With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win+Intel alliance in PC, Android+ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app's virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF), the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.
Jianlin XuYifan YuZhen ChenBin CaoWenyu DongYu GuoJunwei Cao
Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of Service (QoS) and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.
Collaborative filtering solves information overload problem by presenting personalized content to individual users based on their interests, which has been extensively applied in real-world recommender systems. As a class of simple but efficient collaborative filtering method, similarity based approaches make predictions by finding users with similar taste or items that have been similarly chosen. However, as the number of users or items grows rapidly, the traditional approach is suffering from the data sparsity problem. Inaccurate similarities derived from the sparse user-item associations would generate the inaccurate neighborhood for each user or item. Consequently, its poor recommendation drives us to propose a Threshold based Similarity Transitivity (TST) method in this paper. TST firstly filters out those inaccurate similarities by setting an intersection threshold and then replaces them with the transitivity similarity. Besides, the TST method is designed to be scalable with MapReduce framework based on cloud computing platform. We evaluate our algorithm on the public data set MovieLens and a real-world data set from AppChina (an Android application market) with several well-known metrics including precision, recall, coverage, and popularity. The experimental results demonstrate that TST copes well with the tradeoff between quality and quantity of similarity by setting an appropriate threshold. Moreover, we can experimentally find the optimal threshold which will be smaller as the data set becomes sparser. The experimental results also show that TST significantly outperforms the traditional approach even when the data becomes sparser.
Bitmap indexing has been widely used in various applications due to its speed in bitwise operations.However,it can consume large amounts of memory.To solve this problem,various bitmap coding algorithms have been proposed.In this paper,we present COMbining Binary And Ternary encoding(COMBAT),a new bitmap index coding algorithm.Typical algorithms derived from Word Aligned Hybrid(WAH)are COMPressed Adaptive inde X(COMPAX)and Compressed"n"Composable Integer Set(CONCISE),which can combine either two or three continuous words after WAH encoding.COMBAT combines both mechanisms and results in more compact bitmap indexes.Moreover,querying time of COMBAT can be faster than that of COMPAX and CONCISE,since bitmap indexes are smaller and it would take less time to load them into memory.To prove the advantages of COMBAT,we extend a theoretical analysis model proposed by our group,which is composed of the analysis of various possible bitmap indexes.Some experimental results based on real data are also provided,which show COMBAT’s storage and speed superiority.Our results demonstrate the advantages of COMBAT and codeword statistics are provided to solidify the proof.
Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service (DDoS) attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management (UTM) and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud-based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.
