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Using malware for software-defined networking-based smart home security management through a taint checking approach
(2016-08-01)
Wang, Ping
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Chao, Kuo-Ming
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Lo, Chi-Chun
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Lin, Wen-Hui
;
Lin, Hsiao-Chung
Numerous security concerns exist in smart home systems in which Internet of Things devices are connected through a home network to enable control using a centralised gateway with a handset device from the Internet. Safeguarding personal information privacy is an increasing concern in smart living services. To guarantee the mobile security of smart living services, security managers use taint checking approaches with dynamic taint propagation analysis operations to examine how a software-defined networking app uses sensitive information and investigate suspicious security vulnerabilities of devices and the effects of the spread of taint propagation over the Internet by identifying taint paths. For solving the dynamic taint propagation analysis problem, most approaches focus on cloud computing applications (apps) with malware threat analysis that involves program vulnerability analyses, rather than on the risk posed by suspicious apps connected to the cloud computing server. Accordingly, this article proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme for tracking data with taint marking using several taint checking tools with an open software-defined networking architecture for solving the dynamic taint propagation analysis problem. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of risks posed by suspicious apps connected to the centralised gateway in a smart home system. In probabilistic risk analysis, risk and defence capability are used for each taint path to assist a defender in recognising the attack results against network threats caused by malware infection and to estimate the losses of associated taint sources. A case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. A new approach was used for verifying the details of an attack sequence for malware infection by incorporating a finite state machine to appropriately represent the real dynamic taint propagation analysis situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model enables a defender to convert the spread of taint propagation to loss and estimate the risk of a specific threat using behavioural analysis associated with 60 families of real malware. Consequently, our scheme was significantly effective in predicting the risk and loss of tainted data propagation for security concerns in smart home systems when the number of taint paths associated with the propagation rules discovered through taint analysis was increased.
Information Management and Applications of Intelligent Transportation System
(2015-01-01)
Lo, Chi-Chun
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Chao, Kuo-Ming
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Kung, Hsu-Yang
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Chen, Chi-Hua
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Chang, Maiga
A Cross-age Face Recognition Approach Using Fog Computing Architecture for User Authentication on Mobile Devices
(2018-01-01)
Lin, Wen-Hui
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Wang, Ping
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Wu, Bao-Hua
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Chao, Kuo-Ming
;
Lo, Chi-Chun
Mobile commerce security needs quality services where mobile devices respond in real-time and traditional cloud computing approach uses a centralized architecture does not support these systems with such time dependency well. Thus, this study proposes a cross-age face-recognition model with MobileNets to determine the identity of a user in the situation of aging appearance change on a fog computing architecture. Especially, this study uses a face-recognition technique based on a distributed concept. In this concept, MobileNet-based face recognition system effectively performs cross-age face recognition for identity authentication with the enhanced facial features of the users locally. Overall, the MobileNet model reaches acceptable prediction accuracy with lower latency in face recognition process compared with that of the existing CNNs schemes.
Dynamic fine-tuning stacked auto-encoder neural network for weather forecast
(2018-12-01)
Lin, Szu-Yin
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Chiang, Chi-Chun
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Li, Jung-Bin
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Hung, Zih-Siang
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Chao, Kuo-Ming
With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction results and actual data in a dynamic environment. This study applies the concept of a dynamic data-driven system to obtain the correlations between the prediction goals and number of different combination results. Association analysis, sequence analysis, and stacked auto-encoder neural network are employed to design a dynamic data-driven system based on deep learning. (C) 2018 Elsevier B.V. All rights reserved.
A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
(2017-01-01)
Wang, Ping
;
Lin, Wen-Hui
;
Chao, Kuo-Ming
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction gamma located in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting gamma = 1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.
Using ontologies to perform threat analysis and develop defensive strategies for mobile security
(2017-03-01)
Wang, Ping
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Chao, Kuo-Ming
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Lo, Chi-Chun
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Wang, Yu-Shih
;
資訊管理與財務金融系註：原資管所+財金所
Existing studies on the detection of mobile malware have focused mainly on static analyses performed to examine the code-structure signature of viruses, rather than the dynamic behavioral aspects. By contrast, the unidentified behavior of new mobile viruses using the self-modification, polymorphic, and mutation techniques for variants have largely been ignored. The problem of precision regarding malware variant detection has become one of the key concerns in mobile security. Accordingly, the present study proposed a threat risk analysis model for mobile viruses, using a heuristic approach incorporating both malware behavior analysis and code analysis to generate a virus behavior ontology associated with the Prot,g, platform. The proposed model can not only explicitly identify an attack profile in accordance with structural signature of mobile viruses, but also overcome the uncertainty regarding the probability of an attack being successful. This model is able to achieve this by extending frequent episode rules to investigate the attack profile of a given malware, using specific event sequences associated with the sandbox technique for mobile applications (apps) and hosts. For probabilistic analysis, defense evaluation metrics for each node were used to simulate the results of an attack. The simulations focused specifically on the attack profile of a botnet to assess the threat risk. The validity of the proposed approach was demonstrated numerically by using two malware cyber-attack examples. Overall, the results presented in this paper prove that the proposed scheme offers an effective countermeasure, evaluated using a set of security metrics, for mitigating network threats by considering the interaction between the attack profiles and defense needs.
A Social Trust based Recommendation Mechanism for Web Service Dynamic Collaboration
(2013)
Lin, Szu-Yin
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Yang, Ya-Ching
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Lo, Chi-Chun
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Chao, Kuo-Ming
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資訊管理與財務金融系註：原資管所+財金所
In the distributed commercial environment, many enterprises utilize Web services to provide their resources such as data and applications to consumers. They collaborate with other relevant enterprises to accomplish tasks by sharing processes and resources. However, it is difficult to find the appropriate web service combination for enterprise, because the Quality of Services (QoS) that are widely used to select Web services is not sufficient anymore. In dynamic collaboration environment, it does not only need to consider the QoS, functionality and degree of matching, but also the social trust relationship between enterprises. This study proposes a social-trust-based recommendation mechanism for service-oriented dynamic collaboration. It uses the legal binding of Service Level Agreement and the characteristics of Social Trust Network of Web services to recommend proper and trustworthy services to support dynamic collaboration.
A Novel Threat and Risk Assessment Mechanism for Security Controls in Service Management
(2013)
Wang, Ping
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Chao, Kuo-Ming
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breeches at a minimal cost. However, to support rational management decisions, TRA schemes require a careful analysis of the trade-off between the residual risk and the Return on Investment (ROI) given prescribed budget and time constraints. Accordingly, the present study proposes an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments. The proposed scheme enables defenders to identify appropriate countermeasures in accordance with three different defensive strategies associated with the organization\'s security policy. In implementing the proposed scheme, a sandbox technique is used to examine the attack profile and attack probability of various forms of cyber attacks. The cost and residual risk of various defensive strategies are then evaluated and presented to the defender as a set of recommendations. Defense evaluation metrics for each node for probabilistic analysis is used to simulate the attack results. The simulations focus specifically on the attack profile of botnet to the threat risk assessment. The validity of the proposed approach is demonstrated by simulating the TRA process for a Zeus botnet attack. Overall, the results show that iADTree provides an effective means of modeling the interaction process between the attacker and the defender, analyzing the risk at each node of the tree given various defensive strategies, and developing cost-effective countermeasures for mitigating the network threat.
Using Dynamic Taint Approach for of Malware Threat
(2015)
Wang, Ping
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Lin, Wen-Hui
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Chao, Wun Jie
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Chao, Kuo-Ming
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Lo, Chi-Chun
Most existing approaches focus on examining the values are dangerous for information flow within inter-suspicious modules of cloud applications (apps) in a host by using malware threat analysis, rather than the risk posed by suspicious apps were connected to the cloud computing server. Accordingly, this paper proposes a taint propagation analysis model incorporating a weighted spanning tree analysis scheme to track data with taint marking using several taint checking tools. In the proposed model, Android programs perform dynamic taint propagation to analyse the spread of and risks posed by suspicious apps were connected to the cloud computing server. In determining the risk of taint propagation, risk and defence capability are used for each taint path for assisting a defender in recognising the attack results against network threats caused by malware infection and estimate the losses of associated taint sources. Finally, a case of threat analysis of a typical cyber security attack is presented to demonstrate the proposed approach. Our approach verified the details of an attack sequence for malware infection by incorporating a finite state machine (FSM) to appropriately reflect the real situations at various configuration settings and safeguard deployment. The experimental results proved that the threat analysis model allows a defender to convert the spread of taint propagation to loss and practically estimate the risk of a specific threat by using behavioural analysis with real malware infection.
An efficient transaction commit protocol for composite web services
(2006)
Younas, Muhammad
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Chao, Kuo-Ming
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Lo, Chi-Chun
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Li, Yinsheng
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資訊管理與財務金融系註：原資管所+財金所
Transaction commit protocols have widely been used to ensure the correctness and reliability of distributed applications. This paper investigates into the performance of such protocols within the context of composite web services. It presents a new commit protocol which aims to improve the performance of composite web services transactions. The proposed protocol is tested through various analytical experiments. These experiments reveal that the proposed protocol significantly improves the performance in committing a composite web service transaction. The experiments also exhibit the processing overhead of the proposed protocol in the case of unsuccessful execution of a web service transaction.

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