• Convergence Security Journal
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• v.21 no.3
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• pp.13-23
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• 2021

The attacker's techniques and tools are becoming intelligent and sophisticated. Existing Anti-Virus cannot prevent security accident. So the security threats on the endpoint should also be considered. Recently, EDR security solutions to protect endpoints have emerged, but they focus on visibility. There is still a lack of detection and responsiveness. In this paper, we use real-world EDR event logs to aggregate knowledge-based MITRE ATT&CK and autoencoder-based anomaly detection techniques to detect anomalies in order to screen effective analysis and analysis targets from a security manager perspective. After that, detected anomaly attack signs show the security manager an alarm along with log information and can be connected to legacy systems. The experiment detected EDR event logs for 5 days, and verified them with hybrid analysis search. Therefore, it is expected to produce results on when, which IPs and processes is suspected based on the EDR event log and create a secure endpoint environment through measures on the suspicious IP/Process.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.923-929
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• 2019

The stability of the propulsion system is crucial for the autonomous vessel. Multiple power generation and propulsion systems should be provided for the stability of the propulsion system. High power generation capacity is calculated for stability, resulting in economical decline due to low load operation. To solve this problem, we need to optimize the power system. In this paper, an OPMS for electric propulsion ship is constructed. The OPMS consists of a hybrid power generation system, an energy storage system, and a control load system. The power generation system consists of a dual fuel engine, the energy storage system is a battery, and the control load system consists of the propulsion load, continuous load, intermittent load, cargo part load and deck machine load. The power system was constructed by modeling the characteristics of each system. For the experiment, a scenario based on ship operation was prepared and the stability and economical efficiency were compared with existing electric propulsion ships.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.711-732
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• 2019

Many unmanned aerial vehicle (UAV) applications have been employed for performing data collection in facilitating tasks such as surveillance and monitoring objectives in remote and dangerous environments. In light of the fact that most of the existing UAV relaying applications operate in conventional half-duplex (HD) mode, a full-duplex (FD) based UAV relay aided wireless network is investigated, in which the UAV relay helps forwarding information from the source (S) node to the destination (D). Since the activated UAV relays are always floating and flying in the air, its channel state information (CSI) as well as channel capacity is a time-variant parameter. Considering decode-and-forward (DF) relaying protocol in UAV relays, the cooperative relaying channel capacity is constrained by the relatively weaker one (i.e. in terms of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR)) between S-to-relay and relay-to-D links. The channel capacity can be optimized by adaptively optimizing the transmit power of S and/or UAV relay. Furthermore, a hybrid HD/FD mode is enabled in the proposed UAV relays for adaptively optimizing the channel utilization subject to the instantaneous CSI and/or remaining self-interference (SI) levels. Numerical results show that the channel capacity of the proposed UAV relay aided wireless networks can be maximized by adaptively responding to the influence of various real-time factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Korean Journal of Metals and Materials
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• v.56 no.12
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• pp.910-914
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• 2018

Since catalyst technology is one of the promising technologies to improve the working performance of next generation energy and electronic devices, many efforts have been made to develop various catalysts with high efficiency at a low cost. However, there are remaining challenges to be resolved in order to use the suggested catalytic materials, such as platinum (Pt), gold (Au), and palladium (Pd), due to their poor cost-effectiveness for device applications. In this study, to overcome these challenges, we suggest a useful method to increase the surface area of a noble metal catalyst material, resulting in a reduction of the total amount of catalyst usage. By employing block copolymer (BCP) self-assembly and nano-transfer printing (n-TP) processes, we successfully fabricated sub-20 nm Pt line and cross-bar patterns. Furthermore, we obtained a highly ordered Pt cross-bar pattern on a Ni thin film and a Pt-embedded Ni thin film, which can be used as hetero hybrid alloy catalyst structure. For a detailed analysis of the hybrid catalytic material, we used scanning electron microscope (SEM), transmission electron microscope (TEM) and energy-dispersive X-ray spectroscopy (EDS), which revealed a well-defined nanoporous Pt nanostructure on the Ni thin film. Based on these results, we expect that the successful hybridization of various catalytic nanostructures can be extended to other material systems and devices in the near future.

• Tribology and Lubricants
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• v.37 no.3
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• pp.99-105
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• 2021

In this study, for a high-pressure turbine (HPT) of 800 MW class supercritical thermal-power plant, considering aerodynamic cross-coupling, we performed a rotordynamic logarithmic decrement (LogDec) stability analysis with various tilting pad journal bearing (TPJB) designs, which several steam turbine OEMs (original equipment manufacturers) currently apply in their supercritical and ultra-supercritical HPTs. We considered the following TPJB designs: 6-Pad load on pad (LOP)/load between pad (LBP), 5-Pad LOP/LBP, Hybrid 3-Pad LOP (lower 3-Pad tilting and upper 1-Pad fixed), and 5-Pad LBPs with the design variables of offset and preload. We used the API Level-I method for a LogDec stability analysis. Following results are summarized only in a standpoint of LogDec stability. The Hybrid 3-Pad LOP TPJBs most excellently outperform all the other TPJBs over nearly a full range of cross-coupled stiffness. In a high range of cross-coupled stiffness, both the 6-Pad LOP and 5-Pad LOP TPJBs may be recommended as a practical conservative bearing design approach for enhancing a rotordynamic stability of the HPT. As expected, in a high range of cross-coupled stiffness, the 6-Pad LBP TPJBs exhibit a better performance than the 5-Pad LBP TPJBs. However, contrary to one's expectation, notably, the 5-Pad LOP TPJBs exhibit a slightly better performance than the 6-Pad LOP TPJBs. Furthermore, we do not recommend any TPJB design efforts of either increasing a pad offset from 0.5 or a pad preload from 0 for the HPT in a standpoint of stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1051-1077
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• 2021

The secure communication of information is a major concern over the internet. The information must be protected before transmitting over a communication channel to avoid security violations. In this paper, a new hybrid method called compressed encrypted data embedding (CEDE) is proposed. In CEDE, the secret information is first compressed with Lempel Ziv Welch (LZW) compression algorithm. Then, the compressed secret information is encrypted using the Advanced Encryption Standard (AES) symmetric block cipher. In the last step, the encrypted information is embedded into an image of size 512 × 512 pixels by using image steganography. In the steganographic technique, the compressed and encrypted secret data bits are divided into pairs of two bits and pixels of the cover image are also arranged in four pairs. The four pairs of secret data are compared with the respective four pairs of each cover pixel which leads to sixteen possibilities of matching in between secret data pairs and pairs of cover pixels. The least significant bits (LSBs) of current and imminent pixels are modified according to the matching case number. The proposed technique provides double-folded security and the results show that stego image carries a high capacity of secret data with adequate peak signal to noise ratio (PSNR) and lower mean square error (MSE) when compared with existing methods in the literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.