Complete a Low self-control and hacking Template Based On Case Study

Introduction 

Occupational crime involves the commission of crime through opportunities created via legitimate employment (Green, 1990) and low self-control has been used to explain occupational crime with some success (Gibson & Wright, 2001; Langton et al., 2006). White hat hackers, employed to undertake hacking activities legitimately for the purpose of testing online security, use techniques, learn capabilities, and identify potential targets daily which are by their very nature applicable to illegal activity (Arlander, 2020). A number of criminological studies have begun to examine the relevance of low self-control theory in explaining cybercrime (Holt et al., 2011). Low self-control has been linked to various forms of cybercriminal activity including software piracy where 70% of 152 offenders were noted by Californian police to have low self-control (Moon et al., 2010), music piracy where 73% of 567 offenders reported a low level of self-control on the Pearson scale of self-control in an online survey conducted in Boston (Higgins et al., 2007), and movie piracy where 82% of 54 offenders in an interview in Michigan identified as having low levels of self-control (Higgins et al., 2008). However, empirical research conducted on low self-control and computer hacking has produced mixed results which do not fully support the link between self-control and cybercrime (Bossler & Burruss, 2010; Hans, 2020).

Given some empirical support for the link between low self-control and cybercriminality (Bossler & Burruss, 2010; Hans, 2020; Higgins et al., 2007, 2008; Holt et al., 2011; Moon et al., 2010), and the opportunity created for occupational crime through professional work as a white hat hacker (Arlander, 2020), this study aims to address the lack of research examining the relationship between low self-control and illegal hacking amongst professional white hat hackers in Australia. The research question is: What is the relationship between levels of self- control and types of illegal hacking amongst white hat hackers in Sydney? This relationship between variables will be tested by employing a self-report survey. Knowing whether low self-control is associated with hacking could lead to workplace policies around Internet usage and the illegal nature of white hat hacking (Peterson, 2020). 

Sampling strategy 

The current study will be using survey data gathered from white hat hackers that are attending the 2020 DEFCON hacking convention held in Sydney. DEFCON is an annual hacking convention that attracts the largest group of white hat hackers around the world with approximately 30,000 attendees who are hackers, with a smaller sub-population identifying as white hat hackers (Defcon, 2020). The researchers are aiming to gather data from at least 300 participants. Links to an online survey will be distributed on location across a four day period from a booth located at the convention, enabling direct access to the population of interest (hackers) and also a potentially large sample size (convention attendees who identify as white hackers) (Altmann, 1974). The drawback of using this site is that only very sophisticated hackers may be in attendance meaning there will be less representation of unsophisticated hackers, and a number of participants may be from outside the Sydney area targeted for this research project (Jones, 2020).

The researchers will be employing a convenience sampling method to draw the sample, given the convention has been chosen as a site where many hackers will be in attendance and can be conveniently recruited into the study (Miller, 2021). Convenience sampling is associated with a high vulnerability to selection bias as participants are not selected randomly rather chosen specifically for their characteristics, and such an approach often results in low credibility as it is not clear whether the same sample would have been achieved if the general community were surveyed (Bachmann & Schutt, 2019). However, sampling the sub- population of white hat hackers who attend the convention is an efficient and quick way to recruit participants for the study, and this will enable a fast turnaround of research results because the surveys will be completed in a short space of time due to the use of convenience sampling (Tacker, 2020).

The unit of analysis is the individual hacker, because individuals are completing the survey and responding about their self-control and types of hacking, and analysis of the data will focus on comparing individuals with other individuals rather than creating groups (Tate, 2020).

The concepts to be measured are self-control and white hat hacking. The independent variable is level of self-control and the dependent variable is types of white hat hacking. The level of self-control will be measured by utilising the Grasmick et al. (1993) scale, which aims to capture the latent traits of self-control through the use of an attitudinal measure comprised of 24 items. Respondents will use a five-point Likert scale to indicate their level of agreement with each question, ranging from strongly disagree to strongly agree (Donner et al. 2014). Examples of question include: “Can you delay your receipt of a reward by 30 minutes? Do you often find yourself exploding with anger? Do you find yourself easily getting frustrated with others?” (Donner et al., 2014, p.9). Scores below 60 on indicate low levels of self-control and scored 60 and above indicate high levels of self-control.In order to measure the dependent variable, the types of illegal hacking participants engaged in, the researchers will be measuring engagement or non-engagement in nine different forms of illegal hacking, based on prior research on online deviance (Bossler & Holt, 2010; Skinner & Fream, 1997; Donner et.al. 2014) as well as on the different types of cybercriminal activities outlined by Bhardwaj and Singh (2011). Respondents will be asked to whether in the past twelve months they have used their own or someone else’s computer to: (1) illegally gain access to someone else’s computer; (2) illegally gain access to someone else’s social media account; (3) illegally gain access to someone else’s bank account details; (4) attempted to hack into or hacked into a protected server; (5) perpetrated a denial of service attack; (6) distributed malware; (7) used cheats in an online video game; (8) engaged in a MitM attack; and (9) distributed spyware (Donner et al., 2014). The response options provided for each question is yes/no.

 Conclusion 

The current study aims to explore whether there is an observable association between level of self-control in white hat hackers and the types of hacking they engage in. This research fills a gap in previous research examining this specific type of crime and self-control. In order to combat cybercrime, companies that retain valuable information such as banks and government agencies employ white hat hackers to maintain network security (Caldwell, 2011). Therefore, it becomes imperative that we examine the mechanisms by which those responsible for our online security might choose to use their knowledge of cybersecurity systems to engage in cybercriminal activities. This will increase in importance as technology and humanity’s reliance on it continues to evolve, making education around cybercrime for employees increasingly important, in addition to increasing law enforcement awareness of these types of crime (Yar & Steinmetz, 2019).