In modern automobiles, the reliance on vehicle Controller Area Network (CAN) networks has surged, underlining the paramount significance of safeguarding these networks against intrusions. In this work, we unveil an innovative approach for intrusion detection and explanation within in-vehicle CAN networks, employing the formidable synergy of Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Our method is tailored to address network imbalance challenges, offering prowess in binary and multiclass classification tasks. Integral to our approach is the seamless integration of SHAP values, serving as illuminating guides that unravel the intricacies of detected intrusions. This fusion elevates the system’s interpretability, equipping stakeholders with deeper insights. Our contribution is underpinned by a rigorous evaluation of our approach, featuring a comprehensive analysis of a published dataset alongside comparisons with established literature. The results underscore the exceptional efficacy of our method, showcasing its remarkable accuracy in detecting intrusions. However, the essence of our methodology transcends mere detection precision. The explanatory capabilities of SHAP values come to the forefront. This augments both understanding and decision-making into the contributing factors behind the detected intrusion classification model.
@inproceedings{Huong_2023_SOICT,author={Le, Thi-Thu-Huong and Suryanto, Naufal and Kim, Howon and Ji, Janghyun and Heo, Shinwook},title={Enhancing Intrusion Detection and Explanations for Imbalanced Vehicle CAN Network Data},month=dec,year={2023},isbn={9798400708916},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3628797.3628994},doi={10.1145/3628797.3628994},booktitle={Proceedings of the 12th International Symposium on Information and Communication Technology},pages={777–784},numpages={8},keywords={imbalance classification, XAI, intrusion detection system, CAN network, SHAP, XGBoost},location={Ho Chi Minh, Vietnam},series={SOICT '23},}
ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion
Naufal Suryanto, Yongsu Kim, Harashta Tatimma Larasati, and 6 more authors
In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2023
@inproceedings{Suryanto_2023_ICCV,author={Suryanto, Naufal and Kim, Yongsu and Larasati, Harashta Tatimma and Kang, Hyoeun and Le, Thi-Thu-Huong and Hong, Yoonyoung and Yang, Hunmin and Oh, Se-Yoon and Kim, Howon},title={ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month=oct,year={2023},pages={4305-4314},}
Trend of Paradigm for integrating Blockchain, Artificial Intelligence, Quantum Computing, and Internet of Things
Rini Wisnu Wardhani, Dedy Septono Catur Putranto, Thi-Thu-Huong Le, and 5 more authors
Blockchain technology is used to support digital assets such as cryptocurrencies and tokens. Commonly, smart contracts are used to generate tokens on top of the blockchain network. There are two fundamental types of tokens: fungible and non-fungible (NFTs). This paper focuses on NFTs and offers a technique to spot plagiarism in NFT images. NFTs are information that is appended to files to produce distinctive signatures. It can be found in image files, real artifacts, literature published online, and various other digital media. Plagiarism and fraudulent NFT images are becoming a big concern for artists and customers. This paper proposes an efficient deep learning-based approach for NFT image plagiarism detection using the EfficientNet-B0 architecture and the Triplet Semi-Hard Loss function. We trained our model using a dataset of NFT images and evaluated its performance using several metrics, including loss and accuracy. The results showed that the EfficientNet-B0-based deep neural network with triplet semi-hard loss outperformed other models such as Resnet50, DenseNet, and MobileNetV2 in detecting plagiarized NFTs. The experimental results demonstrate sufficient to be implemented in various NFT marketplaces.
@article{Prihatno_2023_AplSci,author={Prihatno, Aji Teguh and Suryanto, Naufal and Oh, Sangbong and Le, Thi-Thu-Huong and Kim, Howon},title={NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss},journal={Applied Sciences},volume={13},month=feb,year={2023},number={5},article-number={3072},url={https://www.mdpi.com/2076-3417/13/5/3072},issn={2076-3417},doi={10.3390/app13053072},}
2022
Adversarial Wall: Physical Adversarial Attack on Cityscape Pretrained Segmentation Model
Naufal Suryanto, Harashta Tatimma Larasati, Yongsu Kim, and 1 more author
In Korea Institute of Information Security and Cryptology, Nov 2022
@inproceedings{Suryanto_2022_CVPR,author={Suryanto, Naufal and Kim, Yongsu and Kang, Hyoeun and Larasati, Harashta Tatimma and Yun, Youngyeo and Le, Thi-Thu-Huong and Yang, Hunmin and Oh, Se-Yoon and Kim, Howon},title={DTA: Physical Camouflage Attacks Using Differentiable Transformation Network},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month=jun,year={2022},pages={15305-15314},}
2021
Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches
Yongsu Kim, Hyoeun Kang, Naufal Suryanto, and 3 more authors
@article{Yongsu_2021_Sensors,author={Kim, Yongsu and Kang, Hyoeun and Suryanto, Naufal and Larasati, Harashta Tatimma and Mukaroh, Afifatul and Kim, Howon},title={Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches},journal={Sensors},volume={21},month=aug,year={2021},number={16},article-number={5323},url={https://www.mdpi.com/1424-8220/21/16/5323},pubmedid={34450763},issn={1424-8220},doi={10.3390/s21165323},}
2020
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization
Naufal Suryanto, Hyoeun Kang, Yongsu Kim, and 3 more authors
@article{Suryanto_2020_distributed_blackbox,author={Suryanto, Naufal and Kang, Hyoeun and Kim, Yongsu and Yun, Youngyeo and Larasati, Harashta Tatimma and Kim, Howon},title={A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization},journal={Sensors},volume={20},month=dec,year={2020},number={24},article-number={7158},url={https://www.mdpi.com/1424-8220/20/24/7158},pubmedid={33327453},issn={1424-8220},doi={10.3390/s20247158},}
Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial Patches
Yongsu Kim, Hyoeun Kang, Afifatul Mukaroh, and 3 more authors
@inproceedings{Yongsu_2020_WISA,author={Kim, Yongsu and Kang, Hyoeun and Mukaroh, Afifatul and Suryanto, Naufal and Larasati, Harashta Tatimma and Kim, Howon},editor={You, Ilsun},title={Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial Patches},booktitle={Information Security Applications},month=dec,year={2020},publisher={Springer International Publishing},address={Cham},pages={3--15},isbn={978-3-030-65299-9},}
ABSTRACT: Together We Can Fool Them: A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization
Naufal Suryanto, Hyoeun Kang, Yongsu Kim, and 3 more authors
In Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, Nov 2020
@inproceedings{Suryanto_2020_CCSW,author={Suryanto, Naufal and Kang, Hyoeun and Kim, Yongsu and Yun, Youngyeo and Larasati, Harashta Tatimma and Kim, Howon},title={ABSTRACT: Together We Can Fool Them: A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization},month=nov,year={2020},isbn={9781450380843},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3411495.3421368},doi={10.1145/3411495.3421368},booktitle={Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop},pages={165},numpages={1},keywords={distributed attack, particle swarm optimization, adversarial examples},location={Virtual Event, USA},series={CCSW'20},}
2019
A Study on Federated Access Control in Interoperability of Heterogeneous IoT Platforms
Ismail, Naufal Suryanto, Hyeongon Kim, and 2 more authors
Korea Communications Society Summer Conference, Jun 2019
@inproceedings{Suryanto_2017_IES,author={Suryanto, Naufal and Ikuta, Chihiro and Pramadihanto, Dadet},booktitle={2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)},title={Multi-group particle swarm optimization with random redistribution},month=sep,year={2017},volume={},number={},pages={1-5},doi={10.1109/KCIC.2017.8228445},}
Trend of Paradigm for integrating Blockchain, Artificial Intelligence, Quantum Computing, and Internet of ThingsSmart Media Journal, Mar 2023
Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial PatchesIn Information Security Applications, Dec 2020
ABSTRACT: Together We Can Fool Them: A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm OptimizationIn Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, Nov 2020
OCF Gateway Implementation for IoT Platforms InteroperabilityIn Conference on Information Security and Cryptography-Winter 2019 (CISC-W’19), Nov 2019
Multi-group particle swarm optimization with random redistributionIn 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Sep 2017