A Layered Architecture for Detecting Malicious Behaviors

We address the semantic gap problem in behavioral monitoring by using hierarchical behavior graphs to infer high-level behaviors from myriad low-level events that could be parts of many different kinds of behavior. Our experimental system traces the execution of a process, performing data-flow analysis to identify meaningful actions such as \u201cproxying\u201d, \u201ckeystroke logging\u201d, \u201cdata leaking\u201d, and \u201cdownloading and executing a program\u201d from complex combinations of rudimentary system calls. To preemptively address evasive malware behavior, our specifications are carefully crafted to detect alternate sequences of events that achieve the same high-level goal. We tested seven malicious bots and eleven benign programs and found that we were able to thoroughly identify high-level behaviors across this diverse code base. Moreover, we were able to distinguish malicious execution of high-level behaviors from benign by distinguishing remotely-initiated from locally-initiated actions

Comparative multi-goal tradeoffs in systems engineering of microbial metabolism

<p>Abstract</p> <p>Background</p> <p>Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs.</p> <p>Results</p> <p>Here, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (<it>Escherichia coli</it>, <it>Saccharomyces cerevisiae</it>, and <it>Shewanella oneidensis</it>) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity.</p> <p>Conclusions</p> <p>The resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.</p