Malware is a major threat to modern computer systems. Malicious behaviors are hidden by a variety of techniques: code obfuscation, message encoding and encryption, etc. Countermeasures have been developed to thwart these techniques in order to expose malicious behaviors. However, these countermeasures rely heavily on identifying specific API calls, which has significant limitations as these calls can be misleading or hidden from the analyst. In this paper, we show that malicious programs share a key component which we call a behavior dispatcher, a code structure which is intercepted between various condition checks and malicious actions. By identifying these behavior dispatchers, a malware analysis can be guided into behavior dispatchers and activate hidden malicious actions more easily. We propose BDHunter, a system that automatically identifies behavior dispatchers to assist triggering malicious behaviors. BDHunter takes advantage of the observation that a dispatcher compares an input with a set of expected values to determine which malicious behaviors to execute next. We evaluate BDHunter on recent malware samples to identify behavior dispatchers and show that these dispatchers can help trigger more malicious behaviors (otherwise hidden). Our experimental results show that BDHunter identifies 77.4% of dispatchers within the top 20 candidates discovered. Furthermore, BDHunter-guided concolic execution successfully triggers 13.0x and 2.6x more malicious behaviors, compared to unguided symbolic and concolic execution, respectively. These demonstrate that BDHunter effectively identifies behavior dispatchers, which are useful for exposing malicious behaviors.

As modern attacks become more stealthy and persistent, detecting or preventing them at their early stages becomes virtually impossible. Instead, an attack investigation or provenance system aims to continuously monitor and log interesting system events with minimal overhead. Later, if the system observes any anomalous behavior, it analyzes the log to identify who initiated the attack and which resources were affected by the attack and then assess and recover from any damage incurred. However, because of a fundamental tradeoff between log granularity and system performance, existing systems typically record system-call events without detailed program-level activities (e.g., memory operation) required for accurately reconstructing attack causality or demand that every monitored program be instrumented to provide program-level information.

Network vulnerability is a critical component of network security. Yet vulnerability analysis has received relatively little attention from the security visualization community. This paper describes nv, a web-based Nessus vulnerability visualization. Nv utilizes treemaps and linked histograms to allow security analysts and systems administrators to discover, analyze, and manage vulnerabilities on their networks. In addition to visualizing single Nessus scans, nv supports the analysis of sequential scans by showing which vulnerabilities have been fixed, remain open, or are newly discovered. Nv operates completely in-browser, to avoid sending sensitive data to outside servers. We discuss the design of nv, as well as provide case studies demonstrating vulnerability analysis workflows which include a multiple-node testbed and data from the 2011 VAST Challenge.