Reduced growth factor requirements and accelerated cell-cycle kinetics in adult human melanocytes transformed with SV40 large T antigen

Melanomas develop with high frequency in transgenic mice in which oncogenic sequences of the SV40 DNA tumor virus have been specifically targeted to melanocytes. To investigate the role of SV40 in melanomagenesis, cultured human melanocytes were transformed with a retroviral shuttle vector encoding the SV40 large T antigen and examined for changes in cell-cycle kinetics and growth-factor dependence. Colonies expressing the viral oncogene were morphologically indistinguishable from their non-T-antigen-transformed counterparts. Also like normal melanocytes, the infected cells remained anchorage dependent and non-tumorigenic in nude mice. However, T-antigen-positive cultures exhibited significantly accelerated population doubling times, increased saturation densities with highly confluent monolayers and a three- to fourfold extended life span. Most interestingly, cell-cycle analysis revealed a measurable shift from quiescent to cycling cells in T-antigen-expressing cultures and an acquired ability to progress more rapidly through G1. Moreover, T-antigen-positive melanocytes proliferated in the absence of PMA and required markedly reduced levels of exogenous bFGF. These studies indicate that the viral oncogen of simian virus 40 provides melanocytes with distinct growth advantages that may render these cells unusually susceptible to additional environmental challenges necessary for full expression of the malignant phenotype.     

Detecting zero-day attacks using context-aware anomaly detection at the application-layer

Anomaly detection allows for the identification of unknown and novel attacks in network traffic. However, current approaches for anomaly detection of network packet payloads are limited to the analysis of plain byte sequences. Experiments have shown that application-layer attacks become difficult to detect in the presence of attack obfuscation using payload customization. The ability to incorporate syntactic context into anomaly detection provides valuable information and increases detection accuracy. In this contribution, we address the issue of incorporating protocol context into payload-based anomaly detection. We present a new data representation, called \({c}_n\)-grams, that allows to integrate syntactic and sequential features of payloads in an unified feature space and provides the basis for context-aware detection of network intrusions. We conduct experiments on both text-based and binary application-layer protocols which demonstrate superior accuracy on the detection of various types of attacks over regular anomaly detection methods. Furthermore, we show how \({c}_n\)-grams can be used to interpret detected anomalies and thus, provide explainable decisions in practice.