• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1643-1660
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• 2016

Connectivity and trust in social networks have been exploited to propose applications on top of these networks, including routing, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-enabled routing applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance of such applications, including random walk-based routing, shortest path based routing, breadth-first-search based routing, and Dijkstra routing. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1665-1675
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• 2007

This paper presents the problem of controlling asynchronous sequential machines in the presence of input disturbances, which may be also regarded as an adversary in a game theoretic setting. The main objective is to provide necessary and sufficient condition for the existence of a corrective controller that solves model matching problem of an asynchronous machine suffering from input disturbance. The existence condition can be stated in terms of a simple comparison of two skeleton matrices. The proposed controller eliminates the adversarial effect of input disturbance and makes the controlled machine mimic the behavior of a model in stable-state way. Whenever controller exists, algorithms for their design are outlined and demonstrated in a case study.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.592-593
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• 2022

다중 데이터 기반의 행동 인식 과정에서 데이터 수집 반경이 비교적 제한되는 영상 데이터의 결측에 대한 보완이 요구된다. 본 논문에서는 6축 센서 데이터를 이용하여 결측된 영상 데이터를 생성함으로써 행동 인식의 성능을 개선하는 방법을 제안한다. 가속도와 자이로 센서로부터 수집된 행동 데이터를 이용하여 GAN(Generative Adversarial Network)을 통해 영상에서의 관절(Skeleton) 움직임에 대한 데이터를 생성하고자 한다. 이를 위해 DeepLabCut 기반 모델 학습을 통해 관절 좌표를 추출하며, 전처리된 센서 시퀀스 데이터를 가지고 GRU 기반 GAN 모델을 통해 관절 좌표에 대한 영상 시퀀스 데이터를 생성한다. 생성된 영상 시퀀스 데이터는 영상 데이터의 결측이 발생했을 때 대신 행동 인식 모델의 입력값으로 활용될 수 있어 성능 향상을 기대할 수 있다.

• Cartoon and Animation Studies
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• s.39
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• pp.233-259
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• 2015

Comics is a medium that characters lead a story as words are combined with pictures. Despite characters are simple and exaggerated fictive ones, readers empathize with personality of the character through outer appearance of face and clothes, expression, behavior lines etc. Personality of a character is established by the author or story writer of the comics. Basically, established personality must be maintained through the comics without being broken. To establish personality of a character, understanding of human personality is important. One method for the foregoing is to refer to the framework used for analyzing human personality. As for personality type by blood type, it is considered that there is a specific type of personality according to blood type, and human personality is analyzed by four blood types of A, B, AB and O. Personality type by blood type is convenient to access in the way that it is easy and simple without complexity. The process of establishing personality of a character may be easier by referring to the personality according to blood type. Kishimoto Masashi, the author of the comics is establishing blood types for each character. This study aims to analyze personality depending on the blood types and relations according to blood types among of main and surrounding characters appearing in . As for research methods, this study, first, analyzed how blood type is related to personality in 'the relation between blood type and personality'. Second, this study analyzed blood type and personality in 'personality by blood type of characters in comics '. Third, in the 'personality and relations according to the blood types of characters in the comics ', this study analyzed personality and relations according to the blood type by using a 'caring relationship' which is a like-minded relation according to the blood type. Relations that this study analyzed include '1) Relation between team manager and team member in a team Naruto is belonging', '2) Naruto's eternal triangle and family relations', '3) Naruto's teacher-pupil relations and team manager relation', '4) Main rivalries in ' and '5) change in Naruto and Sasuke's relationship from adversarial to cooperator relationship'. From the relations according to blood types between personality and character established by the author, it is possible to infer that the author referred to the personality of each blood type. Like this, establishing personality of characters and making relations among characters based on the personality type by blood type may be one of useful methods for creating a story.