Honeypots for Cybercrime Research

Perkins, Robert C.; Howell, C. Jordan

doi:10.1007/978-3-030-74837-1_12

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…Although limitations exist, and future research is warranted, one thing is evident: human-based interventions must not be dismissed when developing comprehensive cybersecurity prevention strategies (Howell et al, 2019; Perkins & Howell, 2021). Technological innovation and target hardening will always have a place in cybercrime prevention, but to overlook what does and does not work in a sociological and psychological context regarding those engaged in cybercrime perpetration is a drastic mistake.…”

Section: Discussionmentioning

confidence: 99%

Risk Avoidance Behavior on Darknet Marketplaces

Howell

Maimon

Perkins

et al. 2022

Crime & Delinquency

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The current study employs a quasi-experimental design to test the efficacy of situational crime prevention (SCP) in understanding risk avoidance behavior in a darknet environment. Specifically, we deployed a web scraper to extract data from a popular darknet market. We then used these data to assess change in vendors and customers’ behavior following the deliverance of a warning message from the market administrator regarding the former market administrator’s intention to scam market participants. In the one-month following the message, vendors posted fewer advertisements and customers spent less and made fewer transactions on the marketplace. These findings expand the scope of SCP and provide evidence for Hutchings and Holt’s notion that darknet markets can be disrupted by means of gossip.

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Section: Discussionmentioning

confidence: 99%

Risk Avoidance Behavior on Darknet Marketplaces

Howell

Maimon

Perkins

et al. 2022

Crime & Delinquency

Self Cite

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“…Criminologists often use honeypots to better understand cybercrime (Perkins and Howell, 2021). Honeypots are computer tools designed to attract Internet users to interact with them and collect the data this interaction generates (Spitzner, 2002).…”

Section: Online Ads As Honeypotsmentioning

confidence: 99%

“…When designing and using honeypots, it is important to consider the activity of bots online. Without proper filters, bots can alter the data collected and thus affect analysis (Perkins and Howell, 2021; see also Vetterl, 2020). For example, a repeated count of intrusion attempts may be due to bot activity and not necessarily to humans.…”

Section: Online Ads As Honeypotsmentioning

confidence: 99%

Fieldwork Experiences Researching Cybercriminals

Moneva¹,

Leukfeldt²,

Romagna³

2023

Preprint

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Cybercriminals are an elusive population to study. This makes social research with cybercriminals as valuable as it is scarce. To stimulate research on cybercriminals, it is important that researchers share their insights on successful and unsuccessful approaches, strategies, and techniques. This chapter collects our fieldwork experiences researching cybercriminals, potential cybercriminals, hackers, and hacktivists. After presenting the phases of our fieldwork, we outline six research techniques we have applied and discuss the ethical issues involved. We conclude with some lessons learned and methodological perspectives to guide future research.

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“…1) The sensors subsystem: Sensors are servers that are intentionally exposed to the public network, pretending to offer something interesting for the attacker. A lot of effort has been made to create such technology leading to what is known as a honeypot [23]. Which is a data framework asset whose esteem lies in unauthorized or unlawful utilization of that asset, which means that honeypots derive their values from the threats using them [24].…”

Section: A Dataset Collection Setupmentioning

confidence: 99%

Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset

Mahfouz¹,

Abuhussein²,

Alsubaei³

et al. 2022

IJACSA

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Network intrusion detection is a key step in securing today's constantly developing networks. Various experiments have been put forward to propose new methods for resisting harmful cyber behaviors. Though, as cyber-attacks turn out to be more complex, the present methodologies fail to adequately solve the problem. Thus, network intrusion detection is now a significant decision-making challenge that requires an effective and intelligent approach. Various machine learning algorithms such as decision trees, neural networks, K nearest neighbor, logistic regression, support vector machine, and Naive Bayes have been utilized to detect anomalies in network traffic. However, such algorithms require adequate datasets to train and evaluate anomaly-based network intrusion detection systems. This paper presents a testbed that could be a model for building real-world datasets, as well as a newly generated dataset, derived from real network traffic, for intrusion detection. To utilize this real dataset, the paper also presents an ensemble intrusion detection model using a meta-classification approach enabled by stacked generalization to address the issue of detection accuracy and false alarm rate in intrusion detection systems.

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Honeypots for Cybercrime Research

Cited by 9 publications

References 46 publications

Risk Avoidance Behavior on Darknet Marketplaces

Risk Avoidance Behavior on Darknet Marketplaces

Fieldwork Experiences Researching Cybercriminals

Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset

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