Discovering Family Groups in Passenger Social Networks

People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as a kind of the most com- mon consumer units, have a considerable scale in the field of passenger transportation market. Accurately identifying family groups can help the carriers to provide passengers with personalized travel services and precise product recommendation. This paper studies the problem of finding family groups in the field of civil aviation and proposes a family group detection method based on passenger social networks. First of all, we construct passenger social networks based on their co-travel behaviors extracted from the historical travel records; secondly, we use a collective classification algorithm to classify the social relationships between passengers into family or non-family relationship groups; finally, we employ a weighted com- munity detection algorithm to find family groups, which takes the relationship classification results as the weights of edges. Experimental results on a real dataset of passenger travel records in the field of civil aviation demonstrate that our method can effectively find family groups from historical travel records.

High Performance Classification of Android Malware Using Ensemble Machine Learning

Although Android becomes a leading operating system in market,Android users suffer from security threats due to malwares.To protect users from the threats,the solutions to detect and identify the malware variant are essential.However,modern malware evades existing solutions by applying code obfuscation and native code.To resolve this problem,we introduce an ensemble-based malware classification algorithm using malware family grouping.The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number.It also adopts unified feature extraction technique for handling seamless both bytecode and native code.We propose a unique feature selection algorithm that improves classification performance and time simultaneously.2-gram based features are generated from the instructions and segments,and then selected by using multiple filters to choose most effective features.Through extensive simulation with many obfuscated and native code malware applications,we confirm that it can classify malwares with high accuracy and short processing time.Most existing approaches failed to achieve classification speed and detection time simultaneously.Therefore,the approach can help Android users to keep themselves safe from various and evolving cyber-attacks very effectively.