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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]

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]

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]

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]

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]

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]

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 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]

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]

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 article presents the discussion on digital environment when immersed users become distracted by an AR interface. Topics include access social media and other websites on their personal smartphones at work or use an office network to visit these platforms; and potential oversight could risk users engaging with notifications designed for social engineering or espionage.

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]

The article discusses the ancient city of Giddan/Eddana in Iraq, which has been overshadowed by its more famous neighbor, Dura-Europos. Recent research suggests that Giddan/Eddana was a significant city in its own right, with similarities to Dura-Europos in terms of size and layout. The article provides an overview of the location and setting of Giddan/Eddana, as well as its identification based on historical sources. The text also discusses the archaeological site of Anqa, which was examined by Aurel Stein in 1938-39. The site had historical significance as a community center, but it has suffered archaeological damage over the years. The remains of Anqa include a large tell and an outer enceinte with mud brick walls and interval towers. The text suggests that Anqa was a significant settlement during the Arsacid and Roman periods, with possible Roman military presence. The similarities between Giddan/Eddana and Dura-Europos are explored, and the lack of research on Giddan/Eddana is attributed to colonial legacy and political borders. The article concludes by calling for further research and excavations at the site of Giddan/Eddana. [Extracted from the article]

Social engineering addresses a broad category of techniques aiming to persuade someone to reveal data or perform actions for criminal purposes, such as disclosing personal information about a particular target. Cybersecurity awareness is required to raise people's understanding of how these social engineering techniques are being used and so their capacity to exploit them. To accomplish this objective, primary focus is given to educating and training individuals on how to recognize such incidents and respond to them effectively. To protect people against social engineering threats, various cybersecurity models and approaches have been proposed. There are, however, a few differences between these models, since they are developed for specific purposes. Thus, the main objective of this study is to develop a cybersecurity awareness model specifically designed for Saudi students to protect them from social engineering attacks. The design science methodology was utilized in this study. The proposed model consists of four main stages: education and training, developing policies and guidelines, improving Saudi schools' security, as well as monitoring and evaluation. The model introduced can ensure the safety and privacy of students, teachers, and staff across different social platforms. [ABSTRACT FROM AUTHOR]

In this special issue, we invited contributions that critically examined issues of imperialism, colonialism, power, justice, etc. to expand the canon of anticolonial scholarship and critical scholarship in community psychology. Our two objectives were: (1) to build on the canon of anticolonial and critical race scholarship to cultivate an empirical and theoretical body of work and conceptual frameworks about racism and colonialism within the field of community psychology and (2) to unpack the different manifestations of racism in society from the lens of community psychology and reflect on the implications of these varied forms of injustice in the contemporary moment. Rooted in African epistemology and methodology (Martin, 2012), we find the concept of the algorithm to serve as a potent metaphor for the ways in which these oppressive structures operate given the prevalence of algorithms in our daily lives and the algorithm is symbolic of the information age and predictive powers that seem to govern society beyond conscious control. In this sense, imperial algorithms are these structures, patterns, processes, and procedures that perpetuate imperialism. These imperial algorithms manifest as neo‐colonialism, surveillance, social engineering, carcerality, reality warping of contemporary racism, health disparities exacerbated by COVID‐19, and environmental grids of oppression. Highlights: The algorithm is symbolic of the information age and predictive power.Imperial algorithms are patterns and processes that perpetuate imperialism.This special issue presents imperial algorithms and proposes liberatory strategies in response. [ABSTRACT FROM AUTHOR]

The given text is a list of references and author information for a research paper on advanced persistent threats (APTs) and insider threats in cybersecurity. The paper explores strategies, models, and games to detect and defend against these threats. The authors' research interests include game theory, distributed optimization, cybersecurity, and control systems. [Extracted from the article]

The constant increase in the number of threats to the security of critical infrastructure objects, which include socio-cyberphysical systems, leads to a decrease in the quality of security services and the level of security of infrastructure elements. The object of research is the process of building a complex system of protection in socio-cyberphysical systems. The imperfection of the mechanisms for ensuring the security of critical infrastructure objects, which include socio-cyberphysical systems, the technological complexity of identifying new security threats necessitates an urgent need for a radical revision of the current approaches to its provision. So, it becomes clear that the development of a new approach to ensuring the security of information resources in socio-cyberphysical systems is needed. The article proposes a new approach to the methodological foundations of building multi-contour information protection systems with internal and external circuits on each of the platforms of socio-cyberphysical systems. This approach is based on a universal classifier of threats, which takes into account not the technical aspect of threats, but also their integration with social engineering methods, their synergy of hybridity. The sociopolitical influence on the realization of threats is taken into account, and practical mechanisms for providing basic security services based on post-quantum algorithms are also proposed. To provide basic security services in the proposed multi-contour protection system, it is proposed to use post-quantum algorithms – McEliece crypto-code constructions, which provide Perr=10-9–10-12, safe time Tsec=1025–1035 group operations. Within the framework of the proposed approach, the problem of increasing the level of information security has been formalized and further ways of solving it have been determined. [ABSTRACT FROM AUTHOR]