Jiang, H, Chang, R, Ren, L, Dong, W, Jiang, L & Yang, S 2017, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).View/Download from: Publisher's site
© 2017, Springer International Publishing AG. Owing to lack of authentication for client application (CA), traditional protection mechanism based on ARM TrustZone may lead to the sensitive data leakage within trusted execution environment (TEE). Furthermore, session resources will be occupied by malicious CA due to the design drawback for session mechanism between CA and trusted application (TA). Therefore, attackers can initiate a request to read the data stored in secure world or launch DoS attack by forging malicious CA. In order to address the authentication problems, this paper proposes a CA authentication scheme using ARM TrustZone. When CA establishes a session with trusted application, a CA authentication will be executed in TEE to prevent sensitive data from being accessed by malicious. At the same time, TA closes the session and releases occupied resources. The proposed authentication scheme is implemented on simulation platform built by QEMU and OP-TEE. The experimental results show that the proposed scheme can detect the content change of CA, avoid sensitive data leakage and prevent DoS attack.
Yang, S, Huang, G, Ofoghi, B & Yearwood, J 2020, 'Short text similarity measurement using context-aware weighted biterms', Concurrency and Computation: Practice and Experience.View/Download from: Publisher's site
With the development of internet technologies, social media and mobile devices, short texts have become an increasingly popular medium among users to communicate with friends, search information and review products. Measuring the similarity between short texts is a fundamental task due to its importance in many applications, such as text retrieval, topic discovery, and event detection. However, short texts generally comprise sparse, noisy, and ambiguous information. Hence, effectively measuring the distance between short texts is a challenging task. In this paper, we exploit the advantageous corpus-wide word co-occurrence information into document-level feature enrichment to mitigate the challenges caused by the sparseness of short texts for distance measurement. We propose a novel context-aware weighted Biterm method for short text Distance Measurement (BDM). In BDM, we extract biterms (ie, word pairs) from a short text corpus and exploit a biterm topic model to determine the global weights of biterms in the corpus. We then determine the local importance of a biterm in different contexts (ie, short texts) based on the corpus-level biterm weight. The distance between two short texts is computed using the context-aware weighted biterms. Experimental results on three real-world datasets demonstrate better accuracy and effectiveness of the proposed BDM.
© 2013 IEEE. Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo. However, the instant features (such as abbreviation and informal expression) and the limited length of short texts challenge the clustering task. Fortunately, short texts about the same topic often share some common terms (or term stems), which can effectively represent a topic (i.e., supported by a cluster of short texts), and we also call them topic representative terms. Taking advantage of topic representative terms, it is much easier to cluster short texts by grouping short texts into the most similar topic representative term groups. This paper provides a novel topic representative term discovery (TRTD) method for short text clustering. In our TRTD method, we discover groups of closely bound up topic representative terms by exploiting the closeness and significance of terms. The closeness of the topic representative terms is measured by their interdependent co-occurrence, and the significance is measured by their global term occurrences throughout the whole short text corpus. The experimental results on real-world datasets demonstrate that TRTD achieves better accuracy and efficiency in short text clustering than the state-of-the-art methods.
Chang, R, Jiang, L, Chen, W, He, H, Yang, S, Jiang, H, Liu, W & Liu, Y 2018, 'Towards a multilayered permission-based access control for extending Android security', Concurrency Computation, vol. 30.View/Download from: Publisher's site
Copyright © 2017 John Wiley & Sons, Ltd. This paper discusses security issues on the user equipment, which is the “last mile” of social networks. One of the main Achilles’ heel of social networks is not the organization of networks themselves, but the user devices, typically Android ones. The existing system of privileges makes it easy to infiltrate the network via applications installed on users’ devices. Conventional signature-based and static analysis methods are vulnerable. Access to privacy- and security-relevant parts of the application programming interface is controlled by the corresponding permission in a manifest file. While requesting access to permissions, it may offer opportunities to malicious codes, which will cause security issues. Few works among permission analysis, however, pay attention to the prevention of permission leakage on both hardware and software frameworks. In this paper we tackle the challenge of providing our multilayered permission-based security extension scheme on Android platforms. We propose a usage and access control model and an effective method of preventing permission leakage based on ARM TrustZone security extension mechanism. In contrast to previous work, the proposed security architecture provides a permission-based mandatory access control on Android middleware, Linux kernel, and hardware layers. The evaluation results demonstrate the effectiveness of the proposed scheme in mitigating permission leakage vulnerabilities.
Chen, X, Chen, W, Lu, Z, Long, P, Yang, S & Wang, Z 2017, 'A Duplication-Aware SSD-Based Cache Architecture for Primary Storage in Virtualization Environment', IEEE Systems Journal, vol. 11, no. 4, pp. 2578-2589.View/Download from: Publisher's site
Zhou, J, Yang, S, Xiao, C & Chen, F, 'Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia'.
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused
unprecedented impacts to people's daily life around the world. Various measures
and policies such as lock-down and social-distancing are implemented by
governments to combat the disease during the pandemic period. These measures
and policies as well as virus itself may cause different mental health issues
to people such as depression, anxiety, sadness, etc. In this paper, we exploit
the massive text data posted by Twitter users to analyse the sentiment dynamics
of people living in the state of New South Wales (NSW) in Australia during the
pandemic period. Different from the existing work that mostly focuses the
country-level and static sentiment analysis, we analyse the sentiment dynamics
at the fine-grained local government areas (LGAs). Based on the analysis of
around 94 million tweets that posted by around 183 thousand users located at
different LGAs in NSW in five months, we found that people in NSW showed an
overall positive sentimental polarity and the COVID-19 pandemic decreased the
overall positive sentimental polarity during the pandemic period. The
fine-grained analysis of sentiment in LGAs found that despite the dominant
positive sentiment most of days during the study period, some LGAs experienced
significant sentiment changes from positive to negative. This study also
analysed the sentimental dynamics delivered by the hot topics in Twitter such
as government policies (e.g. the Australia's JobKeeper program, lock-down,
social-distancing) as well as the focused social events (e.g. the Ruby Princess
Cruise). The results showed that the policies and events did affect people's
overall sentiment, and they affected people's overall sentiment differently at
Yang, S, Huang, G & Ofoghi, B 2019, 'Short Text Similarity Measurement Using Context from Bag of Word Pairs and Word Co-occurrence', International Conference on Data Service, Springer, pp. 221-231.
Yang, S, Huang, G, Zhou, X & Xiang, Y 2019, 'Dynamic Clustering of Stream Short Documents Using Evolutionary Word Relation Network', International Conference on Data Service, Springer, pp. 418-428.
Chang, R, Jiang, L, Chen, W, He, H & Yang, S 2017, 'Enhancement of permission management for an ARM-android platform', Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Netwo.View/Download from: Publisher's site
© 2016 IEEE. As the number of smart devices continues to grow dramatically, programmes and data handled by such smart devices have become the primary targets of hackers and malwares. ARM-Android is the most widespread platform for smart devices. Access to privacy-and security-relevant parts of the API is controlled by the corresponding permission in a manifest. However, while requesting access to permissions, these applications may offer opportunities to malicious codes to gain access to other inaccessible resources which will cause a series of security issues. Recently, many researchers focus on the permission-based mechanism which restricts accesses of users and applications to critical resources on an ARM-Android device. Few works among permission analysis, however, pay attention to the prevention of permission leakage on both hardware and software frameworks. In this paper we tackle the challenge of providing our permission-based security architecture on ARM-Android platform. We propose an usage and access control model and an effective method of preventing permission leakage based on ARM TrustZone security extension. In contrast to previous work, the proposed security architecture provides a flexible mandatory access control on Android middleware, Linux kernel, and hardware layers. The evaluation results demonstrate the effectiveness in mitigating permission leakage vulnerabilities.
Yang, S, Huang, G, Xiang, Y, Zhou, X & Chi, C-H 2017, 'Modeling User Preferences on Spatiotemporal Topics for Point-of-Interest Recommendation', Proceedings - 2017 IEEE 14th International Conference on Services Computing, SCC 2017.View/Download from: Publisher's site
© 2017 IEEE. With the development of the location-based social networks (LBSNs) and the popular of mobile devices, a plenty of user’s check-in data accumulated enough to enable personalized Point-of-Interest recommendations services. In this paper, we propose a scheme of modeling user’s preferences on spatiotemporal topics (UPOST scheme) for accurate individualized POI recommendation. In the UPOST scheme, we discover temporal topics from semantic locations (i.e., people’s description words for a location) to learn users’ preferences. UPOST infers user’s preference for different types of places during different periods by learning the spatiotemporal topics from the historical semantic locations of users. We have developed two algorithms under the UPOST scheme: The time division LDA algorithm (TDLDA) and the time adaptive topic discovery algorithm (TATD). In TDLDA, we divide the check-in dataset into different time segments and use one LDA for one segment. Then we improve TDLDA further by developing a new TATD algorithm to discover spatiotemporal topics. The experimental results demonstrate the effectiveness of our UPOST scheme, both TDLDA and TATD outperform the counterpart method that do not consider semantic locations.
Chen, X, Chen, W, Yang, S, Lu, Z & Wang, Z 2014, 'DASH: A duplication-aware flash cache architecture in virtualization environment', Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS.View/Download from: Publisher's site
© 2014 IEEE. With the rapid development of multi-core and multi-threading technologies, the performance gap between CPU and storage system is widening year by year, causing the storage system to be the bottleneck of the whole system performance. To alleviate this situation, flash memory has been used as the caching device of HDDs. On the other hand, cloud computing is becoming more and more popular and mature in industry field. As the key building block of it, virtualization technology allows several virtual machines (VMs) running on one single physical machine simultaneously, most of which usually run the same or similar operating systems and applications. In this scenario, flash cache will be occupied by many duplicate data blocks. However, existing flash cache architectures and replacement policies don’t take this observation into consideration, which greatly limits the efficient use of the flash cache. In this paper, we propose a new duplication-aware flash cache architecture (DASH). In this architecture, flash cache is organized to cache only one copy of the duplicate data blocks, which can notably expand the effective cache capacity, making more I/O requests hit in the cache. Moreover, this architecture can reduce the amount of data written to flash cache, and thus the life span of flash device can be significantly prolonged. Experiments based on realistic applications show that, in some situations, our cache architecture can improve the cache hit ratio by 5 times, reduce the average I/O latency by 63% and eliminate flash cache writes by 81%.