Biblio

Active Cyber Defense (ACD) reconfigures cyber systems (networks and hosts) in timely manner in order to automatically respond to cyber incidents and mitigate potential risks or attacks. However, to launch a successful cyber defense, ACD strategies need to be proven effective in neutralizing the threats and enforceable under the current state and capabilities of the network. In this paper, we present a bounded model checking framework based on SMT to verify that the network can support the given ACD strategies accurately and safely without jeopardizing cyber mission invariants. We abstract the ACD strategies as sets of serializable reconfigurations and provide user interfaces to define cyber mission invariants as reachability, security, and QoS properties. We then verify the satisfaction of these invariants under the given strategies. We implemented this system on OpenFlow-based Software Defined Networks and we evaluated the time complexity for verifying ACD strategies on OpenFlow networks of over two thousand nodes and thousands of rules.

Pseudo-random number generators (PRNGs) are a critical infrastructure for cryptography and security of many computer applications. At the same time, PRNGs are surprisingly difficult to design, implement, and debug. This paper presents the first static analysis technique specifically for quality assurance of cryptographic PRNG implementations. The analysis targets a particular kind of implementation defect, the entropy loss. Entropy loss occurs when the entropy contained in the PRNG seed is not utilized to the full extent for generating the pseudo-random output stream. The Debian OpenSSL disaster, probably the most prominent PRNG-related security incident, was one but not the only manifestation of such a defect. Together with the static analysis technique, we present its implementation, a tool named Entroposcope. The tool offers a high degree of automation and practicality. We have applied the tool to five real-world PRNGs of different designs and show that it effectively detects both known and previously unknown instances of entropy loss.