• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3511-3532
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• 2019

Coverage-guided fuzzing is an efficient solution that has been widely used in software testing. By guiding fuzzers through the coverage information, seeds that generate new paths will be retained to continually increase the coverage. However, we observed that most samples follow the same few high-frequency paths. The seeds that exercise a high-frequency path are saved for the subsequent mutation process until the user terminates the test process, which directly affects the efficiency with which the low-frequency paths are tested. In this paper, we propose a fuzzing solution, ER-Fuzz, that truncates the recording of a high-frequency path to influence coverage. It utilizes a deep learning-based classifier to locate the high and low-frequency path transfer points; then, it instruments at the transfer position to promote the probability low-frequency transfer paths while eliminating subsequent variations of the high-frequency path seeds. We implemented a prototype of ER-Fuzz based on the popular fuzzer AFL and evaluated it on several applications. The experimental results show that ER-Fuzz improves the coverage of the original AFL method to different degrees. In terms of the number of crash discoveries, in the best case, ER-Fuzz found 115% more unique crashes than did AFL. In total, seven new bugs were found and new CVEs were assigned.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.306-309
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• 2019

The damage to farms due to wild animals such as wild boars and elks increases every year, but, in the current system, the catchers from government hunt animals by using guns at night as making an effort to detect wild animals personally by using flashlights. This is very time-inefficient and immediate follow-up action on being damaged is not possible. In this paper, we introduce a system which can detect and recognize the wild animals or the people with high accuracy using thermal imaging camera and infrared camera in company with deep learning technology, so that could kick out or catch the wild animals more quickly than current system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.119-127
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• 2023

Recently, deep learning has been used in the development of various institutional devices and services to help the visually impaired people in their daily lives. This is because not only are there few products and facility guides written in braille, but less than 10% of the visually impaired can use braille. In this paper, we propose a system that recognizes beverage cans in real time and outputs the beverage can name with sound for the convenience of the visually impaired. Five commercially available beverage cans were selected, and a CNN model and a YOLO model were designed to recognize the beverage cans. After augmenting the image data, model training was performed. The accuracy of the proposed CNN model and YOLO model is 91.2% and 90.8%, respectively. For practical verification, a system was built by attaching a camera and speaker to a Raspberry Pi. In the system, the YOLO model was applied. It was confirmed that beverage cans were recognized and output as sound in real time in various environments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.27-36
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• 2024

Accidents or disease can lead to acquired voice dysphonia. In this case, we propose a new input interface based on eye movements to facilitate communication for patients. Unlike the existing method that presents the English alphabet as it is, we reorganized the layout of the alphabet to support the Korean alphabet and designed it so that patients can enter words by themselves using only eye movements, gaze, and blinking. The proposed interface not only reduces fatigue by minimizing eye movements, but also allows for easy and quick input through an intuitive arrangement. For natural communication, we also implemented a system that allows patients who are unable to speak to communicate with their own voice. The system works by tracking eye movements to record what the patient is trying to say, then using Glow-TTS and Multi-band MelGAN to reconstruct their own voice using the learned voice to output sound.

• Journal of IKEEE
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• v.28 no.3
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• pp.397-404
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• 2024

This paper presents the design and implementation results of a Raspberry-Pi-based embedded system with an FPGA accelerator that can detect and classify objects using an FMCW radar sensor for preventing door collision accidents in vehicles. The proposed system performs a radar sensor signal processing and a deep learning processing that classifies objects into bicycles, automobiles, and pedestrians. Since the CNN algorithm requires substantial computation and memory, it is not suitable for embedded systems. To address this, we implemented a lightweight deep learning model, BNN, optimized for embedded systems on an FPGA, and verified the results achieving a classification accuracy of 90.33% and an execution time of 20ms.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.165-172
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• 2019

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.109-120
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• 2023

With the increasing interest in blockchain technology and its employment in diverse sectors and industries, including: finance, business, voting, industrial and many other medical and educational applications. Recently, the blockchain technology has played significant role in preventing fraud transactions in accounting systems, as the blockchain offers high security measurements, reduces the need for centralized processing, and blocks access to the organization information and system. Therefore, this paper studies, analyses, and investigates the adoption of blockchain technology with accounting systems, through analyzing the results of several research works which have employed the blockchain technology to secure their accounting systems. In addition, we investigate the performance of applying the deep learning and machine learning approaches for the purpose of fraud detection and classification. As a result of this study, the adoption of blockchain technology will enhance the safety and security of accounting systems, through identifying and classifying the possible frauds that may attack the accounting and business organizations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• Journal of KIBIM
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• v.13 no.2
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• pp.29-36
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• 2023

This study explores the potential of utilizing video-based data analysis and machine learning techniques to estimate the number of occupants within a building. The research methodology involves developing a sophisticated counting system capable of detecting and tracking individuals' entry and exit patterns. The proposed method demonstrates promising results in various scenarios; however, it also identifies the need for improvements in camera performance and external environmental conditions, such as lighting. The study emphasizes the significance of incorporating machine learning in architectural and urban planning applications, offering valuable insights for the field. In conclusion, the research calls for further investigation to address the limitations and enhance the system's accuracy, ultimately contributing to the development of a more robust and reliable solution for building occupancy estimation.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.