Vol. 51, No. 11,
Nov. 2024
Digital Library
Enhancing Container Security Using Machine Learning Based on Kernel Tracing Logs
Hyeonseok Shin, Minjung Jo, Hosang Yoo, Yongwon Lee, Jiyeon Lee, Byungchul Tak
http://doi.org/10.5626/JOK.2024.51.11.947
The use of container technology has been rapidly increasing as it gains attention in cloud environments. Containers are lighter and more advantageous for deployment than virtual machines because they do not require a separate operating system. However, containers can have security vulnerabilities due to their characteristic of sharing the same host kernel. In this paper, we designed and implemented a security system to address these vulnerabilities by using eBPF technology, kernel tracing logs, and an ensemble machine learning model. Our system can effectively detect attacks leveraging race conditions and the heap spray technique used in kernel memory vulnerabilities. Unlike traditional security policy-based approaches, it allows for rapid and dynamic responses without needing profile creation. For detecting attacks leveraging race conditions, the system achieved over 99% accuracy in Precision, Recall, and F1-Score, while it recorded over 97% accuracy across all metrics for heap spray detection.
Optimizing Throughput Prediction Models Based on Feature Category Contribution in 4G/5G Network Environments
http://doi.org/10.5626/JOK.2024.51.11.961
The acceleration in 5G technology adoption due to increased network data consumption and limitations of 4G has led to the establishment of a heterogeneous network environment comprising both 4G and limited 5G. Consequently, this highlights the importance of throughput prediction for network service quality (QoS) and resource optimization. Traditional throughput prediction research mainly relies on the use of single attributes or extraction of attributes through correlation analysis. However, these approaches have limitations, including potential exclusion of variables with nonlinear relationships with arbitrariness and inconsistency of correlation coefficient thresholds. To overcome these limitations, this paper proposed a new approach based on Feature Importance. This method could calculate the relative importance of features used in the network and assign contribution scores to attribute categories. By utilizing these scores, throughput prediction was enhanced. This approach was applied and tested on four open network datasets. Experiments demonstrated that the proposed method successfully derived an optimal category combination for throughput prediction, reduced model complexity, and improved prediction accuracy compared to using all categories.
Effective Embedding Techniques for Misbehavior Classification in Vehicular Ad-Hoc Networks
http://doi.org/10.5626/JOK.2024.51.11.970
Vehicular Ad-Hoc Networks (VANET) is a network technology enabling data transmission between vehicles that includes V2X communication, which facilitates the exchange of both external and internal vehicle information based on communication between vehicles, infrastructure, and pedestrians. However, broadcasting data containing faults or attack information within the network can lead to critical issues, making Misbehavior Detection (MBD) systems an essential technology in VANET. While recent studies have increasingly employed machine learning for MBD, the patterns of misbehavior types in VANET often resemble normal behavior, posing challenges for comprehensive and accurate classification. Existing research has suggested a hierarchical classification system to categorize misbehaviors based on different types of attacks and faults. This study proposed an embedding representation method for constructing a hierarchical classification system to improve the accuracy of misbehavior classification models. By extracting embedding vectors for multivariate time-series data through a pre-trained LSTM model, this study compressed core data related to misbehavior types and employed hierarchical clustering to group various attack types into broader categories.
Semi-Supervised Object Detection for Small Imbalanced Drama Dataset
http://doi.org/10.5626/JOK.2024.51.11.978
Images of the theme of a drama are typically zoomed-in mainly to people. As a result, people-oriented images are predominant in drama data, and class imbalance naturally occurs. This paper addresses the issue of class imbalance in drama data for object detection tasks and proposes various sampling methods to tackle this challenge within the framework of semi-supervised learning. Experimental evaluations demonstrated that the suggested semi-supervised learning approach with specialized sampling methods outperformed traditional supervised and semi-supervised methods. This study underscores the significance of selecting appropriate training data and sampling methods to optimize object detection performance in specialized datasets with unique characteristics.
Lightweight Temporal Segment Network for Video Scene Understanding: Validation in Driver Assault Detection
Juneyong Lee, Joon Kim, Junhui Park, Jongho Jo, Ikbeom Jang
http://doi.org/10.5626/JOK.2024.51.11.987
"The number of driver assaults in transportation such as taxis and buses has been increasing over the past few years. It can be especially difficult to respond quickly to assaults on drivers by drunks late at night. To address this issue, our research team proposed a lightweight CNN-based Temporal Segment Network (TSN) model that could detect driver assaults by passengers in real time. The TSN model efficiently processes videos by sampling a small number of image frames and divides videos into two streams for learning: one for spatial information processing and the other for temporal information processing. Convolutional neural networks are employed in each stream. In this research, we applied a lightweight CNN architecture, MobileOne, significantly reducing the model size while demonstrating improved accuracy even with limited computing resources. The model is expected to contribute to rapid response and prevention of hazardous situations for drivers when it is integrated into vehicular driver monitoring systems."
Dynamic Unit State Data-Driven False Alarm Filtering for Regression Unit Testing
Youngseok Choi, Ahcheong Lee, Hyoju Nam, Insub Lee, Namhoom Jung, Kyutae Cho, Moonzoo Kim
http://doi.org/10.5626/JOK.2024.51.11.996
Regression testing focuses on testing changed parts of software to quickly find errors caused by changes. Unit testing individually tests each unit (i.e., a small component of software) to identify a bug quickly. We propose a new regression testing technique using unit testing with a dynamic unit state-based false alarm reduction model. Experimental results showed that when the proposed technique was applied to 10 C programs, acc@10 performance increased by 40%p compared to the state-of-the-art technique foridentifying a buggy function. For 7 programs, target regression bugs were ranked within the top 20% of the bugs reported by the proposed technique.
Projection Model and Calibration Method for Multi-camera Systems with Ultra-wide Field-of-view Fisheye Lenses
http://doi.org/10.5626/JOK.2024.51.11.1005
Fisheye lenses with a wide field-of-view (FoV) are commonly utilized as sensors for mobile platforms such as cars and robots due to their capacity to recognize a large area at once. Algorithms that utilize multiple fisheye cameras to estimate distances and platform movements in all directions of 360 degrees have been developed and are presently deployed as robot sensors and for 3D modeling. In this paper, we present a novel projection model for fisheye lenses with an ultra-wide FoV exceeding 220 degrees. Next, we introduce a multi-camera system calibration method that can efficiently and accurately estimate the calibration of intrinsic and extrinsic parameters for a multi-camera system equipped with these lenses. The proposed methodology has successfully calibrated a range of systems, spanning from compact helmet-mounted capture devices to larger car-mounted systems. The experimental results show that the proposed method achieves sub-pixel calibration accuracy.
Topic-Aware Cross-Attention for Dialogue Summarization
http://doi.org/10.5626/JOK.2024.51.11.1011
Unlike general document summarization, dialogue summarization frequently involves informal and colloquial language. It requires an understanding of the context and flow of the dialogue. It also requires consideration of topics. This study proposes a Topic-Aware Cross-Attention mechanism that can incorporate elements to recognize topic distributions into a cross-attention mechanism to reflect characteristics of dialogue. This Topic-Aware Cross-Attention mechanism can extract topic distributions of dialogue and summary and apply the similarity of these distributions to the cross-attention mechanism within BART model’s decoder to perform dialogue summarization. The proposed Topic-Aware Cross-Attention mechanism can adjust application degree of topic distribution similarity to the cross-attention mechanism by modifying topic-ratio. Experimental results on DialogSum and SAMSum datasets demonstrated the suitability of the method for dialogue summarization.
Explainable Supporting Facts Generation via Query-Focused Multi-Document Summarization for Open Domain Question Answering Model
http://doi.org/10.5626/JOK.2024.51.11.1020
"Open domain question answering system requires external knowledge not satisfied by knowledge inherent in the language model to answer a given query. It is a technology that is being studied importantly for solving the hallucination problem that occurs in recent large language models. In this paper, we propose a model that utilizes structural information of Query-attentive Semantic Graph (QSG) to summarize information between distant documents based on a query and utilize it as supporting factors for a multi-document-based question answering system. Query-based supporting factors generated by summarizing can improve answer generation performance and show better explainability than extracted supporting factors."
Improving Conversational Query Rewriting through Generative Coreference Resolution
http://doi.org/10.5626/JOK.2024.51.11.1028
Conversational search enables retrieval of relevant passages for a current turn query by understanding the contextual meaning in a multi-turn dialogue. In conversational search, Conversational Query Reformulation enables utilization of off-the-shelf retrievers by transforming context-dependent queries into self-contained forms. Existing approaches primarily fine-tune pre-trained language models using human-rewritten queries as labels or prompt large language models (LLMs) to address ambiguity inherent in the current turn query, such as ellipsis and coreference. However, our preliminary experimental results indicate that existing models continue to face challenges with coreference resolution. This paper addresses two main research questions: 1) Can a model be trained to distinguish anaphoric mentions that need further clarification? And 2) Can a model be trained to clarify detected coreference mentions into more specified phrases? To investigate these questions, we devised two main components - the detector and the decoder. Our experiments demonstrated that our fine-tuned detector could identify diverse anaphoric phrases within questions, while our fine-tuned decoder could successfully clarify them, ultimately enabling effective coreference resolution for query rewriting. Therefore, we present a novel paradigm, Coreference Aware Conversational Query Reformulation, utilizing these main components.
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