Vol. 43, No. 3,
Mar. 2016
Digital Library
Mandatory Access Control for Android Application Security
June-sung Na, Do-Yun Kim, Wooguil Pak, Young-June Choi
In this paper, we investigate the security issues of the Android platform which dominates the global market of smart mobile devices. The current permission model for Android security is not powerful and has two problems. One is the coarse-grained relationship between permissions and methods which require them. The other is that mobile users do not have rights to control the permissions of the application. To solve these problems, we propose MacDroid which can control the platform’s resources for accessing installed applications. Users can control the application’s behavior via MacDroid’s policy. We have divided the permission set into method units. The results of the performance test using a pure Android platform show that our proposed scheme can improve security within a short time.
Malware Family Recommendation using Multiple Sequence Alignment
Malware authors spread malware variants in order to evade detection. It`s hard to detect malware variants using static analysis. Therefore dynamic analysis based on API call information is necessary. In this paper, we proposed a malware family recommendation method to assist malware analysts in classifying malware variants. Our proposed method extract API call information of malware families by dynamic analysis. Then the multiple sequence alignment technique was applied to the extracted API call information. A signature of each family was extracted from the alignment results. By the similarity of the extracted signatures, our proposed method recommends three family candidates for unknown malware. We also measured the accuracy of our proposed method in an experiment using real malware samples.
Energy-aware EDZL Real-Time Scheduling on Multicore Platforms
Mobile real-time systems with limited system resources and a limited power source need to fully utilize the system resources when the workload is heavy and reduce energy consumption when the workload is light. EDZL (Earliest Deadline until Zero Laxity), a multiprocessor real-time scheduling algorithm, can provide high system utilization, but little work has been done aimed at reducing its energy consumption. This paper tackles the problem of DVFS (Dynamic Voltage/Frequency Scaling) in EDZL scheduling. It proposes a technique to compute a uniform speed on full-chip DVFS platforms and individual speeds of tasks on per-core DVFS platforms. This technique, which is based on the EDZL schedulability test, is a simple but effective one for determining the speeds of tasks offline. We also show through simulation that the proposed technique is useful in reducing energy consumption.
Memory Efficient Parallel Ray Casting Algorithm for Unstructured Grid Volume Rendering on Multi-core CPUs
We present a novel memory-efficient parallel ray casting algorithm for unstructured grid volume rendering on multi-core CPUs. Our method is based on the Bunyk ray casting algorithm. To solve the high memory overhead problem of the Bunyk algorithm, we allocate a fixed size local buffer for each thread and the local buffers contain information of recently visited faces. The stored information is used by other rays or replaced by other face’s information. To improve the utilization of local buffers, we propose an image-plane based ray grouping algorithm that makes ray groups have high coherency. The ray groups are then distributed to computing threads and each thread processes the given groups independently. We also propose a novel hash function that uses the index of faces as keys for calculating the buffer index each face will use to store the information. To see the benefits of our method, we applied it to three unstructured grid datasets with different sizes and measured the performance. We found that our method requires just 6% of the memory space compared with the Bunyk algorithm for storing face information. Also it shows compatible performance with the Bunyk algorithm even though it uses less memory. In addition, our method achieves up to 22% higher performance for a large-scale unstructured grid dataset with less memory than Bunyk algorithm. These results show the robustness and efficiency of our method and it demonstrates that our method is suitable to volume rendering for a large-scale unstructured grid dataset.
A Process Algebra for Modeling Secure Movements of Distributed Mobile Processes
Some process algebras were applied to enterprise business modelling for formal specification and verification. π-calculus and mobile ambient can be considered for the distributed and mobile, especially to represent the movements of distributed real-time business processes. However there are some limitations to model the movements: 1) π-calculus passes the name of port for indirect movements, and 2) mobile ambient uses ambient to synchronize asynchronous movements forcefully. As a solution to the limitations, this paper presents a new process algebra, called δ-calculus, to specify direct and synchronous movements of business processes over geo-temporal space. Any violation of safety or security of the systems caused by the movements can be indicated by the properties of the movements: synchrony, priority and deadline. A tool, called SAVE, was developed on ADOxx meta-modelling platform to demonstrate the concept.
Buffer Management Scheme for Interactive Video Streaming
Kwang-Min Na, Tae-Young Lee, Heon-Hui Kim, Kwang-Hyun Park, Yong-Hoon Choi
In this paper, we propose a buffer management scheme suitable for interactive multimedia services. We consider a typical delay optimization environment so that receiver buffer lengths vary according to the round trip time estimation. In this environment, we propose an optimization technique for minimizing the loss of information that may occur when a reduced buffer length forces I/P/B frames in the buffer to drop. We modeled our problem as a Knapsack Problem for which we used dynamic programing in order to find an approximate solution. The proposed technique is compared with the existing buffer management techniques. Through simulation studies, we found that our approach could increase PSNR, which is important to video quality.
An R&E Model between University and High School for Information & Communication Technology Major Introduction and its Case Study
Since no course is offered in the area of professional ICT(Information & Communication Technology) in the high school curriculum, high school students do not have the opportunity to learn about the ICT engineering area and the possible career paths in the field. Due to this problem, high school students are not motivated to choose ICT majors at university level, and in turn, the ICT departments are struggling to recruit qualified students. In this paper, we present an R&E (research and education) model to mitigate this problem. We also present a case study on the program following the model offered by “H“ university in collaboration with a local high school. Through the program we provided high school students with design and development experiences to solve engineering problems related to the ICT area and tried to attract their attention to ICT majors. The participants of the R&E program were able to experience the systematic engineering design process, ICT tools, teamwork, and communication skills through problem solving procedures. Based on three years of observation and the survey, it was found that more than 76% of students were motivated highly by ICT and engineering majors via the program. The main contribution of the paper is that we have proposed and proved the R&E program model and applied the ICT R&E model to a program to attract qualified students to ICT majors.
Fin Cutting Line Detection Technique based on RANSAC for Fish Cutting Automation System
The fishing industry requires many workers to manually carry out the jobs of sorting and cutting fishes. There are therefore many dangerous situations in their working environment and the throughput is inefficiently low. This paper introduces an automatic fin cutting system based on RANSAC that is able to increase the throughput of fish processing jobs. The system proposed in this paper first detects the edges of a fish using a high-pass filter. The boundary lines between fin and body are then detected by adjusting parameters and the threshold of the noise filters. Finally, the optimal cutting lines are detected using RANSAC. Through an experiment with a sample of 50 fishes, this paper shows that the proposed system detects the cutting lines with about 90% accuracy.
Tumor Motion Tracking during Radiation Treatment using Image Registration and Tumor Matching between Planning 4D MDCT and Treatment 4D CBCT
During image-guided radiation treatment of lung cancer patients, it is necessary to track the tumor motion because it can change during treatment as a consequence of respiratory motion and cardiac motion. In this paper, we propose a method for tracking the motion of the lung tumors based on the three-dimensional image information from planning 4D MDCT and treatment 4D CBCT images. First, to effectively track the tumor motion during treatment, the global motion of the tumor is estimated based on a tumor-specific motion model obtained from planning 4D MDCT images. Second, to increase the accuracy of the tumor motion tracking, the local motion of the tumor is estimated based on the structural information of the tumor from 4D CBCT images. To evaluate the performance of the proposed method, we estimated the tracking results of proposed method using digital phantom. The results show that the tumor localization error of local motion estimation is reduced by 45% as compared with that of global motion estimation.
Syllable-based Korean POS Tagging Based on Combining a Pre-analyzed Dictionary with Machine Learning
Chung-Hee Lee, Joon-Ho Lim, Soojong Lim, Hyun-Ki Kim
This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.
Ontology Modeling and Rule-based Reasoning for Automatic Classification of Personal Media
Hyun-Kyu Park, Chi-Seung So, Young-Tack Park
Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can’t classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.
An Efficient Large Graph Clustering Technique based on Min-Hash
Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.
A Location-based Highway Safety System using Smart Mobile Devices
Jaehyun Lee, Sungjin Park, Joon Yoo
In this paper, we propose a highway safety system that comprises a small number of central servers and smart mobile devices. To implement this system, we constructed a central server that collects GPS location information on cars, whose update messages are decreased via the car location estimation algorithm. The in-car mobile devices use the accelerometer sensors to detect hazardous situations; this information is updated to the central server that relays the information to the corresponding endangered cars via location-based unicast using LTE communication. To evaluate the proposed algorithm, we equipped a mobile device app on a real car and conducted real experiments in various environments such as city streets, rural areas, and highway roads. Furthermore, we conducted simulations to evaluate the propagation of danger information. Finally, we conducted simulated experiments to detect car collisions as well as exceptions, such as falling of the mobile device from the cradle.
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