Mechanical Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/2263
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Item QUANTIFYING AND PREDICTING USER REPUTATION IN A NETWORK SECURITY CONTEXT(2019) Gratian, Margaret Stephanie; Cukier, Michel; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Reputation has long been an important factor for establishing trust and evaluating the character of others. Though subjective by definition, it recently emerged in the field of cybersecurity as a metric to quantify and predict the nature of domain names, IP addresses, files, and more. Implicit in the use of reputation to enhance cybersecurity is the assumption that past behaviors and opinions of others provides insight into the expected future behavior of an entity, which can be used to proactively identify potential threats to cybersecurity. Despite the plethora of work in industry and academia on reputation in cyberspace, proposed methods are often presented as black boxes and lack scientific rigor, reproducibility, and validation. Moreover, despite widespread recognition that cybersecurity solutions must consider the human user, there is limited work focusing on user reputation in a security context. This dissertation presents a mathematical interpretation of user cyber reputation and a methodology for evaluating reputation in a network security context. A user’s cyber reputation is defined as the most likely probability the user demonstrates a specific characteristic on the network, based on evidence. The methodology for evaluating user reputation is presented in three phases: characteristic definition and evidence collection; reputation quantification and prediction; and reputation model validation and refinement. The methodology is illustrated through a case study on a large university network, where network traffic data is used as evidence to determine the likelihood a user becomes infected or remains uninfected on the network. A separate case study explores social media as an alternate source of data for evaluating user reputation. User-reported account compromise data is collected from Twitter and used to predict if a user will self-report compromise. This case study uncovers user cybersecurity experiences and victimization trends and emphasizes the feasibility of using social media to enhance understandings of users from a security perspective. Overall, this dissertation presents an exploration into the complicated space of cyber identity. As new threats to security, user privacy, and information integrity continue to manifest, the need for reputation systems and techniques to evaluate and validate online identities will continue to grow.Item EMPIRICAL STUDIES BASED ON HONEYPOTS FOR CHARACTERIZING ATTACKERS BEHAVIOR(2015) Sobesto, Bertrand; Cukier, Michel; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The cybersecurity community has made substantial efforts to understand and mitigate security flaws in information systems. Oftentimes when a compromise is discovered, it is difficult to identify the actions performed by an attacker. In this study, we explore the compromise phase, i.e., when an attacker exploits the host he/she gained access to using a vulnerability exposed by an information system. More specifically, we look at the main actions performed during the compromise and the factors deterring the attackers from exploiting the compromised systems. Because of the lack of security datasets on compromised systems, we need to deploy systems to more adequately study attackers and the different techniques they employ to compromise computer. Security researchers employ target computers, called honeypots, that are not used by normal or authorized users. In this study we first describe the distributed honeypot network architecture deployed at the University of Maryland and the different honeypot-based experiments enabling the data collection required to conduct the studies on attackers' behavior. In a first experiment we explore the attackers' skill levels and the purpose of the malicious software installed on the honeypots. We determined the relative skill levels of the attackers and classified the different software installed. We then focused on the crimes committed by the attackers, i.e., the attacks launched from the honeypots by the attackers. We defined the different computer crimes observed (e.g., brute-force attacks and denial of service attacks) and their characteristics (whether they were coordinated and/or destructive). We looked at the impact of computer resources restrictions on the crimes and then, at the deterrent effect of warning and surveillance. Lastly, we used different metrics related to the attack sessions to investigate the impact of surveillance on the attackers based on their country of origin. During attacks, we found that attackers mainly installed IRC-based bot tools and sometimes shared their honeypot access. From the analysis on crimes, it appears that deterrence does not work; we showed attackers seem to favor certain computer resources. Lastly, we observed that the presence of surveillance had no significant impact on the attack sessions, however surveillance altered the behavior originating from a few countries.