Matching Items (45)
Description
The link between childhood neuropsychological deficits and early-onset offending--the assumed precursor to life-course persistent offending--has been well established, yet the underlying mechanisms facilitating this relationship are less understood. Support is growing for the claim that self-control is a key mechanism that links neuropsychological deficits to early-onset offending. Despite this, findings are mixed with regard to the mediating effect of self-control in the relationship between neuropsychological deficits and antisocial behavior. These studies largely support the notion that self-control exerts a mediating effect on neuropsychological deficits when the offending being studied is less serious. Using data from the National Longitudinal Survey of Youth (NLSY), the present study seeks to build upon the existing literature by examining whether self-control mediates the relationship between neuropsychological deficits and two types of early-onset offending--low and high risk--as a means of testing core tenets of Gottfredson and Hirschi's (1990) and Moffitt's (1993) criminological theories. Findings show that while self-control and neuropsychological deficits independently predict general early-onset offending, these effects vary as a consequence of early-onset offender type. The results point to the need for future research to explore the possibility that the early-onset offender group that leads to persistent offending could be more precisely defined. Examining early-onset offending as a single construct limits our ability to make inferences about those offenders that are the most persistent in their offending patterns and, arguably, more likely to continue offending over the life-course.
ContributorsInfante, Arynn (Author) / Burt, Callie H (Thesis advisor) / Decker, Scott (Committee member) / Young, Jacob Tn (Committee member) / Arizona State University (Publisher)
Created2014
Description
Over the past 40 years, the rate at which women are incarcerated has increased dramatically. Of the 111,000-plus female inmates currently in prison, most will be returned to the community and reenter the labor market. Despite its significance in prisoner reentry and in how ex-offenders remain crime-free, previous research finds that employers are unwilling to hire employees with a criminal record. Moreover, Pager (2003) and Pager, Western, and Bonikowski (2009) found that White job applicants with a prison record were more likely to be interviewed or hired than Black or Hispanic applicants without a record. These troubling findings regarding the effect of race/ethnicity, however, are from research that focuses on men's employment. Given the already low job prospects of ex-prisoners makes it more difficult for women with a prison record to find employment, who also face labor market barriers on account of their race/ethnicity and gender. This dissertation research uses two audit methods with an experimental design to examine the independent and interaction effects of race/ethnicity and incarceration on the likelihood women job applicants will advance through the hiring process. Job applications were submitted online and in-person. The effect of race/ethnicity varied by the method used to apply for jobs. When applying for jobs online, Black women had lower odds of employment than White women. Hispanic women, however, had higher odds of employment than White women when food service jobs were applied for in-person. The effect of a prison record was significant in both experiments; the effect was direct online, but conditioned by ethnicity in-person. Hispanic women with a prison record were less likely than White women with a prison record to advance through the hiring process. The results point to the importance of understanding how women are disadvantaged by incarceration and how mass incarceration contributes to racial/ethnic inequality through its effect in the labor market. Several recommendations follow for future research and policies concerning prisoner reentry and the use of criminal record information by employers.
ContributorsOrtiz, Natalie Rose (Author) / Decker, Scott (Thesis advisor) / Spohn, Cassia (Committee member) / Holtfreter, Kristy (Committee member) / Arizona State University (Publisher)
Created2014
Description
Since Gottfredson and Hirschi proposed the general theory of crime, the direct link between self-control and delinquency has gained strong empirical support, and low self-control is now considered as a significant predictor of individual delinquent behaviors. However, the indirect link between self-control and delinquency still remains understudied. This study fills this void by introducing thoughtfully reflective decision making (TRDM), an important factor intimated by rational choice theory, as the mediator of the relationship between low self-control and delinquency. Using self-reported data from the city of Changzhi, China, this study finds that self-control is closely related to TRDM, low self-control is a significant predictor of general and non-violent delinquency, and TRDM does not mediate the effect of low self-control on delinquency. Findings from this study largely support the generalizability of self-control theory under the Chinese cultural environment, and also suggest that it might be fruitful to test other criminological theories in the Chinese context. The study's findings and their implications for theory and research are discussed.
ContributorsZhang, Wenrui (Author) / Wang, Xia (Thesis advisor) / Decker, Scott (Committee member) / Sweeten, Gary (Committee member) / Arizona State University (Publisher)
Created2014
Description
The current study examines the role that context plays in hackers' perceptions of the risks and payoffs characterizing a hacktivist attack. Hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) is examined through a general game theoretic framework as a product of costs and benefits, as well as the contextual cues that may sway hackers' estimations of each. In two pilot studies, a bottom-up approach is utilized to identify the key motives underlying (1) past attacks affiliated with a major hacktivist group, Anonymous, and (2) popular slogans utilized by Anonymous in its communication with members, targets, and broader society. Three themes emerge from these analyses, namely: (1) the prevalence of first-person plural pronouns (i.e., we, our) in Anonymous slogans; (2) the prevalence of language inducing status or power; and (3) the importance of social injustice in triggering Anonymous activity. The present research therefore examines whether these three contextual factors activate participants' (1) sense of deindividuation, or the loss of an individual's personal self in the context of a group or collective; and (2) motive for self-serving power or society-serving social justice. Results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than their interplay with subjective risks. As expected, the use of we language led to a decrease in subjective risks, possibly due to primed effects of deindividuation. In line with game theory, the joint appearance of both power and justice motives resulted in (1) lower subjective risks, (2) higher payoffs, and (3) higher attack likelihood overall. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of deindividuation and power for the broader population of Internet users around the world.
ContributorsBodford, Jessica (Author) / Kwan, Virginia S. Y. (Thesis advisor) / Shakarian, Paulo (Committee member) / Adame, Bradley J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The literature on hopelessness suggests youth living amid impoverished conditions, social disorganization, and limited resources are more likely to experience increased feelings of hopelessness. Similarly, many of the aforementioned aspects are considered, in some capacity, in the research on gangs. Though a considerable amount of gang literature alludes to the fact that loss of hope may be present, it neither directly addresses it nor references it. This study attempts to converge the present literature on hopelessness among minority youth to minority youth in street gangs. This is done using data obtained from an earlier evaluation of the Mesa Gang Intervention Project, using self-report data from 197 youth, asking questions about socio-demographic information, gang activity, education, employment, crime and delinquency, family and individual crisis, and self-reported detention. Findings implicate a connection exists between gang membership and increased levels of hopelessness. Moreover, results suggest education and self-esteem help to reduce loss of hopelessness.
ContributorsRedner-Vera, Erica N (Author) / Katz, Charles M. (Thesis advisor) / Decker, Scott (Committee member) / Roe-Sepowitz, Dominique (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of information source detection problem (or called rumor source detection) is to identify the source of information diffusion in networks based on available observations like the states of the nodes and the timestamps at which nodes adopted the information (or called infected). The solution of the problem can be used to answer a wide range of important questions in epidemiology, computer network security, etc. This dissertation studies the fundamental theory and the design of efficient and robust algorithms for the information source detection problem.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
ContributorsZhu, Kai (Author) / Ying, Lei (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Liu, Huan (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2015
Description
Data breaches have been on a rise and financial sector is among the top targeted. It can take a few months and upto a few years to identify the occurrence of a data breach. A major motivation behind data breaches is financial gain, hence most of the data ends up being on sale on the darkweb websites. It is important to identify sale of such stolen information on a timely and relevant manner. In this research, we present a system for timely identification of sale of stolen data on darkweb websites. We frame identifying sale of stolen data as a multi-label classification problem and leverage several machine learning approaches based on the thread content (textual) and social network analysis of the user communication seen on darkweb websites. The system generates alerts about trends based on popularity amongst the users of such websites. We evaluate our system using the K-fold cross validation as well as manual evaluation of blind (unseen) data. The method of combining social network and textual features outperforms baseline method i.e only using textual features, by 15 to 20 % improved precision. The alerts provide a good insight and we illustrate our findings by cases studies of the results.
ContributorsDharaiya, Krishna Tushar (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Shoshitaishvili, Yan (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this research, I try to solve multi-class multi-label classication problem, where
the goal is to automatically assign one or more labels(tags) to discussion topics seen
in deepweb. I observed natural hierarchy in our dataset, and I used dierent
techniques to ensure hierarchical integrity constraint on the predicted tag list. To
solve `class imbalance' and `scarcity of labeled data' problems, I developed semisupervised
model based on elastic search(ES) document relevance score. I evaluate
our models using standard K-fold cross-validation method. Ensuring hierarchical
integrity constraints improved F1 score by 11.9% over standard supervised learning,
while our ES based semi-supervised learning model out-performed other models in
terms of precision(78.4%) score while maintaining comparable recall(21%) score.
the goal is to automatically assign one or more labels(tags) to discussion topics seen
in deepweb. I observed natural hierarchy in our dataset, and I used dierent
techniques to ensure hierarchical integrity constraint on the predicted tag list. To
solve `class imbalance' and `scarcity of labeled data' problems, I developed semisupervised
model based on elastic search(ES) document relevance score. I evaluate
our models using standard K-fold cross-validation method. Ensuring hierarchical
integrity constraints improved F1 score by 11.9% over standard supervised learning,
while our ES based semi-supervised learning model out-performed other models in
terms of precision(78.4%) score while maintaining comparable recall(21%) score.
ContributorsPatil, Revanth (Author) / Shakarian, Paulo (Thesis advisor) / Doupe, Adam (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
An examination of 12 darkweb sites involved in selling hacking services - often referred to as ”Hacking-as-a-Service” (HaaS) sites is performed. Data is gathered and analyzed for 7 months via weekly site crawling and parsing. In this empirical study, after examining over 200 forum threads, common categories of services available on HaaS sites are identified as well as their associated topics of conversation. Some of the most common hacking service categories in the HaaS market include Social Media, Database, and Phone hacking. These types of services are the most commonly advertised; found on over 50\% of all HaaS sites, while services related to Malware and Ransomware are advertised on less than 30\% of these sites. Additionally, an analysis is performed on prices of these services along with their volume of demand and comparisons made between the prices listed in posts seeking services with those sites selling services. It is observed that individuals looking to hire hackers for these services are offering to pay premium prices, on average, 73\% more than what the individual hackers are requesting on their own sites. Overall, this study provides insights into illicit markets for contact based hacking especially with regards to services such as social media hacking, email breaches, and website defacement.
ContributorsVincent, Brian W (Author) / Shakarian, Paulo (Thesis advisor) / Candan, Selcuk (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2018