Browsing by Author "Lee, Chun-Liang"

Now showing 1 - 6 of 6

A computer virus spreading model based on resource limitations and interaction costs
(2013-03-01)
Huang, Chung-Yuan
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Lee, Chun-Liang
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Wen, Tzai-Hung
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Sun, Chuen-Tsai
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資訊工程學系
Computer viruses are major threats to Internet security and privacy, therefore many researchers are addressing questions linked to virus propagation properties, spreading models, epidemic dynamics, tipping points, and control strategies. We believe that two important factors - resource limitations and costs - are being overlooked in this area due to an overemphasis on power-law connectivity distributions of scale-free networks affecting computer virus epidemic dynamics and tipping points. The study show (a) a significant epidemic tipping point does exists when resource limitations and costs are considered, with the tipping point exhibiting a lower bound; (b) when interaction costs increase or usable resources decrease, epidemic tipping points in scale-free networks grow linearly while density curves decrease linearly; (c) regardless of whether Internet user resources obey delta, uniform, or normal distributions, they retain the same epidemic dynamics and tipping points as long as the average value of those resources remains unchanged across different scale-free networks; (d) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased through the use of a throttling strategy. (C) 2012 Elsevier Inc. All rights reserved.
A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection
(2015-10-05)
Lee, Chun-Liang
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Lin, Yi-Shan
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Chen, Yaw-Chung
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資訊工程學系
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Department of Computer Science
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.
An Efficient Conflict Detection Algorithm for Packet Classification
(2010)
Lee, Chun-Liang
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Lin, Guan-Yu
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Chen, Yaw-Chung
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資訊工程學系
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Department of Computer Science
Packet classification is essential for supporting advanced network services such as firewall, quality-of-service (QoS), and policy-based routing. The rules used by routers to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. In this paper, we propose an algorithm which can efficiently detect and resolve filter conflicts by using tuple based search. The time complexity of the proposed algorithm is O(nW+S), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and S is the number of conflicts. We use the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than the plane-sweep algorithm both in time and space, particularly for databases with larger numbers of conflicts.
An Efficient Conflict Detection Algorithm for Packet Filters
(2012-02-01)
Lee, Chun-Liang
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Lin, Guan-Yu
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Chen, Yaw-Chung
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資訊工程學系
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Department of Computer Science
Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW + s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by Class-Bench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.
Fast and Memory Efficient Conflict Detection for Multidimensional Packet Filters
(2012)
Lee, Chun-Liang
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Lin, Guan-Yu
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Chen, Yaw-Chung
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資訊工程學系
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Department of Computer Science
Packet classification plays an important role in supporting advanced network services such as Virtual Private Networks (VPNs), quality-of-service (QoS), and policy-based routing. Routers classify incoming packets into different categories according to pre-defined rules, which are called packet filters. If two or more filters overlap, a conflict may occur and leads to ambiguity in packet classification. In this paper, we propose an algorithm which can efficiently detect and resolve filter conflicts. The proposed algorithm can handle filters with more than two fields, which is more general than algorithms designed for two-dimensional filters. We use the synthetic filter databases generated by Class Bench to evaluate the proposed algorithm. Compared with the bit-vector algorithm, simulation results show that the proposed algorithm can reduce the detection times by over 84% for 10 out of 12 filter databases, and only uses less than 26% of memory space.
Impact of Vehicular Networks on Emergency Medical Services in Urban Areas
(2014-11-01)
Lee, Chun-Liang
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Huang, Chung-Yuan
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Hsiao, Tzu-Chien
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Wu, Chun-Yen
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Chen, Yaw-Chung
The speed with which emergency personnel can provide emergency treatment is crucial to reducing death and disability among acute and critically ill patients. Unfortunately, the rapid development of cities and increased numbers of vehicles are preventing emergency vehicles from easily reaching locations where they are needed. A significant number of researchers are experimenting with vehicular networks to address this issue, but in most studies the focus has been on communication technologies and protocols, with few efforts to assess how network applications actually support emergency medical care. Our motivation was to search the literature for suggested methods for assisting emergency vehicles, and to use simulations to evaluate them. Our results and evidence-based studies were cross-referenced to assess each method in terms of cumulative survival ratio (CSR) gains for acute and critically ill patients. Simulation results indicate that traffic light preemption resulted in significant CSR increases of between 32.4% and 90.2%. Route guidance was found to increase CSRs from 14.1% to 57.8%, while path clearing increased CSRs by 15.5% or less. It is our hope that this data will support the efforts of emergency medical technicians, traffic managers, and policy makers.