• International journal of advanced smart convergence
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• v.5 no.3
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• pp.47-55
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• 2016

Router main task is to provide routing of Internet Protocol (IP) packets. Routing is achieved with help of the IP lookup. Router stores information about the networks and interfaces in data structures commonly called as routing tables. Comparison of IP from incoming packet with the IPs stored in routing table for the information about route is IP Lookup. IP lookup performs by longest IP prefix matching. The performance of the IP router is based on the speed of prefix matching. IP lookup is a major bottle neck in the performance of Router. Various algorithms and data structures are available for IP lookup. This paper is about reviewing various tree based structure and its performance evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3804-3819
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• 2018

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1754-1766
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• 2015

Packet classification carried out in Internet routers is one of the challenging tasks, because it has to be performed at wire-speed using five header fields at the same time. In this paper, we propose a leaf-pushed AQT bitmap trie. The proposed architecture applies the leaf-pushing to an area-based quad-trie (AQT) to reduce unnecessary off-chip memory accesses. The proposed architecture also applies a bitmap trie, which is a kind of multi-bit tries, to improve search performance and scalability. For performance evaluation, simulations are conducted by using rule sets ACL, FW, and IPC, with the sizes of 1k, 5k, and 10k. Simulation results show that the number of off-chip memory accesses is less than one regardless of set types or set sizes. Additionally, since the proposed architecture applies a bitmap trie, the required number of on-chip memory accesses is the 50% of the leaf-pushed AQT trie. In addition, our proposed architecture shows good scalability in the required on-chip memory size, where the scalability is identified by the stable change in the required memory sizes, as the size of rule sets increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1164-1170
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• 2002

In the ACE2000 MPLS system, MPLS Interface Module(MIM) is composed of an ATM Interface Module and a HFMA performing a packet forwarding. In the MIM, the HFMA RSAR receive cells from the Physical layer and reassemble the cells. And the IP Lookup controller perform a packet forwarding after packet classification. Forwarded packet is segmented into cells in the HFMA TSAR and transfer to the ALMA for the transmission to an ATM cell switch. When the MIM make use of an ATM Interface Module, it directly connect the ALMA with a PHY layer using the UTOPIA Level2 interface. Then, an ALMA performs Master Mode. Also, the HFMA TSAR performs the Master Mode in the MIM. Therefore, the UTOPIA-L2 Controller of the Slave Mode require for interfacing between an ALMA and a HFHA TSAR. In this paper, we implement the architecture and cell control mechanism for the UTOPIA-L2 Controller supporting Multi-ports.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.393-393
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• 2004

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.16-18
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• 2018

최근 네트워크 상에 새롭고 다양한 어플리케이션들이 생겨나면서 이에 따른 적절한 어플리케이션별 서비스 제공을 위한 패킷 분류 방법이 요구되고 있다. 이로 인하여 딥 러닝 기술이 발전 하면서 이를 이용한 네트워크 트래픽 분류 방법들이 제안되고 있다. 따라서, 본 논문에서는 딥 러닝 기술 중 Convolution Neural Network 와 Recurrent Neural Network 를 동시에 활용한 네트워크 패킷 분류 방법을 제안한다.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.1
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• pp.75-82
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• 2003

Computer and network security has recently become a popular subject due to the explosive growth of the Internet Especially, attacks based on malformed packet are difficult to detect because these attacks use the skill of bypassing the intrusion detection system and Firewall. This paper designs and implements a network-based intrusion detection system (NIDS) which detects intrusions with malformed-packets in real-time. First, signatures, rules in NIDS like Snouts rule files, are classified using similar properties between signatures NIDS creates a rule tree applying hashing technique based on the classification. As a result the system can efficiently perform intrusion detection.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.205-214
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• 2010

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic classification becomes important for the effective use of network resources. In this paper, we present an application traffic classification method based on fixed IP-port information. A fixed IP-port is a {IP address, port number, transport protocol}triple dedicated to only one application, which is automatically collected from the behavior analysis of individual applications. We can classify the Internet traffic more accurately and quickly by simple packet header matching to the collected fixed IP-port information. Therefore, we can construct a lightweight, fast, and accurate real-time traffic classification system than other classification method. In this paper we propose a novel algorithm to extract the fixed IP-port information and the system architecture. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.1-8
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• 2015

Security has become an issue due to the rapid increases in internet traffic data network. Especially P2P traffic data poses a great challenge to network systems administrators. Preemptive measures are necessary for network quality of service(QoS) and efficient resource management like blocking suspicious traffic data. Deep packet inspection(DPI) is the most exact way to detect an intrusion but it may pose a private security problem that requires time. We used several machine learning methods to compare the performance in classifying network traffic data accurately over time. The Random Forest method shows an excellent performance in both accuracy and time.