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Regardless of industry or job type, most organizations aim to recruit large qualified applicant pools via job advertisements or postings. With little control over those individuals that choose to apply and those that do not, organizations and their recruiters are likely to do what they can to increase their applicant pool. This allows for more options in potential hires during the selection process. In order to control the applicant pool as much as possible, recruiters can try and influence potential applicants through the posted job advertisement. Therefore, it is reasonable to assume that many recruiters will write a slightly inflated or overly positive view of the job in order to appeal to more applicants. However, individuals job searching may perceive this attempt as misleading or deceptive. In order to understand perceived deception in job advertisements and what features of their text elicits an overall negative attitude towards the advertisement, this study proposes a mainly exploratory approach to discover if there is a homogenous higher-level construct of perceived deceptiveness or if there is a more person-centered approach via latent profile analysis (LPA) to explain what applicants perceived as deceptive. After the nature of perceived deceptiveness is better understood, this study aims to utilize natural language processing (NLP) topic modeling to find common deceptive topics within different dimensions of the job posting such as, pay, benefits, qualifications, etc. With the limited empirical guidance provided to practitioners, the proposed study can help facilitate research on best practices in job advertisement writing to gain qualified and quality candidates. In turn, those candidates will tend to maintain positive attitudes towards the job and organization, which can persist even after being hired. / Doctor of Philosophy / In today's job market, organizations aim to attract qualified applicants through appealing job advertisements. However, some applicants may perceive these attempts as misleading or deceptive. This study explores whether there is a common view of what is deceptive within the text of a job advertisement or if it varies based on individualized perceptions. This study aims to classify different types of applicants and their associated perception of deception in job ads. This study also employs natural language processing techniques to analyze the language used in job advertisements, pinpointing common deceptive themes in various sections of the job posting, such as pay, benefits, and qualifications. By uncovering how people perceive deception in job ads, this study hopes to provide valuable insights to organizations for crafting more honest and transparent job postings. This can attract high-quality candidates who maintain positive attitudes towards the job and organization, ultimately contributing to improved hiring practices and fostering a more positive work environment.

URL parser and normalization processes are common and important operations in different web frameworks and technologies. In recent years, security researchers have targeted these processes and discovered high impact vulnerabilities and exploitation techniques. In a different approach, we will focus on semantic disconnect among different framework-independent web technologies (e.g., browsers, proxies, cache servers, web servers) which results in different URL interpretations. We coined the term “Path Confusion” to represent this disagreement and this thesis will focus on analyzing enabling factors and security impact of this problem.In this thesis, we will show the impact and importance of path confusion in two attack classes including Style Injection by Relative Path Overwrite (RPO) and Web Cache Deception (WCD). We will focus on these attacks as case studies to demonstrate how utilizing path confusion techniques makes targeted sites exploitable. Moreover, we propose novel variations of each attack which would expand the number of vulnerable sites and introduce new attack scenarios. We will present instances which have been secured against these attacks, while being still exploitable with introduced Path Confusion techniques. To further elucidate the seriousness of path confusion, we will also present the large scale analysis results of RPO and WCD attacks on high profile sites. We present repeatable methodologies and automated path confusion crawlers which detect thousands of sites that are still vulnerable to RPO or WCD only with specific types of path confusion techniques. Our results attest the severity of path confusion based class of attacks and how extensively they could hit the clients or systems. We analyze some browser-based mitigation techniques for RPO and discuss that WCD cannot be dealt as a common vulnerability of each component; instead it arises when an ecosystem of individually impeccable components ends up in a faulty situation.