Theses and Dissertations from UMD
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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.Item Advanced Honeypot Architecture for Network Threats Quantification(2009) Berthier, Robin G; Cukier, Michel; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Today's world is increasingly relying on computer networks. The increase in the use of network resources is followed by a rising volume of security problems. New threats and vulnerabilities are discovered everyday and affect users and companies at critical levels, from privacy issues to financial losses. Monitoring network activity is a mandatory step for researchers and security analysts to understand these threats and to build better protections. Honeypots were introduced to monitor unused IP spaces to learn about attackers. The advantage of honeypots over other monitoring solutions is to collect only suspicious activity. However, current honeypots are expensive to deploy and complex to administrate especially in the context of large organization networks. This study addresses the challenge of improving the scalability and flexibility of honeypots by introducing a novel hybrid honeypot architecture. This architecture is based on a Decision Engine and a Redirection Engine that automatically filter attacks and save resources by reducing the size of the attack data collection and allow researchers to actively specify the type of attack they want to collect. For a better integration into the organization network, this architecture was combined with network flows collected at the border of the production network. By offering an exhaustive view of all communications between internal and external hosts of the organization, network flows can 1) assist the configuration of honeypots, and 2) extend the scope of honeypot data analysis by providing a comprehensive profile of network activity to track attackers in the organization network. These capabilities were made possible through the development of a passive scanner and server discovery algorithm working on top of network flows. This algorithm and the hybrid honeypot architecture were deployed and evaluated at the University of Maryland, which represents a network of 40,000 computers. This study marks a major step toward leveraging honeypots into a powerful security solution. The contributions of this study will enable security analysts and network operators to make a precise assessment of the malicious activity targeting their network.