N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Streaming Italian horror cinema in the UK: Lovefilm Instant.

This article investigates the distribution of Italian horror cinema in the age of video streaming, analyzing its presence and categorization on the platform Lovefilm Instant UK, in order to investigate the importance of ‘niche’ in what is known as the long tail of online distribution and the online availability of exploitation films. I argue that looking at the streaming presence of Italian horror and comparing it to its prior distribution on home video formats (in particular VHS and DVD) we can grasp how distribution and access have shaped the understanding of the genre. In particular, I address the question of the categorization of the films made by the S-VOD services and the limits of streaming distribution, such as lack of persistency in availability and the need of enhanced curatorship.

Non-invasive Ventilation of Patients with ARDS: Insights from the LUNG SAFE Study

Background: Non-invasive ventilation (NIV) is increasingly used in patients with Acute Respiratory Distress Syndrome (ARDS). Whether, during NIV, the categorization of ARDS severity based on the PaO2/FiO2 Berlin criteria is useful is unknown. The evidence supporting NIV use in patients with ARDS remains relatively sparse.

Methods: The Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study described the management of patients with ARDS. This sub-study examines the current practice of NIV use in ARDS, the utility of the PaO2/FiO2 ratio in classifying patients receiving NIV and the impact of NIV on outcome.

Results: Of 2,813 patients with ARDS, 436 (15.5%) were managed with NIV on days 1 and 2 following fulfillment of diagnostic criteria. Classification of ARDS severity based on PaO2/FiO2 ratio was associated with an increase in intensity of ventilatory support, NIV failure, and Intensive Care Unit (ICU) mortality. NIV failure occurred in 22.2% of mild, 42.3% of moderate and 47.1% of patients with severe ARDS. Hospital mortality in patients with NIV success and failure was 16.1 % and 45.4%, respectively. NIV use was independently associated with increased ICU (HR 1.446; [1.159-1.805]), but not hospital mortality. In a propensity matched analysis, ICU mortality was higher in NIV than invasively ventilated patients with a PaO2/FiO2 lower than 150 mmHg.

Conclusions: NIV was used in 15% of patients with ARDS, irrespective of severity category. NIV appears to be associated with higher ICU mortality in patients with a PaO2/FiO2 lower than 150 mmHg.

Trial Registration: ClinicalTrials.gov NCT02010073