Detecting and classifying method based on similarity matching of Android malware behavior with profile.

Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.

Development of a biphasic electroreactor with a wet scrubbing system for the removal of gaseous benzene.

An efficient, continuous flow electroreactor system comprising a scrubbing column (for absorption) and a biphasic electroreactor (for degradation) was developed to treat gas streams containing benzene. Initial benzene absorption studies using a continuous flow bubble column containing absorbents like 40% sulfuric acid, 10% silicone oil (3, 5, 10 cSt), or 100% silicone oil showed that 100% silicone oil is the most suitable. A biphasic batch electroreactor based on 50 mL of silicone oil and 100 mL of activated Co(III) (activated electrochemically) in 40% sulfuric acid demonstrated that indirect oxidation of benzene is possible by Co(III). Combined experiments on the wet scrubbing column and biphasic electroreactor (BP-ER) were performed to determine the feasibility of benzene removal, which is reside in the silicone oil medium. In semidynamic scrubbing with BP-ER experiments using an aqueous electroreactor volume of 2 L, and an inlet gas flow and a gaseous benzene concentration were 10 Lmin(-1) and 100 ppm, respectively, benzene removal efficiency is 75% in sustainable way. The trend of CO2 evolution is well correlated with benzene recovery in the BP-ER. The addition of sodiumdodecyl sulfate (SDS) enhanced the recovery of silicone oil without affecting benzene removal. This process is promising for the treatment of high concentrations of gaseous benzene.