PARAMETERIZATION OF AN ENERGY MODEL FOR SCORING OF ANTI-HIV DRUGS AND A COMPUTATIONAL METHOD OF LEAD COMPOUND OPTIMIZATION FOR DRUG DISCOVERY

This project aims to parameterize an energy model with the goal of developing a fast method for predicting binding affinities of HIVP inhibitors. This method will be used for in silico compound screening to discover new potential anti-HIV drug candidates. The project also aims to develope a method of optimizing the charges of local parts of a ligand while keeping the rest of the charges roughly constant, rather than attempting to modify all of the ligand's charges towards an optimum, as done in previous approaches. The method developed here will also be computationally faster than existing approaches

A baseline for unsupervised advanced persistent threat detection in system-level provenance

Advanced persistent threats (APT) are stealthy, sophisticated, and unpredictable cyberattacks that can steal intellectual property, damage critical infrastructure, or cause millions of dollars in damage. Detecting APTs by monitoring system-level activity is difficult because manually inspecting the high volume of normal system activity is overwhelming for security analysts. We evaluate the effectiveness of unsupervised batch and streaming anomaly detection algorithms over multiple gigabytes of provenance traces recorded on four different operating systems to determine whether they can detect realistic APT-like attacks reliably and efficiently. This report is the first detailed study of the effectiveness of generic unsupervised anomaly detection techniques in this setting