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With the increasing prevalence of cybercrime victimization there is a growing need for prevention. Previous studies have attempted to uncover risk factors associated with cybercrime victimization in the areas of personal characteristics and online routine activities. This article aims to take the field a step further by including actual self-protective online behavior, obtained through a population-based survey experiment (N = 1886), as a risk factor for cybercrime victimization. In wave 1 of our longitudinal design, personal characteristics, online routine activities, and actual self-protective online behavior concerning password strength, clicking behavior, sharing personal information, and handling phishing emails were measured. In wave 2, cybercrime victimization of several types of cyber-enabled and cyber-dependent cybercrimes was measured one year later. Results indicate that few personal characteristics, online routine activities, and self-protective online behaviors are related to the odds of becoming a cybercrime victim. This furthermore illustrates the heterogeneity of cybercrime victimization, since most significant factors only seem to be related to the risk of one particular type of cybercrime. These results indicate that to explain cybercrime victimization, the research field needs to shift its focus and adapt to new online developments. [ABSTRACT FROM AUTHOR]

Information security is a major concern for everyone nowadays. While substantial research exists on gender differences in education and technology, there appears to be very little research on gender differences in information security and that research examines a broad list of self-reported information security behaviors in a single study. Our research adds to the literature by examining in more depth one specific area of information security behavior: peoples' behavior relating to phishing attacks. This research attempts to investigate gender differences in email security perceptions and behaviors by surveying students, faculty, and staff at one midwestern public, master's granting university. The survey questions are developed based on the Health Belief Model. 414 usable survey response sets were collected and analyzed. The findings suggest that men and women have different perceptions on self-efficacy, vulnerability, barriers, cues to action, and self-reported security behaviors. While the Health Belief Model provides a relatively good fit in explaining email security behaviors for both men and women, each group appears to value each of the underlying factors differently. The findings shed light on how to design and conduct security training to increase adoption of protective email behaviors. [ABSTRACT FROM AUTHOR]

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO n GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%. [ABSTRACT FROM AUTHOR]

In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic. [ABSTRACT FROM AUTHOR]

Phishers exploit the social nature of social media, thereby targeting young adults, who are highly susceptible to phishing. This study focuses on two under-researched factors influencing young adults' susceptibility to social media phishing: the user's relation to the message sender and Fear of Missing Out (FoMO). In an online vignette study, 193 young adults were presented with Instagram chat messages from either known or unknown senders, accompanied by varying consequences for not clicking. These ranged from missing an event with no other user (no consequences) to missing an event with one (low) or several other users (high consequences). The analysis focused on intended behaviour and suspicion, while also capturing young adults' situational fear of missing out on the scenario-based event with the message sender (State FoMO) and their individual Trait FoMO. The results highlight that the user-sender relation is a strong predictor of phishing susceptibility and a crucial contributor to State FoMO. Furthermore, young adults who are high in Trait FoMO exhibited lower suspicion towards phishing attempts. These findings are discussed along with methodological considerations. In addition, strategies to mitigate the identified vulnerabilities are suggested, focusing on areas where social media phishing is most likely to affect young adults. [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]

Smartphone users use the mobile browser to interact with web-based applications. However, mobile browsers are vulnerable to various types of attacks such as phishing, DDoS, etc. There are numerous methods for detecting such attacks on the desktop environment, but, due to hardware limitations, such methods may not be effective for smartphones. Therefore, this paper presents different types of possible cyber-attacks in mobile browsers, and their solutions provided by various researchers to avoid such attacks. Additionally, the survey provides different types of mobile browser-related TLS errors and security indicators related challenges. We have discussed the consequences of emerging domains like mobile payment systems and online meetings. This survey will help the various mobile users in avoiding cyber-attacks while using the Internet for their day-to-day activities and will encourage researchers in designing new effective solutions against various types of mobile-specific cyber-attacks. [ABSTRACT FROM AUTHOR]

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each delineated by 30 attributes. The WOA's hyperparameter optimization enhances the RNN's performance, evidenced by a meticulous validation process. The results, encapsulated in precision, recall, and F1-score metrics, surpass baseline models, achieving an overall accuracy of 92%. This study not only demonstrates the RNN's proficiency in learning complex patterns but also underscores the WOA's effectiveness in refining machine learning models for the critical task of phishing detection. [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]

Phishing is an attack that attempts to replicate the official websites of businesses, including government agencies, financial institutions, e-commerce platforms, and banks. These fraudulent websites aim to obtain sensitive information from users, such as credit card numbers, email addresses, passwords, and personal identities. In response to the increasing number of phishing assaults, several anti-phishing strategies have been developed. However, existing techniques often fail to extract the most crucial features, leading to potential misclassification. Additionally, the complex algorithms employed result in high response times. To address these challenges, this paper proposes a novel approach called Bidirectional Long Short-Term Memory based Gated Highway Attention block Convolutional Neural Network (BiLSTM-GHA-CNN) for detecting phishing URLs. The BiLSTM captures contextual features, while the CNN extracts salient features. The integration of the highway network into the BiLSTM-CNN architecture enables the capture of significant features with rapid convergence. Furthermore, a gating mechanism is employed to weigh the output features of the CNN and BiLSTM. Five datasets from diverse sources such as Phish Tank and Open Phish were created for experimentation. The results demonstrate that BiLSTM-GHA-CNN achieves superior detection accuracy, precision recall, and F1-score compared to state-of-the-art techniques. Moreover, the proposed system significantly reduces the response time to a remarkable 12.46 ms. [ABSTRACT FROM AUTHOR]

Detecting phishing webpages is a critical task in the field of cybersecurity, with significant implications for online safety and data protection. Traditional methods have primarily relied on analyzing URL features, which can be limited in capturing the full context of phishing attacks. In this study, we propose an innovative approach that integrates HTML DOM graph modeling with URL feature analysis using advanced deep learning techniques. The proposed method leverages Graph Convolutional Networks (GCNs) to model the structure of HTML DOM graphs, combined with Convolutional Neural Networks (CNNs) and Transformer Networks to capture the character and word sequence features of URLs, respectively. These multi-modal features are then integrated using a Transformer network, which is adept at selectively capturing the interdependencies and complementary relationships between different feature sets. We evaluated our approach on a real-world dataset comprising URL and HTML DOM graph data collected from 2012 to 2024. This dataset includes over 80 million nodes and edges, providing a robust foundation for testing. Our method demonstrated a significant improvement in performance, achieving a 7.03 percentage point increase in classification accuracy compared to state-of-the-art techniques. Additionally, we conducted ablation tests to further validate the effectiveness of individual features in our model. The results validate the efficacy of integrating HTML DOM structure and URL features using deep learning. Our framework significantly enhances phishing detection capabilities, providing a more accurate and comprehensive solution to identifying malicious webpages. [ABSTRACT FROM AUTHOR]

Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art anti-phishing research is highly fragmented and monolithic and does not address the problem from a pervasive computing perspective. In this survey, we aim to contribute to the existing literature by providing a systematic review of existing experimental phishing research that employs EEG and eye-tracking methods within multi-modal and multi-sensory interaction environments. The main research objective of this review is to examine articles that contain results of at least one EEG-based and/or eye-tracking-based experimental setup within a phishing context. The database search with specific search criteria yielded 651 articles from which, after the identification and the screening process, 42 articles were examined as per the execution of experiments using EEG or eye-tracking technologies in the context of phishing, resulting to a total of 18 distinct papers that were included in the analysis. This survey is approaching the subject across the following pillars: a) the experimental design practices with an emphasis on the applied EEG and eye-tracking acquisition protocols, b) the artificial intelligence and signal preprocessing techniques that were applied in those experiments, and finally, c) the phishing attack types examined. We also provide a roadmap for future research in the field by suggesting ideas on how to combine state-of-the-art gaze-based mechanisms with EEG technologies for advancing phishing research. This leads to a discussion on the best practices for designing EEG and gaze-based frameworks. [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]

In contemporary times, the proliferation of phishing attacks presents a substantial and growing challenge to cybersecurity. This fraudulent tactic is designed to deceive unsuspecting individuals, enticing them to access malicious websites and disclose sensitive personal information such as usernames, passwords, and financial details. As a result, malevolent actors exploit this data for illicit purposes. As the sophistication and maliciousness of phishing continue to evolve, researchers are earnestly developing multiple anti-phishing solutions in the literature. Among these solutions, those based on machine learning and deep learning models have gained substantial attention in recent years. This study proposes an intelligent mechanism to detect phishing URLs. The proposed system is based on the permutation importance method to select the most relevant URL features and the SMOTE-Tomek link method to solve the problem of an unbalanced dataset. In addition, the XGBoost classifier and four deep learning models—CNN, LSTM, and two hybrid models (CNN-LSTM and LSTM-CNN)—are employed to classify URLs as phishing or legitimate and to compare their performance. The experimental results demonstrate the successful functioning of the proposed phishing detection mechanism. It is observed that the proposed mechanism achieved an accuracy ranging from 93.36 to 97.05% without feature selection and data balance across two variants of datasets and different classifiers. It also achieved an accuracy ranging from 94.12 to 97.82% with feature selection and data balance. Finally, our phishing detection mechanism is implemented as a web application to enhance its usability for web users. [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]

Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effectively identifying fake pages. To address this challenge, we propose an image-based coding approach for detecting phishing attacks using a CNN-LSTM hybrid model. This approach combines SMOTE, an enhanced GAN based on the Autoencoder network, and swarm intelligence algorithms to balance the dataset, select informative features, and generate grayscale images. Experiments on three benchmark datasets demonstrate that the proposed method achieves superior accuracy, precision, and sensitivity compared to other techniques, effectively identifying phishing attacks and enhancing online security. [ABSTRACT FROM AUTHOR]

Nowadays, phishing attacks have grown rapidly, and there is an urgent need to introduce a suitable detection method that has the ability to detect different types of phishing attacks. This paper investigates the capability to use bio-inspired meta-heuristic algorithms to improve the performance of the detection engine for phishing attacks by reducing the number of features. This improvement was practiced by investigating the effectiveness of five meta-heuristic algorithms: Particle Swarm Optimization (PSO), Firefly Optimization Algorithm (FFA), Multi-Verse Optimizer (MVO), Moth-Flame Optimization algorithm (MFO), and BAT optimization algorithm, to select the relevant features that could be affected directly by different types of phishing attacks. The suggested detection model was tested and evaluated using four benchmark phishing attack datasets, and the Apache Spark-based decision tree algorithm was selected as a detection engine. The conducted experiments have demonstrated that the Spark-based MVO algorithm achieved the highest detection rate for detecting different types of phishing attacks within four phishing attack datasets. Moreover, the suggested detection model was able to reduce effectively the feature space, which could enhance the computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

Traditional intrusion detection systems rely on known patterns and irregularities. This study proposes an approach to reinforce security measures on QR codes used for marketing and identification. The former investigates the use of a lightweight Deep Learning (DL) model to detect cyberattacks embedded in QR codes. A model that classifies QR codes into three categories: normal, phishing, and malware, is proposed. The model achieves high precision and F1 scores for normal and phishing codes (Class 0 and 1), indicating accurate identification. However, the model's recall for malware (Class 2) is lower, suggesting potential missed detections in this category. This stresses the need for further exploration of techniques to improve the detection of malware QR codes. Despite the particular limitation, the overall accuracy of the model remains impressive at 99%, demonstrating its effectiveness in distinguishing normal and phishing codes from potentially malicious ones. [ABSTRACT FROM AUTHOR]

Internet users possess accounts on dozens of online services where they are often identified by one of their e-mail addresses. They often use the same address on multiple services and for communicating with their contacts. In this paper, we investigate attacks that enable an adversary (e.g., company, friend) to determine (stealthily or not) whether an individual, identified by their e-mail address, has an account on certain services (i.e., an account enumeration attack). Such attacks on account privacy have serious implications as information about one's accounts can be used to (1) profile them and (2) improve the effectiveness of phishing. We take a multifaceted approach and study these attacks through a combination of experiments (63 services), surveys (318 respondents), and focus groups (13 participants). We demonstrate the high vulnerability of popular services (93.7%) and the concerns of users about their account privacy, as well as their increased susceptibility to phishing e-mails that impersonate services on which they have an account. We also provide findings on the challenges in implementing countermeasures for service providers and on users' ideas for enhancing their account privacy. Finally, our interaction with national data protection authorities led to the inclusion of recommendations in their developers' guide. [ABSTRACT FROM AUTHOR]

Sound-squatting is a squatting technique that exploits similarities in word pronunciation to trick users into accessing malicious resources. It is an understudied threat that has gained traction with the popularity of smart speakers and audio-only content, such as podcasts. The picture gets even more complex when multiple languages are involved. We here introduce X-squatter, a multi- and cross-language AI-based system that relies on a Transformer Neural Network for generating high-quality sound-squatting candidates. We illustrate the use of X-squatter by searching for domain name squatting abuse across hundreds of millions of issued TLS certificates, alongside other squatting types. Key findings unveil that approximately 15% of generated sound-squatting candidates have associated TLS certificates, well above the prevalence of other squatting types (7%). Furthermore, we employ X-squatter to assess the potential for abuse in PyPI packages, revealing the existence of hundreds of candidates within a 3-year package history. Notably, our results suggest that the current platform checks cannot handle sound-squatting attacks, calling for better countermeasures. We believe X-squatter uncovers the usage of multilingual sound-squatting phenomena on the Internet and it is a crucial asset for proactive protection against the threat. [ABSTRACT FROM AUTHOR]

Phishing attacks continue to be a concern for academia and practice. Practitioners ranked phishing attacks second to data breaches in a recent industry survey. For scholars, interest in understanding the factors that influence phishing susceptibility, defined as user vulnerability to phishing attacks, continues to grow. While prior research has identified either state (situational cues) or trait (technology use) factors that influence users' response to phishing attacks, little previous research has investigated simultaneously user control of both state and trait factors on susceptibility to phishing. Additionally, the influence of users' automatic or routine technology use, user traits, on phishing susceptibility has not been examined. We investigate the effects of users' control of both state and trait factors on phishing susceptibility. Our results offer several interesting insights. Specifically, while routine technology use trait decreases phishing susceptibility, automatic technology use trait increases phishing susceptibility. Furthermore, while situational cues are related to phishing susceptibility, only users' automatic technology use is related to susceptibility to phishing under message sender situational cues. Our findings provide practical insights for developing countermeasures that incorporate the level of control into training programs that target trainees with customised training aimed at preventing successful phishing attacks. [ABSTRACT FROM AUTHOR]

There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy. [ABSTRACT FROM AUTHOR]

The article is devoted to the identification and analysis of new vectors of cybercrime development, their localization and prevention. The analysis of secondary data, questionnaires, and expert assessment are used as research methods for this problem. The statistics of cybercrime in foreign countries and Russia in recent years have been analyzed. It is found out how the domestic business has changed its attitude to the security of information and information systems of the company. The sectoral landscape of cybercrimes in the Russian Federation for 2023 is presented. New vectors of phishing development are considered in detail and the main resources forged by hackers during phishing attacks are identified. Special attention is paid to phishing by the method of distribution, which today poses the greatest danger to Russian companies. Spoofing has been studied as a new type of cyberattack, where masquerading as a legal object (computer, device or network) is used by hackers as a means of penetrating other computer networks. The characteristics of modern types of spoofing and the negative consequences for business that they lead to are given. The measures taken at the state level to counter new vectors of cybercrime development are indicated. The conclusion is substantiated that state support and information protection measures themselves eliminate the consequences rather than the causes of high cybercrime, which is why the security problem cannot be solved without the participation of domestic business and the population of the country. The consolidated participation of Russian business in the fight against cybercrime is noted, which found its concrete embodiment in the creation of the F.C.C.T. joint-stock company. The key areas of activity of this company in the information technology market and the results of its work in 2023 are highlighted. Attention is focused on the fact that the creation of a joint-stock company F.C.C.T. in the country does not fully solve the problem of ensuring information security, which primarily concerns small and medium-sized business structures. It is stated that many of them, especially in the Russian regions, are not ready to purchase information security services not only financially, but also organizationally. Due to the low demand of entrepreneurs for vulnerability search services in the company’s software, web applications and IT infrastructure, it is proposed to build a typical SOC that meets the most minimal standards for combating cybercrime. An algorithm has been developed to achieve them in the form of step-by-step implementation of simple organizational and methodological recommendations for the prevention of cybercrimes and minimizing their consequences for small and medium-sized businesses.

Despite widespread awareness of risks, significant investments in cybersecurity protection, and substantial economic incentives to avoid security breaches, organizations remain vulnerable to phishing attacks. Phishing research has informed effective practical interventions to address phishing susceptibility that emphasize the importance of broadly applicable IT security knowledge. Yet employees still frequently fall victim to phishing attempts. To help understand why, we conceptualize phishing susceptibility as the failure to differentiate between deceptive and legitimate information processing requests that occur within the context of an employee's typical job responsibilities. We apply this contextual lens to identify characteristics of knowledge workers' organizational task and social context that may enhance or diminish performance in detecting deception in phishing email attempts. To test our hypotheses, we conducted a study in which employees of the finance division of a large university encountered simulated email-based phishing attempts as part of their normal work routine. We found evidence supporting our hypotheses that an individual's susceptibility to phishing attacks is influenced by their position in the knowledge flows of the organization and by the impact of workgroup responsibilities on their cognitive processing. We contend that phishing susceptibility is not merely a matter of IT security knowledge but is also influenced by contextualized, multilevel influences on information processing. As phishing attacks are increasingly targeted to specific organizational settings, it is even more important to incorporate this contextualized information processing view of phishing susceptibility. [ABSTRACT FROM AUTHOR]

The article examines the legal and ethical aspects of a person's responsibility related to the use of the Internet as a means of mass information. It is noted that psychotraumatization by war has become an everyday part of modern life, the media space has changed, which can become a powerful stressor and lead to media trauma of the population and a gradual decrease in the level of its psychological stability. In order to counteract dangerous influences on the Internet, the socialization of the population must be transformed, ensuring stress resistance and mental health of citizens through increasing legal and media literacy. It is noted that a modern person is not only a consumer of content, but also its author, that is, he can freely collect, store, use and distribute information orally, in writing or in another way - as he chooses. However, such information must be disseminated in compliance with the legal norms regarding its authenticity and must not tarnish the honor, dignity and business reputation of other persons, their mental health, i.e. everyone must be aware of their social responsibility for publicizing sensitive information for society. The article describes various types of offenses, such as: cybercrime, phishing, inciting enmity, spreading false information, insult and defamation, copyright infringement, breach of privacy, misuse of Internet resources, spam and unsolicited messages. The features of civil, administrative and criminal liability for the listed offenses are characterized. Also, the article states that persons who distribute illegal content may be held liable. Web resources and their officials can be held responsible only if they ignored the demands of the rights holders to stop copyright infringement or ignored content that is illegal in itself. The article demonstrates that every user must be aware that his actions can have real consequences for himself and other people. It is noted that it is important to adhere to ethical standards, including respect for the privacy of others, credibility and responsibility for the information that is published. Compliance with legal norms and ethical principles is the basis of safe and responsible use of the Internet. [ABSTRACT FROM AUTHOR]

User susceptibility to phishing messages on social media is a growing information security concern. Contingency factors that can influence this susceptibility and the theoretical mechanisms through which they operate need more scholarly attention. To bridge this gap, we present a temptation and restraint (TR) model (a specific manifestation of the dual–system theory) of social media phishing susceptibility, which explains it as an outcome of a struggle between users' temptation toward engaging with a social media phishing message and their cognitive and behavioral restraint against it. The balance in this struggle is a function of various situational contingencies. First, via a Delphi study, we identify four key situational contingency factors in the context of social media that can influence this balance: (1) poor sleep quality, (2) social media ostracism, (3) source likability, and (4) fear appeals. Next, via five randomized controlled experiments using an ostensible social media paradigm with social media users, we show that the TR model explains (a) why and how users engage with social media phishing messages, and (b) when users are more or less susceptible to it based on key situational contingency factors. Our findings offer a nuanced perspective on social media phishing susceptibility, elucidate the fundamental roles of situational contingencies in the genesis of social media phishing victimization, and delineate important directions for future research in this area [ABSTRACT FROM AUTHOR]

For enterprises that do business worldwide, cybersecurity can be a complex undertaking, as risks such as phishing attacks by threat actors continuously evolve across the globe to bypass traditional defenses [...]

As a result, SAP is always evolving its security measures to stay ahead of cyber threats. The company recently launched a dynamic application security scanning system to detect vulnerabilities that [...]

What is dark web monitoring? Dark web monitoring is a service often offered by cybersecurity vendors that scans the dark web for information pertaining to an organization. These software scan [...]

Researchers have identified an attack that impersonates tax authorities from several countries to compromise organizations and deploy a custom backdoor program dubbed Voldemort. While the campaign uses tactics seen in [...]

Artificial Intelligence (AI) has long been recognized for its role in detecting phishing attempts, but its capabilities extend far beyond that. With the rise of sophisticated cyber threats, AI has [...]