DLLMiner: structural mining for malware detection

Existing anti-malware products usually use signature-based techniques as their main detection engine. Although these methods are very fast, they are unable to provide effective protection against newly discovered malware or mutated variant of old malware. Heuristic approaches are the next generation of detection techniques to mitigate the problem. These approaches aim to improve the detection rate by extracting more behavioral characteristics of malware. Although these approaches cover the disadvantages of signature-based techniques, they usually have a high false positive, and evasion is still possible from these approaches. In this paper, we propose an effective and efficient heuristic technique based on static analysis that not only detect malware with a very high accuracy, but also is robust against common evasion techniques such as junk injection and packing. Our proposed system is able to extract behavioral features from a unique structure in portable executable, which is called dynamic-link library dependency tree, without actually executing the application

The Potential Global Climate Suitability of Kiwifruit Bacterial Canker Disease (<i>Pseudomonas syringae</i> pv. <i>actinidiae</i> (Psa)) Using Three Modelling Approaches: CLIMEX, Maxent and Multimodel Framework

In recent years, outbreaks of kiwifruit bacterial canker (Pseudomonas syringae pv. actinidiae, Psa) have caused huge economic losses to two major global kiwifruit producers, Italy and New Zealand. To evaluate the potential global risk areas of Psa, three modelling methods (MaxEnt, CLIMEX and a Multi-Model Framework, including Support Vector Machine or SVM) were used. Current global occurrence data for Psa were collected from different sources. The long-term climate data were sourced from WorldClim and CliMond websites. The model results were combined into a consensus model to identify the hotspots. The consensus model highlighted the areas where two or three models agreed on climate suitability for Psa. All three models agreed with respect to the climate suitability of areas where Psa is currently present and identified novel areas where Psa has not established yet. The SVM model predicted large areas in Central Asia, Australia, and Europe as more highly suitable compared to MaxEnt and CLIMEX. Annual mean temperature and annual precipitation contributed most to the MaxEnt prediction. Both MaxEnt and CLIMEX showed the probability of Psa establishment increased above 5 °C and decreased above 20 °C. The annual precipitation response curve showed that excessive rain (>1200 mm/y) constrains Psa establishment. Our modelling results will provide useful information for Psa management by highlighting the climatically susceptible areas where Psa has not established, such as the USA, Iran, Denmark, Belgium and especially South Africa, where kiwifruit has been planted commercially in recent years

New Zealand pest management: current and future challenges

New Zealand is under increasing pressure from terrestrial and aquatic pests, weeds and diseases that threaten the country's ecosystems and economy. Ongoing improvement in existing pest management methodologies and novel approaches are required in response to public concerns about animal welfare, increasingly stringent trade requirements, abolition of groups of pesticides and resistance to existing pesticides as well as, possibly, biological control agents. Surveillance and pest monitoring are needed to increase the chances of early interception of invasive species or to confirm their eradication. Core capabilities in taxonomy, genomics, phenology, ecology, pest impacts, development of novel control tools and social science are required and must be maintained nationally. Given New Zealand's unique environment, the ecology of invasive pests cannot be presumed to be the same as that in their native ranges, yet currently many pests in New Zealand are managed with poor understanding of their bionomics and impacts. Failure to address these areas will have serious adverse impacts on New Zealand