• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.351-359
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• 2009

Recently with increase of data in the Internet, platform technologies that can process huge data effectively such as Google platform and Hadoop are regarded as worthy of notice. In this kind of platform, there exist network I/O overheads to send task outputs due to the MapReduce operation which is a programming model to support parallel computation in the large cluster system. In this paper, we suggest applying of TIPC (Transparent Inter-Process Communication) protocol for reducing network I/O overheads and increasing network performance in the distributed computing environments. TIPC has a lightweight protocol stack and it spends relatively less CPU time than TCP because of its simple connection establishment and logical addressing. In this paper, we analyze main features of the Hadoop-based distributed computing system, and we build an experimental model which can be used for experiments to compare the performance of various protocols. In the experimental result, TIPC has a higher bandwidth and lower CPU overheads than other protocols.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.13-20
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• 2009

Recently, as traffic flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems (IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network traffic. In this paper we propose an SNMP-based lightweight and fast detection algorithm for traffic flooding attacks, which minimizes the processing and network overhead of the detection system, minimizes the detection time, and provides high detection rate. The attack detection algorithm consists of three consecutive stages. The first stage determines the detection timing using the update interval of SNMP MIB. The second stage analyzes attack symptoms based on correlations of MIB data. The third stage determines whether an attack occurs or not and figure out the attack type in case of attack.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1873-1893
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• 2023

The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2605-2625
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• 2024

Siamese-based segmentation and tracking algorithms improve accuracy and stability for video object segmentation and tracking tasks simultaneously. Although effective, variability in target appearance and background clutter can still affect segmentation accuracy and further influence the performance of tracking. In this paper, we present a memory propagation-based target-aware and mask-attention decision network for robust object segmentation and tracking. Firstly, a mask propagation-based attention module (MPAM) is constructed to explore the inherent correlation among image frames, which can mine mask information of the historical frames. By retrieving a memory bank (MB) that stores features and binary masks of historical frames, target attention maps are generated to highlight the target region on backbone features, thus suppressing the adverse effects of background clutter. Secondly, an attention refinement pathway (ARP) is designed to further refine the segmentation profile in the process of mask generation. A lightweight attention mechanism is introduced to calculate the weight of low-level features, paying more attention to low-level features sensitive to edge detail so as to obtain segmentation results. Finally, a mask fusion mechanism (MFM) is proposed to enhance the accuracy of the mask. By utilizing a mask quality assessment decision network, the corresponding quality scores of the "initial mask" and the "previous mask" can be obtained adaptively, thus achieving the assignment of weights and the fusion of masks. Therefore, the final mask enjoys higher accuracy and stability. Experimental results on multiple benchmarks demonstrate that our algorithm performs outstanding performance in a variety of challenging tracking tasks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1320-1326
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• 2017

Recently, IoT has been studying a lightweight protocol to satisfy device communication in a limited network environment. MQTT is a typical lightweight protocol. It supports small fixed headers to minimize overhead, and adopts publish/subscribe structure to guarantee real-time performance. However, MQTT does not support prioritization of important data and can not provide QoS in a specific IoT service. In this paper, we propose a message processing method to consider the priority of various IoT services in MQTT. In the proposed method, the priority flag is added to the fixed header of the MQTT in the node to transmit the message, and the broker confirms the priority of the corresponding message and processes it preferentially. Through experiment and evaluation, we confirmed the reduction of end-to-end delay between nodes according to priority.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.237-247
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• 2005

Ubiquitous Sensor Network(USN) is the very core of a technology for the Ubiquitous environments. There is the weakness from various security attacks such that tapping of sensor informations, flowing of abnormal packets, data modification and Denial of Service(DoS) etc. And it's required counterplan with them. Especially it's restricted by the capacity of battery and computing. By reasons of theses. positively, USN security technology needs the lightweighted design for the low electric energy and the minimum computing. In this paper, we propose lightweight USN mutual authentication methology based on trust model to solve above problems. The proposed authentication model can minimize the measure of computing because it authenticates the sensor nodes based on trust information represented by subjective logic model. So it can economize battery consumption and resultingly increse the lifetime of sensor nodes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.707-714
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• 2013

IoT, which can be regarded as an enhanced version of M2M communication technology, was proposed to realize intelligent thing to thing communications by utilizing Internet connectivity. Things in IoT are generally heterogeneous and resource constrained. Also such things are connected with each other over LLN(low power and lossy Network). Confidentiality, mutual authentication and message origin authentication are required to make a secure service in IoT. Security protocols used in traditional IP Networks cannot be directly adopted to resource constrained devices in IoT. Under the respect, a IETF standard group proposes to use lightweight version of DTLS protocol for supporting security services in IoT environments. However, the protocol can not cover up all of very constrained devices. To solve the problem, we propose a scheme which tends to support mutual authentication and session key agreement between devices that contain only a single crypto primitive module such as hash function or cipher function because of resource constrained property. The proposed scheme enhances performance by pre-computing a session key and is able to defend various attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4727-4741
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• 2019

The lightweight block cipher Piccolo adopts Generalized Feistel Network structure with 64 bits of block size. Its key supports 80 bits or 128 bits, expressed by Piccolo-80 or Piccolo-128, respectively. In this paper, we exploit the security of reduced version of Piccolo from the first round with the pre-whitening layer, which shows the vulnerability of original Piccolo. As a matter of fact, we first study some linear relations among the round subkeys and the properties of linear layer. Based on them, we evaluate the security of Piccolo-80/128 against the meet-in-the-middle attack. Finally, we attack 13 rounds of Piccolo-80 by applying a 5-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{67.39}$ encryptions and $2^{64.91}$ blocks, respectively. Moreover, we also attack 17 rounds of Piccolo-128 by using a 7-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{126}$ encryptions and $2^{125.49}$ blocks, respectively. Compared with the previous cryptanalytic results, our results are the currently best ones if considering Piccolo from the first round with the pre-whitening layer.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.93-98
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• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.