Hui Yu, Shanchuan Ying, Sai Huang, Fan Ning, Z. Feng
Due to the widespread use of wireless communication network, the criminals easily access the Internet through these distributed points for illegal activities. However the identity of the software layer is easily falsified. If the hardware characteristics can be analyzed from the wireless transmitter, it will greatly improve the accuracy of the wireless transmitters. This paper proposes a framework for identifying wireless transmitters using multicore path network (MPN). The nonlinear power amplifier (PA) model uses volterra series to describe the nonlinear behavior of the wireless transmitters. In the proposed MPN, cyclic spectrum features are extracted as the network input. The framework not only reuses features by residual branch path but also explores new features by dense connection path. Meanwhile the MPN merges different scales through different convolution kernels. Through simulation results, we demonstrate that the proposed scheme can be superior than recent methods and has moderate computational complexity.
{"title":"Wireless Transmitter Identification Using Multicore Path Network","authors":"Hui Yu, Shanchuan Ying, Sai Huang, Fan Ning, Z. Feng","doi":"10.1109/DSA.2019.00062","DOIUrl":"https://doi.org/10.1109/DSA.2019.00062","url":null,"abstract":"Due to the widespread use of wireless communication network, the criminals easily access the Internet through these distributed points for illegal activities. However the identity of the software layer is easily falsified. If the hardware characteristics can be analyzed from the wireless transmitter, it will greatly improve the accuracy of the wireless transmitters. This paper proposes a framework for identifying wireless transmitters using multicore path network (MPN). The nonlinear power amplifier (PA) model uses volterra series to describe the nonlinear behavior of the wireless transmitters. In the proposed MPN, cyclic spectrum features are extracted as the network input. The framework not only reuses features by residual branch path but also explores new features by dense connection path. Meanwhile the MPN merges different scales through different convolution kernels. Through simulation results, we demonstrate that the proposed scheme can be superior than recent methods and has moderate computational complexity.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124592499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Failure Mode and Effect Analysis (FMEA) is a method for identifying and analyzing potential failures in systems and has been widely used for reliability and safety analysis of hardware and software systems. However, there are some shortcomings when the traditional method is applied to the system-level software FMEA, e.g., the relevant domain knowledge is scattered and not systematic, which makes the analysis result greatly depend on the experience and the familiarity of the domain to be analyzed of the analyst. Moreover, traditional methods are usually based on textual descriptions and have no tool support. These shortcomings greatly hinder the sharing and reuse of system-level software FMEA knowledge. Besides, the traditional method uses the risk priority number (RPN) to determine the priority of the failure mode, ignoring the objective attributes of the system itself, which is not reasonable enough. This paper presents a multi ontology-based system-level software fuzzy FMEA method. This method realizes the sharing and reuse of domain knowledge through the ontology. In addition, the failure mode rating method based on entropy weight and fuzzy TOPSIS overcomes the shortcoming of the traditional method and can improve the rationality of failure mode rating.
{"title":"Multi Ontology-Based System-Level Software Fuzzy FMEA Method","authors":"Xuan Hu, Jie Liu, Yichen Wang","doi":"10.1109/DSA.2019.00015","DOIUrl":"https://doi.org/10.1109/DSA.2019.00015","url":null,"abstract":"Failure Mode and Effect Analysis (FMEA) is a method for identifying and analyzing potential failures in systems and has been widely used for reliability and safety analysis of hardware and software systems. However, there are some shortcomings when the traditional method is applied to the system-level software FMEA, e.g., the relevant domain knowledge is scattered and not systematic, which makes the analysis result greatly depend on the experience and the familiarity of the domain to be analyzed of the analyst. Moreover, traditional methods are usually based on textual descriptions and have no tool support. These shortcomings greatly hinder the sharing and reuse of system-level software FMEA knowledge. Besides, the traditional method uses the risk priority number (RPN) to determine the priority of the failure mode, ignoring the objective attributes of the system itself, which is not reasonable enough. This paper presents a multi ontology-based system-level software fuzzy FMEA method. This method realizes the sharing and reuse of domain knowledge through the ontology. In addition, the failure mode rating method based on entropy weight and fuzzy TOPSIS overcomes the shortcoming of the traditional method and can improve the rationality of failure mode rating.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129015332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software plays an increasingly important role in today's world. With the advent of open-source software, an increasing number of developers begin to focus on and apply open-source software as a basic tool for their program development. However, at the same time, introducing open-source software into their own software introduces various types of defects and disadvantages. These unknown risks may cause incalculable economic loss aud credit crises if they were to be exploited in the future. Therefore, it is an important and urgent problem to detect the components of open-source software that may be reused when outsourcing software. To help detecting opensource software components in large-scale software projects, this paper proposes automatic identification technology for subnetworks with similar structural characteristics. This technology is based on node role classification, node similarity matching, and similar subnetwork search. This subject applies complex network technology to the comparison of software networks. In contrast to traditional code detection technology, this study does not constrain the text information of the software's source code. Considering that the basic skeleton structure of an application, in the processes of code reuse and features, remains the same, its network structure is used instead of its software structure to avoid problems such as poor detection results of similar codes as a result of text modification. This technology starts from the software features and the structure of software network.
{"title":"Automatic Detection for Reused Open Source Codes Based on Similarity Identification of Software Networks","authors":"Tao Shi, Liang Yan, Haoran Guo, J. Ai","doi":"10.1109/DSA.2019.00042","DOIUrl":"https://doi.org/10.1109/DSA.2019.00042","url":null,"abstract":"Software plays an increasingly important role in today's world. With the advent of open-source software, an increasing number of developers begin to focus on and apply open-source software as a basic tool for their program development. However, at the same time, introducing open-source software into their own software introduces various types of defects and disadvantages. These unknown risks may cause incalculable economic loss aud credit crises if they were to be exploited in the future. Therefore, it is an important and urgent problem to detect the components of open-source software that may be reused when outsourcing software. To help detecting opensource software components in large-scale software projects, this paper proposes automatic identification technology for subnetworks with similar structural characteristics. This technology is based on node role classification, node similarity matching, and similar subnetwork search. This subject applies complex network technology to the comparison of software networks. In contrast to traditional code detection technology, this study does not constrain the text information of the software's source code. Considering that the basic skeleton structure of an application, in the processes of code reuse and features, remains the same, its network structure is used instead of its software structure to avoid problems such as poor detection results of similar codes as a result of text modification. This technology starts from the software features and the structure of software network.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124259713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sample based cluster scheduling is considered promising for its high-scalability and low-latency. Its major limitation, on the other hand, is its very limited view of cluster resource state. The limitation confines both its decision precision and the support towards many important scheduling features. There have been several approaches to solve this limitation, yet these works are mostly high-cost solutions that use either extra communication or system component to collect more resource information, which damage the scalability and latency of sample based cluster scheduling. In this paper, we propose L-PCS, a novel learning-based approach based on latest private-cluster-state to generate a relatively accurate knowledge of global cluster state. L-PCS gathers and learns process data of schedulers and predicts a more precise approximation of real-time cluster state for each scheduler. It is a dynamic model updated through time for time-validity. The results predicted by trained model serve as references when schedulers make scheduling decisions. Experiment shows that comparing to sample based schedulers without such learning mechanism, L-PCS improves mean absolute error by 2 × to 3 × and gang scheduling results show a maximum increase of 10.1% to 25.09%.
{"title":"Learning Latest Private-Cluster-State to Improve the Performance of Sample-Based Cluster Scheduling","authors":"Yawen Wang, Qing Wang","doi":"10.1109/DSA.2019.00014","DOIUrl":"https://doi.org/10.1109/DSA.2019.00014","url":null,"abstract":"Sample based cluster scheduling is considered promising for its high-scalability and low-latency. Its major limitation, on the other hand, is its very limited view of cluster resource state. The limitation confines both its decision precision and the support towards many important scheduling features. There have been several approaches to solve this limitation, yet these works are mostly high-cost solutions that use either extra communication or system component to collect more resource information, which damage the scalability and latency of sample based cluster scheduling. In this paper, we propose L-PCS, a novel learning-based approach based on latest private-cluster-state to generate a relatively accurate knowledge of global cluster state. L-PCS gathers and learns process data of schedulers and predicts a more precise approximation of real-time cluster state for each scheduler. It is a dynamic model updated through time for time-validity. The results predicted by trained model serve as references when schedulers make scheduling decisions. Experiment shows that comparing to sample based schedulers without such learning mechanism, L-PCS improves mean absolute error by 2 × to 3 × and gang scheduling results show a maximum increase of 10.1% to 25.09%.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Complex CPS such as VAS got rapid development these years, but also became vulnerable to GPS spoofing, packets injection, buffer-overflow and other malicious attacks. Ensuring the behaviors of VAS always keeping secure no matter how the environment changes, would be a prospective direction for VAS security. This paper aims at presenting a reactive synthesisbased approach to implement the automatic generation of secure VAS controller. First, we study the operating mechanism of VAS and construct a high-Ievel model consisting of actuator and monitor. Besides, we analyze the security threats of VAS from the perspective of hardware, software and data transmission, and then extract the corresponding specifications of security properties with LTL formulas. Based on the VAS model and security specifications, the controller can be constructed by GR(l) synthesis algorithm, which is a two-player game process between VAV and Environment. Finally, we expand the function of LTLMoP platform to construct the automatons for controller in multi-robots system, which provides secure behavior strategies under several typical VAS attack scenarios.
{"title":"Synthesizing Secure Reactive Controller for Unmanned Aerial System","authors":"W. Lu, Shaoxian Shu, Rao Shi, Rui Li, Wei Dong","doi":"10.1109/DSA.2019.00065","DOIUrl":"https://doi.org/10.1109/DSA.2019.00065","url":null,"abstract":"Complex CPS such as VAS got rapid development these years, but also became vulnerable to GPS spoofing, packets injection, buffer-overflow and other malicious attacks. Ensuring the behaviors of VAS always keeping secure no matter how the environment changes, would be a prospective direction for VAS security. This paper aims at presenting a reactive synthesisbased approach to implement the automatic generation of secure VAS controller. First, we study the operating mechanism of VAS and construct a high-Ievel model consisting of actuator and monitor. Besides, we analyze the security threats of VAS from the perspective of hardware, software and data transmission, and then extract the corresponding specifications of security properties with LTL formulas. Based on the VAS model and security specifications, the controller can be constructed by GR(l) synthesis algorithm, which is a two-player game process between VAV and Environment. Finally, we expand the function of LTLMoP platform to construct the automatons for controller in multi-robots system, which provides secure behavior strategies under several typical VAS attack scenarios.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"[Title page iii]","authors":"","doi":"10.1109/dsa.2019.00002","DOIUrl":"https://doi.org/10.1109/dsa.2019.00002","url":null,"abstract":"","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128580805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a software defect prediction algorithm based on improved BP neural network, which can effectively improve the prediction accuracy caused by the imbalance of the category distribution of data within the project. In this paper, in order to improve the data imbalance in the project, we use SMOTE algorithm to increase the minority samples (defective software modules), the ENN (Extended Nearest Neighbor Algorithm ) data cleaning algorithm is performed for the post-sampling data noise problem. The SA ( Simulated Annealing ) algorithm is used to optimize the four- layers BP neural network to establish the classification prediction model on the AEEEM database. We use cross validation to evaluate the performance of the proposed algorithm on AEEEM database. The results show that the proposed algorithm can effectively improve the performance of the model in predicting unbalanced data.
{"title":"Software Defect Prediction Model Based on Improved BP Neural Network","authors":"Y. Liu, Fengli Sun, Jun Yang, Donghong Zhou","doi":"10.1109/DSA.2019.00095","DOIUrl":"https://doi.org/10.1109/DSA.2019.00095","url":null,"abstract":"This paper proposes a software defect prediction algorithm based on improved BP neural network, which can effectively improve the prediction accuracy caused by the imbalance of the category distribution of data within the project. In this paper, in order to improve the data imbalance in the project, we use SMOTE algorithm to increase the minority samples (defective software modules), the ENN (Extended Nearest Neighbor Algorithm ) data cleaning algorithm is performed for the post-sampling data noise problem. The SA ( Simulated Annealing ) algorithm is used to optimize the four- layers BP neural network to establish the classification prediction model on the AEEEM database. We use cross validation to evaluate the performance of the proposed algorithm on AEEEM database. The results show that the proposed algorithm can effectively improve the performance of the model in predicting unbalanced data.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"152 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129214388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present Auto-Tracking technology attracted increasing attention in the field of stage lighting. Most of the existing automatic lighting tracing methods adopt indoor positioning technology to acquire the location parameters of the tracing object. Then the location parameters were converted into the control data of moving light for automatic tracing. The main problems are as follows: one is the positional errors of special light position such as front light and fixed-point light. The other is the miss tracking of moving targets. In this paper, according to the projection requirements of front light and fixed-point light, the existing tracing model is improved. The error between the spot and the actual position is analyzed. Test results show that the accuracy of the upper left and upper right directions is relatively high. The bigger the curvature amplitude is, the bigger the error is. At the same time, the test data provide theory evidence for the setting of the tracing light position and the design of the performance route.
{"title":"A Method of Improved Automatic Light Tracing","authors":"Yujian Jiang, Yan-Niu Ren, Kai Song, Wei Jiang","doi":"10.1109/DSA.2019.00016","DOIUrl":"https://doi.org/10.1109/DSA.2019.00016","url":null,"abstract":"At present Auto-Tracking technology attracted increasing attention in the field of stage lighting. Most of the existing automatic lighting tracing methods adopt indoor positioning technology to acquire the location parameters of the tracing object. Then the location parameters were converted into the control data of moving light for automatic tracing. The main problems are as follows: one is the positional errors of special light position such as front light and fixed-point light. The other is the miss tracking of moving targets. In this paper, according to the projection requirements of front light and fixed-point light, the existing tracing model is improved. The error between the spot and the actual position is analyzed. Test results show that the accuracy of the upper left and upper right directions is relatively high. The bigger the curvature amplitude is, the bigger the error is. At the same time, the test data provide theory evidence for the setting of the tracing light position and the design of the performance route.","PeriodicalId":342719,"journal":{"name":"2019 6th International Conference on Dependable Systems and Their Applications (DSA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126606265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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