Ciprian Oprisa

Dan Domnita, Ciprian Oprisa

The problem of fast items retrieval from a fixed collection is often encountered in most computer science areas, from operating system components to databases and user interfaces. We present an approach based on hash tables that focuses on both minimizing the number of comparisons performed during the search and minimizing the total collection size. The standard open-addressing double-hashing approach is improved with a non-linear transformation that can be parametrized in order to ensure a uniform distribution of the data in the hash table. The optimal parameter is determined using a genetic algorithm. The paper results show that near-perfect hashing is faster than binary search, yet uses less memory than perfect hashing, being a good choice for memory-constrained applications where search time is also critical.

Francisc Moldovan, Ciprian Oprisa

The ability to analyze network threats is very important in security research. Traditional approaches, involving sandboxing technology are limited to simulating a single host, missing local network attacks. This issue is addressed by designing a threat analysis framework that uses software-defined networking for simulating arbitrary networks. The presented system offers flexibility, allowing a security researcher to define a virtual network that is able to capture malicious actions and to be restored to the initial state afterwards. Both the framework design and common usage scenarios are described. By providing this framework, we aim to ease the analysis effort in combating cyberthreats.