Privacy and data security is an extremely crucial thing for learning management systems such as Zoom. In the recent year, due to the covid-19 outbreak many educational, businesses field organizations continue their offline meeting and classes to online activities by using applications such as Zoom. Therefore, the daily increase of users in zoom has caught the eye of the hackers. The hackers tend to attack the credentials of those users in zoom and specifically steal the data that has been stored in zoom and sell the information obtained to the dark web. After reading some articles, the researchers suggested implementing the identity and access management application which authority can use to control the accessibility of each user in the zoom application. The application will be able to trace the reason of each user accessing into the system and to see if they have any suspicion on the activities that they were doing. The implemented application will bring a good impact where it can safeguard the user’s privacy and data to allow the user to have worry free when using the application. However, after implementing the application, the researcher uses questionnaires to obtain the raw data from around 200 users from different backgrounds

Identification of Criminal Networks via External Identifier-based Clustering on the Dark Web

In the shadow of silence: Modelling missing data in the dark networks of crime and terrorists

This essay examines the phenomenon of suicide forums in the dark web using material from recent studies published at the widely accessible part of the Internet (surface web). Namely, we clarify the terms “cybersuicide” and “Werther effect”, examine the personality traits that are linked to suicidal tendencies, and analyse the differentiation between the terms “deep web”, “dark net”, and “dark web”, which often get mixed up in everyday use. At the same time, we examine the way suicide is propagated to young people through the dark web forums, report on the basic conversation topics that take place there, study the profiles of the people who constitute them, the motives behind user participation, but also the possibilities for preventing young people’s exposure to these web-sites. The aim of this essay is to achieve a critical understanding of the ominous but real phenomenon of online communities involved in the issue of suicide in Greece and Slovakia and to shed light on the dangers that lurk for young people who navigate the side of the Internet that hosts as much information as it does dangers.

Tales of twin forums on the dark and surface web: social interactions and user sustainability in anonymous online communities

As artificial intelligence reshapes the cybersecurity landscape, the demand for a trustworthy, real-time intelligence platform to track security incidents has become mission-critical. This paper proposes AGS-INTEL, an AI-driven platform designed to revolutionize data breach intelligence by providing a credible, real-time repository that consolidates, verifies, and contextualizes global security incidents. Unlike traditional databases, AGS-INTEL employs a validated scoring algorithm and enriched metadata to capture breach dimensions (legal, technical, sectoral, geopolitical), drawing from GDPR/HIPAA disclosures, threat intelligence, dark web forums, and academic reports, among other sources. Utilizing NLP and agentic AI, it extracts structured metadata from unstructured narratives while integrating ethical data scraping, regulatory compliance, and cross-jurisdictional filtering to ensure high fidelity. A visual analytics dashboard empowers stakeholders, including regulators, policymakers, cybersecurity professionals, and journalists, to analyze breach trends by industry, geography, and threat modality, enhancing transparency and risk governance. By delivering authenticated, actionable data, AGS-INTEL addresses critical gaps in existing tools, setting a new standard for ethical AI in breach intelligence and strengthening societal resilience against escalating cyber threats.

Despite the emergence of the dark web more than 20 years ago, little scholarly attention has focused on identifying potential mental health differences between dark web users and surface web users. Yet, given the pseudo-anonymous nature of the dark web and the purported privacy it provides, individuals with mental health vulnerabilities may be inclined to use the dark web. In the present study, we investigate this matter by drawing on survey data collected in 2024 from a national sample of 2,000 U.S. adults. The results of both bivariate and multivariate analyses indicate that dark web users exhibit greater depressive symptoms and have more paranoid thoughts than surface web users. Likewise, dark web users are more likely than surface web users to report suicidal thoughts, nonsuicidal self-injury, and engagement in digital self-harm. Discussion centers on the implications of these findings for practice as well as avenues for future research.

The dark web is a hidden part of the internet that allows users to communicate securely and anonymously, often using applications such as Tor. This paper specifically addresses the use of Elliptic Curve Cryptography (ECC) for enhanced security within a dark web context, where, although traditional cryptographic algorithms, such as RSA, possess unassailable cryptographic value, they are often computationally inefficient for non-standard computing environments, and do not scale well. We compare ECC and RSA performance in terms of key generation time, encryption/decryption time, and memory usage, and find that ECC outperforms RSA across all metrics in challenging, limitedresource networks. In our testing, we simulate the real-world operational environment of anonymizing networks by using test messages and message flow logs that are anonymized. We demonstrate the relative improvements in computational time and memory usage of ECC over RSA while maintaining equivalent cryptographic strength. Using these results, we create an integrated multi-layered security construct, which uses ECC, evaluates and classifies threat information using machine learning methods to detect anomalies in near real-time, and constructs a blockchain model to allow decentralized audit trail tracking, resulting in a substantially enhanced security and privacy solution to address the unique requirements of anonymous communication in a dark web environment. This study helps to address the lack of empirical evaluations of ECC in dark web contexts, presenting a practical roadmap for implementing innovative cryptographic and analysis protocols for digital anonymity. Various outcomes support the efficacy of pairing lightweight encryption with intelligent behavioural analytics to counter evolving cyber threats. The framework provides a scalable, flexible, and consistently relevant option for countering a rapidly changing threat while enabling future work on post-quantum cryptography.

The expansion of cryptocurrency has reshaped financial systems by enabling decentralised transactions and new digital applications. However, its pseudonymity and global reach have also enabled ransomware, darknet trading, money laundering, and terrorism financing, creating challenges for regulators seeking to protect financial integrity. This study examines the intersection of cryptocurrency, cybercrime, and governance. It identifies techniques exploited by criminals, evaluates forensic and regulatory countermeasures, and considers the governance dilemmas posed by these developments. A qualitative, desk-based approach was adopted, drawing on peer-reviewed literature, institutional policy reports (FATF, IMF, Europol), and industry analyses (Chainalysis, Elliptic, TRM Labs). Thematic content analysis was used to trace patterns of illicit cryptocurrency use, enforcement actions, and regulatory innovations. The findings reveal that blockchain forensics and coordinated policy efforts have strengthened oversight. Yet criminals increasingly use privacy coins, decentralised finance protocols, mixers, and cross-chain laundering to bypass detection. Enforcement remains inconsistent, hindered by fragmented regulation and gaps in cross-border cooperation. Cryptocurrency-enabled crime and terrorism financing remain adaptive threats that test financial stability and governance frameworks. Stronger international coordination, harmonised regulation, and advanced forensic tools are essential to reduce risks. The study recommends deepening cross-border collaboration, investing in blockchain analytics, and adopting flexible governance models that balance innovation with accountability.

Employee credentials like email addresses, passwords, API keys are increasingly targeted by cybercriminals for unauthorized access, phishing and other cyberattacks. According to Verizon's 2025 Data Breach Investigations Report, stolen credentials were involved in 53% of all data breaches in 2025, making credential compromise the most prevalent attack vector in the digital threat landscape. The average cost of a credential-based data breach is estimated to be about $4.67 million, with IBM's 2025 Cost of a Data Breach Report revealing that organizations take an average of 246 days to identify and contain such incidents. Additionally, 94% of passwords are reused across multiple accounts, and credential stuffing attackswhich automate the replay of stolen credentials. While large organizations can afford dedicated Security Operations Centers (SOCs) and expensive breach monitoring tools, small and medium-sized enterprises (SMEs), educational institutions and healthcare organizations often lack even the most basic systems required for credential leak detection. Existing solutions rely on static breach databases or manual user checks, missing real-time and organizationspecific exposures critical for early incident response and containment. We developed BreachGuard, an automated real-time credential leak tracking system designed for organizational cybersecurity. The system continuously scans multiple public sourcesincluding Have I Been Pwned (HIBP) breach database API, Pastebin and GitHub repositoriesusing pattern-based detection (regex), web scraping and API integration to identify employees and their domain credentials. Upon detection, the system triggers instant Slack and email alerts with severity classification and well-known remediation steps. Critical findings show that this system operates at comparatively low cost, making it much more affordable than its commercial alternatives. The open-source design facilitates future extensions for dark web monitoring, machine learningbased detection and SIEM/SOAR integration. By bridging the detection gap through automation and enabling early identification of credential leaks, BreachGuard demonstrates that proactive, multi-source credential monitoring is feasible and economically viable for organizations of all sizes enabling faster detection, remediation of credential-based security incidents and significantly reducing breach costs.

The paper presents the implementation of an automated Open Source Intelligence (OSINT) tool named DarkReaper . It focuses on modular OSINT automation using Python for gathering intelligence from surface and dark web sources. The implementation includes modules for phone numbers, email address, image,ip address and username lookups. Each module performs multi-source data extraction, analysis, and stores structured JSON outputs. The tool demonstrates high efficiency, modularity, and extensibility for cybersecurity investigation tasks.

How do the emerging Web 3.0 technologies affect the survival of non-state armed groups (NSAGs) in their violent struggles vis-à-vis state entities? While techno-optimists argue that Web 3.0 can democratize the internet and curb monopolistic practices, its decentralized features, such as enhanced privacy, data ownership, and personalization, also present significant security challenges. These technologies can be weaponized by NSAGs to promote their efficiency and resilience. Borrowing insights from social movement theory, we construct a theoretical framework to explain how Web 3.0 applications affect the dynamics of NSAGs by impacting their organizational modes and strategies. It is argued that blockchain-based platforms, metaverse projects, and other Web 3.0 technologies promote the efficiency of the recruitment, training, financing, purchasing, and communication processes of NSAGs, increasing their capacities as social organizations, and thereby render these groups more resilient to collapse. We illustrate and corroborate our theoretical claims by examining the cases of how NSAGs such as the Islamic State utilize decentralized crypto exchanges and the Dark Web in their operations.

Virtual currencies, owing to their technical characteristics such as anonymity, decentralisation, and instantaneous cross-border flows, have become a "grey area" for money laundering activities. Models such as dark web transactions and coin mixing services further exacerbate the concealment and complexity of these risks. Despite China's prohibitive regulatory policies, underground and cross-border transaction trends have intensified. Compounded by institutional deficienciesincluding inconsistent global legal characterisation, inadequate inter-departmental and cross-border coordination, regulatory gaps for emerging business models, and ineffective oversight of Virtual Asset Service Providers (VASPs)anti-money laundering monitoring and enforcement face severe challenges. This paper analyses typical virtual currency money laundering patterns and regulatory shortcomings based on the risk generation logic of overlapping technological characteristics and regulatory environments. It proposes a comprehensive governance approach: clarifying the "non-financial virtual commodity" nature of virtual currencies and refining the regulatory framework; strengthening VASPs' end-to-end compliance and AML obligations; enhancing traceability capabilities through intelligent monitoring and blockchain analysis; and deepening international and domestic multi-departmental collaboration under the FATF framework. This research holds significant theoretical and practical implications for preventing financial risks and safeguarding economic security.

Dark web is an obscure part of the internet which offers the anonymity and privacy to a user but also subjects a user to unlawful activities such as cybercrime, black marketplace and sale of dangerous goods. Although there is a reasonable amount of literature regarding the technical and criminal aspects of the dark web, a little is done on how students as one of the most active online communities perceive its existence and its associated threats to the online space. The current paper, DarkWeb Aware focuses on the awareness, attitudes, and perceptions of the students towards the dark web and how parental guidance influences the online activities. Open-ended questionnaires based on a descriptive-qualitative approach were used to administer questionnaires to the 100 college students of Technological University of the Philippines-Taguig. A thematic analysis revealed that there were some prominent trends in the sources of awareness, misconceptions, and interest in the dark web. The findings have shown that most students first read about the dark web through social media, with most mentioning the criminal that can lead to false knowledge or potentially harmful interest. Ignorance as to what the dark web was all about was very high with most of the students viewing it as a more dangerous space than a privacy device. Parents were not so aware or supervising. The results have shown that there is significant lack of co-relation between parental and student digital literacy and online student participation. The paper concludes that the digital literacy levels in schools and homes should be enhanced to enable the young generation to have safer and informed online behaviors.

Through a six-month operational deployment with law enforcement agencies, this study introduces the Quantum Threat Detection Model (QTDM), a groundbreaking hybrid quantum-classical framework that exhibits quantifiable quantum advantage in counter-narcotics cybersecurity. The framework integrates NISQ-era quantum processors with dynamic workload partitioning and quantum kernel techniques to overcome significant constraints of conventional AI systems in the analysis of encrypted dark web transactions. Three groundbreaking contributions are shown via empirical validation: (1) 94.3% (±1.2%) classification accuracy for dark web drug transactions, which is 5.8 times faster than traditional GPU clusters in processing encrypted data; (2) finding a 10-qubit performance plateau and a 0.5% error rate threshold, which establishes ideal boundaries for resource allocation in NISQ-era implementations; and (3) the first GDPR/CCPA-aligned ethical governance protocol for quantum-powered surveillance, which includes algorithmic bias monitoring and quantum warrant procedures. Operational findings include 76% early detection rate for synthetic opioids, 92% adversarial resistance against GAN-generated obfuscation, and 42% improvement in trafficking network identification. The QTDM framework lowers the threat detection latency from 47 minutes to 8.2 minutes while processing 2.4 million transactions per day with 98.7% uptime. By offering a technological architecture and policy framework for the ethical implementation of quantum technology in international security applications, this study establishes quantum cybersecurity as an operational reality rather than a theoretical potential.

Dark web forums are critical platforms for illicit activities and anonymous communication, making their analysis essential for cybersecurity, law enforcement, and academic research. This systematic literature review synthesises methodologies for data collection and analysis of dark web forum content. Following PRISMA 2020 guidelines, we searched SciSpace, Google Scholar, and PubMed, identifying 364 papers, of which 11 provided detailed methodological insights. Key methodologies include web crawling, machine learning, natural language processing, and social network analysis. Results show the dominance of Python-based automated tools, with hybrid approaches combining automation and manual verification proving most effective. Challenges include ethical considerations, data accessibility, and platform dynamism. The field is maturing but requires standardised frameworks and improved reproducibility. This review outlines current practices, evaluates methodological effectiveness, and suggests future directions for research and application.

In this paper, the author discusses the complex issues of cross-border jurisdiction and enforcement of intellectual property laws as relates to the distribution of Child Sexual Abuse Material content within the Dark Web and specifically the legal and technological environment in India. It examines the complex trends of digital abuse and its resulting effects on the victimization of Indian women, in the narrower context of non-consensual image-based harassment, but it also contemplates a larger problem of online child sexual abuse and exploitation that is facilitated by the ubiquitous digital technologies. The paper also examines how the anonymity provided by the Dark Web, along with the advent of cryptocurrency markets, makes investigating and prosecuting hackers more difficult, providing a complicated sentencing ground on such crimes. Another factor that contributes to this challenge is the prevalence of child pornography on the internet that would require stringent legal systems and international collaboration to protect the children around the world. Coupled with the disastrous figures that show how every third internet user in the world is a child, it is possible to state that there is a high level of exposure of this demographic on the online market, to its exploitation

This study evaluates the zero-shot classification performance of eight commercial large language models (LLMs), GPT-4o, GPT-4o Mini, GPT-3.5 Turbo, Claude 3.5 Haiku, Gemini 2.0 Flash, DeepSeek Chat, DeepSeek Reasoner, and Grok, using the CoDA dataset (n = 10,000 Dark Web documents). Results show strong macro-F1 scores across models, led by DeepSeek Chat (0.870), Grok (0.868), and Gemini 2.0 Flash (0.861). Alignment with human annotations was high, with Cohen’s Kappa above 0.840 for top models and Krippendorff’s Alpha reaching 0.871. Inter-model consistency was highest between Claude 3.5 Haiku and GPT-4o (κ = 0.911), followed by DeepSeek Chat and Grok (κ = 0.909), and Claude 3.5 Haiku with Gemini 2.0 Flash (κ = 0.907). These findings confirm that state-of-the-art LLMs can reliably classify illicit content under zero-shot conditions, though performance varies by model and category.

Antibiotic purchasing through online pharmacies: A mystery shopper study

Immediate worldwide connections are now possible with the help of social media, which has revolutionized communication. But internet also gives terrorist and extremist groups powerful tools for radicalization, recruiting, finance, planning, and dissemination. While the Internet has made it simpler to identify terrorist activity on social media and through browser history, the popularity of social media and the dark web has also acted as the ideal anonymous medium for long-distance terrorist recruiting and communication. This study focuses on the use of social media a tool for promoting extremist and terror propaganda by terrorist organization. This study uses qualitative approach using interview method technique. Purposive sampling has been used to draw the sample of the experts and security analysts. Finding of the study suggest that social media is being used for promoting terror agenda by various terrorist organizations. There are certain challenges to cope with the situation. However, the effective use of social media and internet-based checks can produce effective outcomes.