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- [Research] [Prof. Koo,Hyung joon/ Prof. Hwang, sung jae] SecAI/SoftSec Lab, Approval for FSE2024 publication
- [Prof. Koo,Hyung joon/ Prof. Hwang, sung jae] SecAI/SoftSec Lab, Approval for ACM International Conference on the Foundations of Software Engineering (FSE 2024) publication Abstract. Decompilation is a process of converting a low-level machine code snippet back into a high-level programming language such as C. It serves as a basis to aid reverse engineers in comprehending the contextual semantics of the code. In this respect, commercial decompilers like Hex-Rays have made significant strides in improving the readability of decompiled code over time. While previous work has proposed the metrics for assessing the readability of source code, including identifiers, variable names, function names, and comments, those metrics are unsuitable for measuring the readability of decompiled code primarily due to i) the lack of rich semantic information in the source and ii) the presence of erroneous syntax or inappropriate expressions. In response, to the best of our knowledge, this work first introduces R2I, the Relative Readability Index, a specialized metric tailored to evaluate decompiled code in a relative context quantitatively. In essence, R2I can be computed by i) taking code snippets across different decompilers as input and ii) extracting pre-defined features from an abstract syntax tree. For the robustness of R2I, we thoroughly investigate the enhancement efforts made by existing decompilers and academic research to promote code readability, identifying 31 features to yield a reliable index collectively. Besides, we conducted a user survey to capture subjective factors such as one’s coding styles and preferences. Our empirical experiments demonstrate that R2I is a versatile metric capable of representing the relative quality of decompiled code (e.g., obfuscation, decompiler updates) and being well aligned with human perception in our survey.
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- 작성일 2024-01-25
- 조회수 277
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- [Research] [Prof. SHIN, DONG KUN] Intelligent Embedded Systems Lab, AAAI 2024 Conference Paper
- [Prof. SHIN, DONG KUN] Intelligent Embedded Systems Lab, AAAI 2024 Conference Paper [Paper #1 Information] Proxyformer: Nystrom-Based Linear Transformer with Trainable Proxy Tokens Sangho Lee, Hayun Lee, Dongkun Shin Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024 Transformer-based models have demonstrated remarkable performance in various domains, including natural language processing, image processing and generative modeling. The most significant contributor to the successful performance of Transformer models is the self-attention mechanism, which allows for a comprehensive understanding of the interactions between tokens in the input sequence. However, there is a well-known scalability issue, the quadratic dependency of self-attention operations on the input sequence length n, making the handling of lengthy sequences challenging. To address this limitation, there has been a surge of research on efficient transformers, aiming to alleviate the quadratic dependency on the input sequence length. Among these, the Nyströmformer, which utilizes the Nyström method to decompose the attention matrix, achieves superior performance in both accuracy and throughput. However, its landmark selection exhibits redundancy, and the model incurs computational overhead when calculating the pseudo-inverse matrix. We propose a novel Nyström method-based transformer, called Proxyformer. Unlike the traditional approach of selecting landmarks from input tokens, the Proxyformer utilizes trainable neural memory, called proxy tokens, for landmarks. By integrating contrastive learning, input injection, and a specialized dropout for the decomposed matrix, Proxyformer achieves top-tier performance for long sequence tasks in the Long Range Arena benchmark.
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- 작성일 2023-12-20
- 조회수 350
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- [Research] A paper from Prof. Jinkyu Lee’s Lab. (RTCL@SKKU) published in IEEE RTSS 2023
- A paper from RTCL@SKKU (Advisor: Jinkyu Lee) has been published in IEEE RTSS 2023. IEEE RTSS is the premier conference in real-time systems, in which around 30 papers are usually published every year. In this year, IEEE RTSS 2023 was held in Taipei, Taiwan. IEEE RTSS 2023 Website http://2023.rtss.org/ Real-Time Computing Lab. Website https://rtclskku.github.io/website/ (Paper Title) RT-Blockchain: Achieving Time-Predictable Transactions (Abstract) Although blockchain technology is being increasingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time payments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles spe- cialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transactions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open- source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks. Jinkyu Lee | jinkyu.lee@skku.edu | RTCL@SKKU | https://rtclskku.github.io/website/
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- 작성일 2023-12-11
- 조회수 281
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- [Research] DASH Lab, Three Papers are accepted for publication at CIKM 2023 International Conference and Hosting the First Interna
- DASH Lab’s three papers have been accepted for CIKM (Conference on Information and Knowledge Management) 2023, one of the top-tier international academic conferences in the artificial intelligence and information retrieval field. The papers will be presented in October. Authors are doctoral candidates in computer science and engineering, Eunju Park and Binh M. Le, along with master’s students, Beomsang Cho in computer science and engineering, Sangyoung Lee in artificial intelligence, Seungyeon Baek in artificial intelligence, Jiwon Kim in artificial intelligence. The papers are as follows: 1.Machine Unlearning Research 2.Research on Deepfakes in Collaboration with CSIRO’s Data61 in Australia 3.Research on Datasets for Online ID fraud detection Also, the 1st international workshop on anomaly and novelty detection in satellite and drones systems is hosted at CIKM 2023. The organizing committee consists of Simon S. Woo from Sungkyunkwan University, Shahroz Tariq from CSIRO’s Data61, Youjin Shin from Catholic University, Daewon Chung from Korea Aerospace Research Institute. This workshop is centered around anomaly detection in the time-series and vision data of satellite and drone systems. 1. Sanyong Lee and Simon Woo, “UNDO: Effective and Accurate Unlearning Method for Deep Neural Networks”, Proceedings of the 32nd ACM International Conference on Information & Knowledge Management. 2023. Machine learning has evolved through extensive data usage, including personal and private information. Regulations like GDPR highlight the "Right to be forgotten" for user and data privacy. Research in machine unlearning aims to remove specific data from pre-trained models. We introduce a novel two-step unlearning method, UNDO. First, we selectively disrupt the decision boundary of forgetting data at the coarse-grained level. However, this can also inadvertently affect the decision boundary of other remaining data, lowering the overall performance of the classification task. Hence, we subsequently repair and refine the decision boundary for each class at the fine-grained level by introducing a loss to maintain the overall performance while completely removing the class. Our approach is validated through experiments on two datasets, outperforming other methods in effectiveness and efficiency. 2. Beomsang Cho, Binh M. Le, Jiwon Kim, Simon S. Woo , Shahroz Tariq, Alsharif Abuadbba, and Kristen Moore , “Toward Understanding of Deepfake Videos in the Wild”, Proceedings of the 32nd ACM International Conference on Information & Knowledge Management. 2023. Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing datasets suffer from significant drawbacks that hamper their effectiveness. Notably, these datasets fail to encompass the latest deepfake videos produced by state-of-the-art methods that are being shared across various platforms. This limitation impedes the ability to keep pace with the rapid evolution of generative AI techniques employed in real-world deepfake production. Our contributions in this IRB-approved study are to bridge this knowledge gap from current real-world deepfakes by providing in-depth analysis. We first present the largest and most diverse and recent deepfake dataset (RWDF-23) collected from the wild to date, consisting of 2,000 deepfake videos collected from 4 platforms targeting 4 different languages span created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding the dataset’s scope beyond the previous research, we capture a broader range of real-world deepfake content, reflecting the ever-evolving landscape of online platforms. Also, we conduct a comprehensive analysis encompassing various aspects of deepfakes, including creators, manipulation strategies, purposes, and real-world content production methods. This allows us to gain valuable insights into the nuances and characteristics of deepfakes in different contexts. Lastly, in addition to the video content, we also collect viewer comments and interactions, enabling us to explore the engagements of internet users with deepfake content. By considering this rich contextual information, we aim to provide a holistic understanding of the evolving deepfake phenomenon and its impact on online platforms. 3. Eun-Ju Park, Seung-Yeon Back, Jeongho Kim, and Simon S. Woo, ”KID34K: A Dataset for Online Identity Card Fraud Detection”, Proceedings of the 32nd ACM International Conference on Information & Knowledge Management. 2023. Though digital financial systems have provided users with convenient and accessible services, such as supporting banking or payment services anywhere, it is necessary to have robust security to protect against identity misuse. Thus, online digital identity (ID) verification plays a crucial role in securing financial services on mobile platforms. One of the most widely employed techniques for digital ID verification is that mobile applications request users to take and upload a picture of their own ID cards. However, this approach has vulnerabilities where someone takes pictures of the ID cards belonging to another person displayed on a screen, or printed on paper to be verified as the ID card owner. To mitigate the risks associated with fraudulent ID card verification, we present a novel dataset for classifying cases where the ID card images that users upload to the verification system are genuine or digitally represented. Our dataset is replicas designed to resemble real ID cards, making it available while avoiding privacy issues. Through extensive experiments, we demonstrate that our dataset is effective for detecting digitally represented ID card images, not only in our replica dataset but also in the dataset consisting of real ID cards. 4. The 1st International Workshop on Anomaly and Novelty detection in Satellite and Drones systems (ANSD '23) The workshop on Anomaly and Novelty Detection in Drones and Satellite data at CIKM 2023 aims to bring together researchers, practitioners, and industry experts to discuss the latest advancements and challenges in detecting anomalies and novelties in drone and satellite data. With the increasing availability of such data, the workshop seeks to explore the potential of machine learning and data mining techniques to enable the timely and accurate detection of unexpected events or changes. The workshop will include presentations of research papers, keynote talks, panel discussions, and poster sessions, with a focus on promoting interdisciplinary collaboration and fostering new ideas for tackling real-world problems. Should you have questions, please ask professor Simon S. Woo(swoo@g.skku.edu) in DASH Lab(https://dash.skku.edu).
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- 작성일 2023-09-18
- 조회수 550
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- [Research] [Prof. Hyoungshick Kim] Security Laboratory, Approval for UbiComp 2023 publication
- [Prof. Hyoungshick Kim] Security Laboratory, Approval for UbiComp 2023 publication [Thesis title] On the Long-Term Effects of Continuous Keystroke Authentication: Keeping User Frustration Low through Behavior Adaptation. One of the main challenges in deploying a keystroke dynamics-based continuous authentication scheme on smartphones is ensuring low error rates over time. Unstable false rejection rates (FRRs) would lead to frequent phone locks during long-term use, and deteriorating attack detection rates would jeopardize its security benefits. The fact that it is undesirable to train complex deep learning models directly on smartphones or send private sensor data to servers for training present unique deployment constraints, requiring on-device solutions that can be trained fully on smartphones. To improve authentication accuracy while satisfying such real-world deployment constraints, we propose two novel feature engineering techniques: (1) computation of pair-wise correlations between accelerometer and gyroscope sensor values, and (2) on-device feature extraction technique to compute dynamic time warping (DTW) distance measurements between autoencoder inputs and outputs via transfer-learning. Using those two feature sets in an ensemble blender, we achieved 6.4 percent equal error rate (EER) in a public dataset. In comparison, blending two state-of-the-art solutions achieved 14.1 percent EER in the same test settings. Our real-world dataset evaluation showed increasing FRRs (user frustration) over two months; however, through periodic model retraining, we were able to maintain average FRRs around 2.5 percent while keeping attack detection rates around 89 percent. The proposed solution has been deployed in the latest Samsung Galaxy smartphone series to protect secure workspace through continuous authentication.
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- 작성일 2023-07-12
- 조회수 771
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- [Research] [Prof. Jinkyu Lee] RTCL@SKKU accepted in ACM Mobisys 2023
- A paper from RTCL@SKKU (Advisor: Jinkyu Lee) has been accepted in ACM Mobisys 2023. ACM Mobisys is the premier conference in mobile systems, in which around 40 papers are usually published every year. In this year, ACM Mobisys 2023 will be held in Helsinki, Finland. ACM Mobisys 2023 website: https://www.sigmobile.org/mobisys/2023/ RTCL@SKKU website: https://rtclskku.github.io/website/ (Paper Title) MixMax: Leveraging Heterogeneous Batteries to Alleviate Low Battery Experience for Mobile Users (Authors in RTCL@SKKU) Jaeheon Kwak (M.S. alumni, the first author), Prof. Jinkyu Lee (the co-corresponding author) (Teaser) https://youtu.be/LPXcpKlQxa0 (Abstract) Despite the physical advance of an existing single-cell battery system, mobile users are still suffering from low battery anxiety. With a careful analysis of users’ battery usage behavior collected for 19,855 hours, we propose a heterogeneous battery system, MixMax, consisting of three complementary battery types tailored to minimizing the low battery time. While composing a heterogeneous battery system opens up a chance to simultaneously improve the capacity and the charging speed, one must face non-trivial challenges to determine the ratio of enclosed batteries and charge/discharge policies during the run-time. They are highly dependent on each other, which entails almost infinite candidates for the choice. MixMax gracefully unwinds the dependencies as it formulates the decision-making problem into an optimization problem and decomposes it into multiple sub-problems instead. To evaluate MixMax, we fabricate coin-cell batteries and experiment with them to model an accurate battery emulator which sophisticatedly reproduces the dynamics of battery systems. Our experimental results demonstrate that MixMax can reduce the low battery time by up to 24.6% without compromising capacity, volume, weight, and more importantly, users’ battery usage behavior. In addition, we prototype MixMax on a smartphone, presenting the practicality of MixMax on mobile systems. Jinkyu Lee | jinkyu.lee@skku.edu | RTCL@SKKU | https://rtclskku.github.io/website/
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- 작성일 2023-06-07
- 조회수 735
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- [Research] [ Prof. Sungkil Lee] A paper of Computer Graphics Lab. (CGLab) is accepted to ACM SIGGRAPH 2023
- A paper of Computer Graphics Lab. (CGLab, Advisor: Sungkil Lee; the first author: Janghun Kim), entitled "Potentially Visible Hidden-Volume Rendering for Multi-View Warping," has been accepeted to ACM SIGGRAPH 2023. The paper is going to be presented at LA, USA, August, 2023. ACM SIGGRAPH is the most prestigious conference in Computer Graphics area. This paper is selected as a journal-track paper, and will be published in ACM Trasactions on Graphics, Volume 42, No. 4의 special issue. Abstract -------- This paper presents the model and rendering algorithm of Potentially Visible Hidden Volumes (PVHVs) for multi-view image warping. PVHVs are 3D volumes that are occluded at a known source view, but potentially visible at novel views. Given a bound of novel views, we define PVHVs using the edges of foreground fragments from the known view and the bound of novel views. PVHVs can be used to batch-test the visibilities of source fragments without iterating individual novel views in multi-fragment rendering, and thereby, cull redundant fragments prior to warping. We realize the model of PVHVs in Depth Peeling (DP). Our Effective Depth Peeling (EDP) can reduce the number of completely hidden fragments, capture important fragments early, and reduce warping cost. We demonstrate the benefit of our PVHVs and EDP in terms of memory, quality, and performance in multi-view warping.
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- 작성일 2023-05-25
- 조회수 944