Biblio

Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.

HDFS has been widely used for storing massive scale data which is vulnerable to site disaster. The file system backup is an important strategy for data retention. In this paper, we present an efficient, easy- to-use Backup and Disaster Recovery System for HDFS. The system includes a client based on HDFS with additional feature of remote backup, and a remote server with a HDFS cluster to keep the backup data. It supports full backup and regularly incremental backup to the server with very low cost and high throughout. In our experiment, the average speed of backup and recovery is up to 95 MB/s, approaching the theoretical maximum speed of gigabit Ethernet.

We propose secure RAID, i.e., low-complexity schemes to store information in a distributed manner that is resilient to node failures and resistant to node eavesdropping. We generalize the concept of systematic encoding to secure RAID and show that systematic schemes have significant advantages in the efficiencies of encoding, decoding and random access. For the practical high rate regime, we construct three XOR-based systematic secure RAID schemes with optimal encoding and decoding complexities, from the EVENODD codes and B codes, which are array codes widely used in the RAID architecture. These schemes optimally tolerate two node failures and two eavesdropping nodes. For more general parameters, we construct efficient systematic secure RAID schemes from Reed-Solomon codes. Our results suggest that building “keyless”, information-theoretic security into the RAID architecture is practical.