability and security. [Extracted from the article]

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]