Search Results (22)

In such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. The framework is evaluated using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset. The research methodology applied robust preprocessing, the implementation of a compact autoencoder architecture, and a threshold-based anomaly detection strategy. Evaluation metrics, such as confusion matrices, receiver operating characteristic (ROC) curves, precision–recall (PR) curves, and reconstruction error distributions, are used to assess the model’s performance. Also, a threshold sensitivity analysis has been applied to explore detection trade-offs at varying levels of strictness. Although the model’s recall remains modest due to class imbalance, it demonstrates strong precision at higher thresholds, which demonstrates its utility in minimizing false positives. Overall, this research study is a practical and privacy-conscious approach to fraud detection that aligns with the operational realities of financial institutions and regulatory compliance toward scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments. Full article

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article

Technological advancements have helped all sectors to evolve. This advancement has widened the cyberspace and attack surface, which has led to a drastic increase in cyberattacks. Cybersecurity solutions have also evolved. The advancement is relatively slower in developing countries. However, the financial sector in developing countries has shown resistance to cyberattacks. This paper investigates the reasons for this resistance. Despite using legacy systems, the banking sector in Pakistan has demonstrated resistance to cyberattacks. The research used a qualitative approach. Semi-structured interviews were conducted with nine cybersecurity experts in the banking sector to illustrate the reasons for this cybersecurity resistance. The research focused on cybersecurity experts in the banking sector, recognizing that this industry is particularly prone to cyberattacks on a global scale. The study utilised a thematic analysis technique to find resistance factors. The analysis suggests that the opportunity cost of cyberattacks and lower attack surface in developing countries like Pakistan are the main reasons for the lower financial losses. The findings of this research will encourage the adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) for cybersecurity in developing countries’ banking and financial sectors. Full article

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of “How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?” has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). Full article

As cybercrime proliferates globally, law enforcement agencies face significant challenges in responding effectively. This essay shares perspectives from Singapore, where cybercrime accounted for about 70% of the total annual crime in 2023, with no clear data on case resolution rates. This situation reflects a broader global trend and highlights the need to reconceptualize policing objectives in cyberspace. The fundamental differences between cybercrime and physical crime necessitate a shift from emphasizing the identification and prosecution of perpetrators to adopting a harm-centric perspective. Under this perspective, structures and policies should be implemented to disrupt financial flows, ensure data security, disrupt the spread of harmful content, and prevent physical damage. Once this is done, strategies such as public–private partnerships, international cooperation, and training and building capabilities to address specific harms can be more effectively implemented to mitigate the growing threat that cybercrime poses worldwide. Full article

With the emergence of blockchain technology, the cryptocurrency market has experienced significant growth in recent years, simultaneously fostering environments conducive to cybercrimes such as phishing scams. Phishing scams on blockchain platforms like Ethereum have become a grave economic threat. Consequently, there is a pressing demand for effective detection mechanisms for these phishing activities to establish a secure financial transaction environment. However, existing methods typically utilize only the most recent transaction record when constructing features, resulting in the loss of vast amounts of transaction data and failing to adequately reflect the characteristics of nodes. Addressing this need, this study introduces a multiscale feature fusion approach integrated with a graph convolutional network model to detect phishing scams on Ethereum. A node basic feature set comprising 12 features is initially designed based on the Ethereum transaction dataset in the basic feature module. Subsequently, in the edge embedding representation module, all transaction times and amounts between two nodes are sorted, and a gate recurrent unit (GRU) neural network is employed to capture the temporal features within this transaction sequence, generating a fixed-length edge embedding representation from variable-length input. In the time trading feature module, attention weights are allocated to all embedding representations surrounding a node, aggregating the edge embedding representations and structural relationships into the node. Finally, combining basic and time trading features of the node, graph convolutional networks (GCNs), SAGEConv, and graph attention networks (GATs) are utilized to classify phishing nodes. The performance of these three graph convolution-based deep learning models is validated on a real Ethereum phishing scam dataset, demonstrating commendable efficiency. Among these, SAGEConv achieves an F1-score of 0.958, an AUC-ROC value of 0.956, and an AUC-PR value of 0.949, outperforming existing methods and baseline models. Full article

Business Email Compromise (BEC) attacks have emerged as serious threats to organizations in recent years, exploiting social engineering and malware to dupe victims into divulging confidential information and executing fraudulent transactions. This paper provides a comprehensive review of BEC attacks, including their principles, techniques, and impacts on enterprises. In light of the rising tide of BEC attacks globally and their significant financial impact on business, it is crucial to understand their modus operandi and adopt proactive measures to protect sensitive information and prevent financial losses. This study offers valuable recommendations and insights for organizations seeking to enhance their cybersecurity posture and mitigate the risks associated with BEC attacks. Moreover, we analyze the Greek landscape of cyberattacks, focusing on the existing regulatory framework and the measures taken to prevent and respond to cybercrime in accordance with the NIS Directives of the EU. By examining the Greek landscape, we gain insights into the effectiveness of countermeasures in this region, as well as the challenges and opportunities for improving cybersecurity practices. Full article

The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people’s lives on social and psychological levels. Conventional practice involves cyber experts sourcing data from various outlets and applying personal discernment and rational inference to manually formulate cyber intelligence specific to a country. This traditional approach introduces personal bias towards the country-level cyber reports. However, this paper reports a novel approach where country-level cyber intelligence is automatically generated with artificial intelligence (AI), employing cyber-related social media posts and open-source cyber-attack statistics. Our innovative cyber threat intelligence solution examined 37,386 tweets from 30,706 users in 54 languages using sentiment analysis, translation, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), N-gram, and Porter stemming. Moreover, the presented study utilized 238,220 open-intelligence cyber-attack statistics from eight different web links, to create a historical cyber-attack dataset. Subsequently, AI-based algorithms, like convolutional neural network (CNN), and exponential smoothing were used for AI-driven insights. With the confluence of the voluminous Twitter-derived data and the array of open-intelligence cyber-attack statistics, orchestrated by the AI-driven algorithms, the presented approach generated seven-dimensional cyber intelligence for Australia and China in complete automation. Finally, the topic analysis on the cyber-related social media messages revealed seven main themes for both Australia and China. This methodology possesses the inherent capability to effortlessly engender cyber intelligence for any country, employing an autonomous modality within the realm of pervasive computational platforms. Full article

The growing prevalence of cybercrime, particularly among young adults, necessitates the promotion of digital citizenship to educate students about responsible online behavior and to equip them with the skills to mitigate cyber risks. The specific objective of this study was to investigate the effect of digital citizenship skills on the prevention of cybercrime among higher education students. A mixed-method approach, including surveys and interviews, was employed to collect data from 652 students in Saudi Arabia. This study found that digital citizenship generally has a significant impact on students’ awareness and prevention of cybercrime through the development of responsible online behavior. Knowledge of digital law came first, followed by beliefs about digital manners. Digital communication skills came third, followed by digital rights, knowledge, and duties in fourth place. Then, digital commerce skills and digital health beliefs came fifth and sixth, respectively. This was followed by digital access skills, then digital security, and finally digital culture. The results also revealed a negative statistical relationship between digital citizenship and cybercrimes’ various forms including national, financial, banking, social, immoral, insulting, slanderous, defaming, threatening, and harassment in virtual learning environments. These findings have significant implications for the understanding of how higher education institutions can promote digital citizenship and prevent cybercrime by integrating digital citizenship education into their curriculum, providing training for educators, and establishing clear policies and guidelines for responsible online behavior. Full article

Cybercrime presents a significant threat to global society. With the number of cybercrimes increasing year after year and the financial losses escalating, law enforcement must advance its capacity to identify cybercriminals, collect probative evidence, and bring cybercriminals before the courts. Arguably to date, the approach to combatting cybercrime has been technologically centric (e.g., anti-virus, anti-spyware). Cybercrimes, however, are the result of human activities based on human motives. It is, therefore, important that any comprehensive law enforcement strategy for combatting cybercrime includes a deeper understanding of the hackers that sit behind the keyboards. The purpose of this systematic review was to examine the state of the literature relating to the application of a human-centric investigative tool (i.e., profiling) to cybercrime by conducting a qualitative meta-synthesis. Adhering to the PRISMA 2020 guidelines, this systematic review focuses specifically on cybercrime where a computer is the target (e.g., hacking, DDoS, distribution of malware). Using a comprehensive search strategy, this review used the following search terms: “cybercrime”, “computer crime”, “internet crime”, “cybercriminal”, “hacker”, “black hat”, “profiling”, “criminal profiling”, “psychological profiling”, “offender profiling”, “criminal investigative analysis”, “behavioral profiling”, “behavioral analysis”, “personality profiling”, “investigative psychology”, and “behavioral evidence analysis” in all combinations to identify the relevant literature in the ACM Digital Library, EBSCOhost databases, IEEE Xplore, ProQuest, Scopus, PsychInfo, and Google Scholar. After applying the inclusion/exclusion criteria, a total of 72 articles were included in the review. This article utilizes a systematic review of the current literature on cyber profiling as a foundation for the development of a comprehensive framework for applying profiling techniques to cybercrime—described as cyber behavioral analysis (CBA). Despite decades of research, our understanding of cybercriminals remains limited. A lack of dedicated researchers, the paucity of research regarding human behavior mediated by technology, and limited access to datasets have hindered progress. The aim of this article was to advance the knowledge base in cyber behavioral sciences, and in doing so, inform future empirical research relating to the traits and characteristics of cybercriminals along with the application of profiling techniques and methodologies to cybercrime. Full article

The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both social media and web sources. The proposed method underwent evaluation from 13 October 2022 to 6 April 2023, utilizing a dataset comprising 37,386 tweets generated by 30,706 users across 54 languages. To address non-English content, a total of 8199 HTTP requests were made to facilitate translation. Additionally, the system processed 238,220 cyber threat data from the web. Within a remarkably brief duration of 6 s, the system autonomously generated a comprehensive cyber intelligence report encompassing 7 critical dimensions of cyber intelligence for countries such as Russia, Ukraine, China, Iran, India, and Australia. Full article

Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this method to guard their data centers and other digital systems. The lack of scalability, sluggish response times, and inability to spot advanced and insider threats are among some of the problems with conventional approaches to network security. These flaws highlight the need for research to build more efficient and all-encompassing security methods to guard against the expanding variety of network attacks. Cybercriminals use AI and data poisoning, as well as model theft strategies to automate their attacks. A cyber security technique based on artificial intelligence is presented in this study for financial sector management (CS-FSM). In order to map and prevent unexpected risks from devouring a business, artificial intelligence is one of the best technologies. Using the proposed technique, cyberattack problems can be classified and solved. To ensure the security of financial sector information, algorithms such as the Enhanced Encryption Standard (EES) encrypt and decrypt data. By learning from the training data, the K-Nearest Neighbor (KNN) algorithm produces predictions. In the financial sector, it is used to detect and stop malware attacks. The proposed method increases cyber security systems’ performance by increasing their defense against cyberattacks. CS-FSM enhances data privacy (18.3%), scalability (17.2%), risk reduction (13.2%), data protection (16.2%), and attack avoidance (11.2%) ratios. Full article

The current study is primarily concerned with the developments in financial technology, or fintech, that have significantly altered traditional financial systems, focusing on several risk categories that have emerged in the financial technology sector’s digital ecosystem. This paper is a review of existing literature related to the risk landscape in fintech, particularly its publication trend, journal productivity, impact, affiliated organizations, and related themes. A bibliometric and content analysis of 84 articles collected through Scopus’ structured database is performed for a comprehensive review. It is revealed that financial technology development has decreased physical crime while simultaneously increasing cybercrime. Another challenge is the asymmetrical technology between financial markets and the relevant supervisors. These current issues necessitate the creation of an Act on Fintech to create a comprehensive legislative framework. The present study’s findings are helpful for academia and industry to aid their existing knowledge about fintech and associated risks, particularly its timeline, geographical spread, and development of coherent themes. Full article

The number of fraud occurrences in electronic banking is rising each year. Experts in the field of cybercrime are continuously monitoring and verifying network infrastructure and transaction systems. Dedicated threat response teams (CSIRTs) are used by organizations to ensure security and stop cyber attacks. Financial institutions are well aware of this and have increased funding for CSIRTs and antifraud software. If the company has a rule-based antifraud system, the CSIRT can examine fraud cases and create rules to counter the threat. If not, they can attempt to analyze Internet traffic down to the packet level and look for anomalies before adding network rules to proxy or firewall servers to mitigate the threat. However, this does not always solve the issues, because transactions occasionally receive a “gray” rating. Nevertheless, the bank is unable to approve every gray transaction because the number of call center employees is insufficient to make this possible. In this study, we designed a machine-learning-based rating system that provides early warnings against financial fraud. We present the system architecture together with the new ML-based scoring extension, which examines customer logins from the banking transaction system. The suggested method enhances the organization’s rule-based fraud prevention system. Because they occur immediately after the client identification and authorization process, the system can quickly identify gray operations. The suggested method reduces the amount of successful fraud and improves call center queue administration. Full article

This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests regarding the terms “Cyberattacks on the computer systems of a financial institution”, “Cyberattacks on the network infrastructure of a financial institution”, and “Cyberattacks on the cloud infra-structure of a financial institution”, obtained with Google Trends for the period from 16 April 2017 to 4 October 2022. The authors examined the data using the Z-score, the QS test, and the method of differences of average levels. The data were found to be non-stationary with outliers and a seasonal component, so exponential smoothing was applied to reduce fluctuations and clarify the trends. As a result, the authors built additive and multiplicative cyclical and trend-cyclical models with linear, exponential, and damped trends. According to the models’ quality evaluation, the best results were shown by the trend-cyclic additive models with an exponential trend for predicting cyberattacks on computer systems and the cloud infrastructure and a trend-cyclic additive model with a damped tendency for predicting cyberattacks on the network infrastructure. The obtained results indicate that the U.S. can expect cybercrimes in the country’s financial system in the short and medium term and develop appropriate countermeasures of a financial institution to reduce potential financial losses. Full article