A recent class of threats, known as Advanced Persistent Threats (APTs), has drawn increasing attention from researchers, primarily from the industrial security sector. APTs are cyber attacks executed by sophisticated and well-resourced adversaries targeting specific information in high-profile companies and governments, usually in a long term campaign involving different steps. To a significant extent, the academic community has neglected the specificity of these threats and as such an objective approach to the APT issue is lacking. In this paper, we present the results of a comprehensive study on APT, characterizing its distinguishing characteristics and attack model, and analyzing techniques commonly seen in APT attacks. We also enumerate some non-conventional countermeasures that can help to mitigate APTs, hereby highlighting the directions for future research.

We report conversion efficiencies of experimental single and dual light guide luminescent solar concentrators. We have built several 5 cm × 5 cm and 10× cm × 10 cm luminescent solar concentrator (LSC) demonstrators consisting of c-Si photovoltaic cells attached to luminescent light guides of Lumogen F Red 305 dye and perylene perinone dye. The highest overall efficiency obtained was 4.2% on a 5 cm × 5 cm stacked dual light guide using both luminescent materials. To our knowledge, this is the highest reported experimentally determined efficiency for c-Si photovoltaic-based LSCs. Furthermore, we also produced a 5 cm × 5 cm LSC specimen based on an inorganic phosphor layer with an overall efficiency of 2.5%.

Malware typically uses Domain Generation Algorithms (DGAs) as a mechanism to contact their Command and Control server. In recent years, different approaches to automatically detect generated domain names have been proposed, based on machine learning. The first problem that we address is the difficulty to systematically compare these DGA detection algorithms due to the lack of an independent benchmark. The second problem that we investigate is the difficulty for an adversary to circumvent these classifiers when the machine learning models backing these DGA-detectors are known. In this paper we compare two different approaches on the same set of DGAs: classical machine learning using manually engineered features and a 'deep learning' recurrent neural network. We show that the deep learning approach performs consistently better on all of the tested DGAs, with an average classification accuracy of 98.7% versus 93.8% for the manually engineered features. We also show that one of the dangers of manual feature engineering is that DGAs can adapt their strategy, based on knowledge of the features used to detect them. To demonstrate this, we use the knowledge of the used feature set to design a new DGA which makes the random forest classifier powerless with a classification accuracy of 59.9%. The deep learning classifier is also (albeit less) affected, reducing its accuracy to 85.5%. CCS CONCEPTS• Security and privacy → Malware and its mitigation; • Computing methodologies → Neural networks; Classification and regression trees;

No abstract

No abstract