Browsing by Author "Hu, Shiyan"

Now showing 1 - 4 of 4

Co-placement optimization in sensor-reusable cyber-physical digital microfluidic biochips
(2019-01-01)
Li, Jian-De
;
Kuo, Chun-Hao
;
Lu, Guan-Ruei
;
Wang, Sying-Jyan
;
Li, Katherine Shu-Min
Digital microfluidic biochips (DMFBs) facilitate modern healthcare applications. One of the most important applications is point-of-care (POC) clinical diagnosis which is directly related to human health. However, biochemical experiments are usually error-prone, which makes monitoring intermediate results during bioassay execution be required to ensure the correctness of POC clinical diagnosis. To tackle this problem, cyber-physical digital microfluidic biochips with integrated sensors have been proposed and have attracted attention recently. In order to fully utilize the sensors for detection, the reusability of sensors should be taken into consideration during the module placement stage of cyber-physical DMFBs synthesis flow. Moreover, excessive actuation of electrodes may cause reliability degradation and this issuse should also be taken care of during module placement stage. In order to deal with the aforementioned problems, this paper presents the first co-optimization method for both module and sensor placement. Experimental results show that the proposed method can effectively minimize bioassay completion time while meeting all constraints from sensors and electrodes.
Leveraging Strategic Detection Techniques for Smart Home Pricing Cyberattacks
(2016-03-04)
Liu, Yang
;
Hu, Shiyan
;
Ho, Tsung-Yi
;
資訊工程學系
;
Department of Computer Science
In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing cyberattacks. Our simulation results demonstrate that the pricing cyberattack can reduce the cyberattacker\'s bill by 34.3 percent at cost of the increase of others\' bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.
Placement Optimization of Cyber-Physical Digital Microfluidic Biochips
(2016-01-01)
Kuo, Chun-Hao
;
Lu, Guan-Ruei
;
Ho, Tsung-Yi
;
Chen, Hung-Ming
;
Hu, Shiyan
Digital microfluidic biochips maximize the possibilities in modern healthcare applications, such as point-of-care (POC) tests and immunoassays. Because of the error-prone biochemical experiments, cyber-physical digital microfluidic biochips with sensor integration is essential. However, the correctness of bioassays is closely related to the actuating times of electrodes, which should be taken care of during the module placement stage. Moreover, sensor constraint in sensor planning should also be considered to avoid sensing errors. This paper presents the first module and sensor co-placement algorithm of cyberphysical digital microfluidic biochips while considering the sensor constraint and minimizing the actuating times of electrodes. The experimental results show that we can effectively minimize the bioassay completion time and satisfy the sensor constraint.
Vulnerability Assessment and Defense Technology for Smart Home Cybersecurity Considering Pricing Cyberattacks
(2014)
Liu, Yang
;
Hu, Shiyan
;
Ho, Tsung-Yi
;
資訊工程學系
;
Department of Computer Science
Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyberattacks in the smart home context. Our simulation results demonstrate that the pricing cyberattack can reduce the attacker\'s bill by 34.3% at the cost of the increase of others\' bill by 7.9% on average. In addition, the pricing cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.