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Honeypots are a deceptive technology used to capture malicious activity. The technology is useful for studying attacker behavior, tools, and techniques but can be difficult to implement and maintain. Historically, a lack of measures of effectiveness prevented researchers from assessing honeypot implementations. The consequence being ineffective implementations leading to poor performance, flawed imitation of legitimate services, and premature discovery by attackers. Previously, we developed a taxonomy for measures of effectiveness in dynamic honeypot implementations. The measures quantify a dynamic honeypots effectiveness in fingerprinting its environment, capturing valid data from adversaries, deceiving adversaries, and intelligently monitoring itself and its surroundings. As a step towards developing automated effectiveness testing, this work introduces a tool for priming a target honeypot for evaluation. We outline the design of the tool and provide results in the form of quantitative calibration data.
Mobile nodes, in particular smartphones are one of the most relevant devices in the current Internet in terms of quantity and economic impact. There is the common believe that those devices are of special interest for attackers due to their limited r
This paper presents an experimental study and the lessons learned from the observation of the attackers when logged on a compromised machine. The results are based on a six months period during which a controlled experiment has been run with a high i
A honeypot is a type of security facility deliberately created to be probed, attacked and compromised. It is often used for protecting production systems by detecting and deflecting unauthorized accesses. It is also useful for investigating the behav
Node.js is one of the most popular frameworks for building web applications. As software systems mature, the cost of running their entire regression test suite can become significant. Selective Regression Testing (SRT) is a technique that executes on
Android is present in more than 85% of mobile devices, making it a prime target for malware. Malicious code is becoming increasingly sophisticated and relies on logic bombs to hide itself from dynamic analysis. In this paper, we perform a large scale