Into the Reverie: Exploration of the Dream Market

Carr, Theo; Zhuang, Jun; Sablan, Dwight; LaRue, Emma; Wu, Yubao; Hasan, Mohammad Al; Mohler, George

doi:10.1109/bigdata47090.2019.9006092

Cited by 2 publications

(1 citation statement)

References 19 publications

(14 reference statements)

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“…While individuals are aware of these risks and take measures to protect their privacy and security, the ability to differentiate between legal and illegal content on the dark web is not universal. [13][14][15][16][17][18] It is important to note that the available data does not directly measure mental health outcomes, and further research is needed to establish a robust link between dark web usage and mental health. Longitudinal studies, qualitative interviews, and psychological assessments can provide a more comprehensive understanding of the psychological impact of dark web usage.…”

Section: Discussionmentioning

confidence: 99%

The dark web: A hidden menace or a tool for privacy protection

Kaur,

Mukherjee,

Moza

et al. 2024

IJFMTS

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The dark web has long been a subject of debate, as it raises questions about its covert threats and privacy safeguarding capabilities. It's an internet realm requiring specific software or authorization, notorious for illegal activities and untraceable transactions. Researchers and law enforcement agencies use the dark web for intelligence gathering on cybercriminals, making it crucial in understanding the evolution of the internet and emerging criminal activities. Additionally, studying the dark web helps identify cybersecurity threats and vulnerabilities. This research explores the relationship between the dark web and mental health, investigating the public's perception of the dark web as both a hidden menace and a tool for privacy protection. The primary aim is to examine the correlation between public perception and engagement in dark web services, while also exploring potential mental health ramifications. To collect data, we conducted a survey among university students and regular internet users. The research process involved identifying 18 research questions covering topics such as dark web knowledge, usage frequency, security measures, awareness of government regulations, ability to differentiate legal and illegal content, transaction methods, and understanding the dark web's role in promoting freedom of speech. We collected 158 responses from different departments of Chandigarh University, removing duplicates to retain 156 responses meeting our criteria. Data was compiled in an Excel file for further statistical evaluation. While the dark web is not widely accessed, users may experience psychological consequences due to its association with cybercrime, disturbing content, and radicalization. This connection leads to heightened anxiety, fear, and distress among users. Concerns about privacy, security, and associated risks further contribute to these apprehensions. Motivations for dark web access, such as curiosity and anonymity, may stem from underlying psychological factors. However, navigating illegal and potentially harmful content can result in emotional turmoil, moral dilemmas, guilt, or shame. These findings highlight the need for public awareness campaigns, educational initiatives, and targeted interventions promoting mental well-being and safe internet practices. Enhancing individuals' knowledge about the dark web's risks and potential psychological consequences empowers them to make informed decisions and protect their mental health in the digital age.

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Section: Discussionmentioning

confidence: 99%

The dark web: A hidden menace or a tool for privacy protection

Kaur,

Mukherjee,

Moza

et al. 2024

IJFMTS

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Efficient Bank Fraud Detection with Machine Learning

Zhang,

Cheng,

Wang

et al. 2023

JCMEA

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The rapid expansion of IT technology has led to a significant increase in financial transactions, accompanied by a corresponding rise in fraudulent activities. This paper tackles the challenge of detecting fraud in various forms, such as credit card fraud, banker cheque fraud, and online funds transfer fraud, which have become increasingly sophisticated. Traditional methods struggle to keep pace with these evolving fraud techniques, necessitating advanced approaches. We propose the use of machine learning algorithms to enhance the detection of fraudulent transactions. Utilizing the BankSim dataset from Kaggle, which includes features like age, gender, payment domain, and transaction amount, we conducted a comprehensive analysis. The dataset was preprocessed to handle missing values and balance the instances of fraud. We then applied several machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM), training these models on a training set and evaluating them on a test set. The performance of these models was assessed using precision, recall, and F1-measure metrics. Our findings demonstrate that the SVM algorithm achieved the highest accuracy at 99.23%, significantly outperforming the other algorithms and previous studies. This study highlights the effectiveness of machine learning, particularly SVM, in developing robust fraud detection systems, offering a promising solution to improve financial security.

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Into the Reverie: Exploration of the Dream Market

Cited by 2 publications

References 19 publications

The dark web: A hidden menace or a tool for privacy protection

The dark web: A hidden menace or a tool for privacy protection

Efficient Bank Fraud Detection with Machine Learning

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