Clustering versus SVM for malware detection

Previous work has shown that cluster analysis can be used to effectively classify malware into meaningful families. In this research, we apply cluster analysis to the challenging problem of classifying previously unknown malware. We perform several experiments involving malware clustering. We compare our clustering results to those obtained when a support vector machine (SVM) is trained on the malware family. Using clustering, we are able to classify malware with an accuracy comparable to that of an SVM. An advantage of the clustering approach is that a new malware family can be classified before a model has been trained specifically for the family.     

A set of features to detect web security threats

The increasing growth of malicious websites and systems for distributing malware through websites is making it urgent the adoption of effective techniques for timely detection of web security threats. Current mechanisms may exhibit some limitations, mainly concerning the amount of resources required, and a low true positives rate for zero-day attacks. With this paper, we propose and validate a set of features extracted from the content and the structure of webpages, which could be used as indicators of web security threats. The features are used for building a predictor, based on five machine learning algorithms, which is applied to classify unknown web applications. The experimentation demonstrated that the proposed set of features is able to correctly classify malicious web sites with a high level of precision, corresponding to 0.84 in the best case, and recall corresponding to 0.89 in the best case. The classifiers reveal to be successful also with zero day attacks.     

Reactive blending of commercial PET and PC with freshly added catalysts

Reactive blending of commercial poly(ethylene terephthalate) (PET) and bisphenol A polycarbonate (PC), with catalysts added in the form of powder dispersed on the polymers just before melt mixing, was performed in a Brabender Plasticord 2000 apparatus at 275°C. Catalytic activity of the catalysts freshly added to polymers was found to be much higher as compared with that of the residues of the same type of catalysts remaining in PET after its synthesis. Furthermore, the catalytic activity appeared to be strongly dependent on the structure of the ligand that influences the catalyst solubility in the polymer melt. N.m.r. spectroscopy, selective degradation of PC fragments, solubility tests in methylene chloride and d.s.c. measurements made it possible to range the catalysts studied according to their catalytic activity.     

Room-temperature efficient light detection by amorphous Ge quantum wells

In this work, ultrathin amorphous Ge films (2 to 30 nm in thickness) embedded in SiO₂ layers were grown by magnetron sputtering and employed as proficient light sensitizer in photodetector devices. A noteworthy modification of the visible photon absorption is evidenced due to quantum confinement effects which cause both a blueshift (from 0.8 to 1.8 eV) in the bandgap and an enhancement (up to three times) in the optical oscillator strength of confined carriers. The reported quantum confinement effects have been exploited to enhance light detection by Ge quantum wells, as demonstrated by photodetectors with an internal quantum efficiency of 70%.     

Expression of α-amylase inhibitors in diploid Triticum species

The aim of the work was to characterize the expression of various α-amylase inhibitors (αAIs), well known anti-nutritional compounds, for the development of healthier diploid wheat-based functional foods.     

Durability of SiO2–TiO2 Photocatalytic Coatings on Ceramic Tiles

The aim of this investigation was the evaluation of the durability of photocatalytic sol–gel coating deposited on industrial ceramic tiles. In particular, the effect of substrate roughness on photocatalytic performance before and after different aging tests was determined. The results showed that the photodegradation process was clearly affected by the surface roughness of the substrate. In particular, the smoother surface had a higher photocatalytic activity, faster hydrophilicity but a lower durability to abrasion with respect to the matt surface.     

Spur Failure in River Engineering

Spurs are river engineering elements used to protect river banks from erosion and to concentrate flow to the river axis. Today, spurs are also employed for promoting environmental conditions along a river bank. These elements are characterized by a large variety of geometrical parameters, of which none is definitely fixed. Based on a preliminary study that identified optimum spur arrangements in a straight river reach the present research project adds to the protection of spurs by riprap. The first two spurs are demonstrated to require a suitable riprap for promoting nearly uniform scour conditions along the entire spur reach. This research investigated the effects of a variety of parameters on spur flow, notably spur length, spur spacing, spur height plus the diameter and the number of riprap rows, along with the main hydraulic and granulometric parameters. Design equations were established based on a large experimental campaign to predict riprap failure in terms of the previous set of variables. The failure modes are described along with a novel failure mechanism. The paper ends with a set of limitations allowing for the application of these results in river engineering.     

Coffee brew shelf life modelling by integration of acceptability and quality data

A methodology for the development of shelf life models predicting consumer acceptability of coffee brew on the basis of simple and easily detectable quality indices was proposed. Coffee brew was assessed during storage for consumer acceptability using both acceptability scoring methodology and evaluation of consumer rejection by survival analysis. Samples were also analysed for hydrogen ion concentration and intensity of sensory attributes (acid, off-flavour and bitter) by a trained sensory panel. Hydrogen ion concentration and sourness resulted to be the indices best correlating with the percentage of consumer rejecting the product during storage. Mathematical functions predicting hydrogen ion concentration and sourness as a function of the risk of consumer rejection were defined. These equations provided information to assist companies in choosing acceptance limits and were integrated with classic shelf life kinetic approach to produce shelf life models accounting for consumer response. Due to lower uncertainty of hydrogen ion concentration data as compared to sourness sensory data, only the former resulted to be a useful indicator of coffee brew shelf life.