Malware is any malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Thus, malware detection is a critical topic in computer security. Currently, signature-based detection is the most extended method for detecting malware. Although this method is still used on most popular commercial computer antivirus software, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malware. Applying a methodology proven successful in similar problem-domains, we propose the use of ngrams (every substring of a larger string, of a fixed lenght n) as file signatures in order to detect unknown malware whilst keeping low false positive ratio. We show that n-grams signatures provide an effective way to detect unknown malware.
We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids.
Abstract-The arrival of the smart grid paradigm has brought a number of novel initiatives that aim at increasing the level of energy efficiency of buildings such as smart metering or demand side management. Still, all of them demand an accurate load estimation. Short-term load forecasting in buildings presents additional requirements, among others the need of prediction models with simple or non-existing parametrisation processes. We extend a previous work that evaluated a number of algorithms to this end. Herewith we present several improvements including a variable data learning window and diverse learning data weighting combinations that further up improve our results. Finally, we have tested all the algorithms and modalities with four different datasets to show how the results hold up.
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