政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153151

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153151

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113392/144379 (79%)
Visitors : 51230027 Online Users : 914

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/153151

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153151

Title:	基於多模態融合的長序列表示嵌入框架 An Embedding Framework on Long Sequence Representation with Multimodal Fusion
Authors:	夏秋如 Shia, Chiu-Ju
Contributors:	蕭舜文 Hsiao, Shun-Wen 夏秋如 Shia, Chiu-Ju
Keywords:	長序列表示圖神經網絡點矩陣法注意力機制多模態融合 Long Sequence Representation Graph Neural Networks Dot-matrix Method Attention Mechanism Multimodal Fusion
Date:	2024
Issue Date:	2024-09-04 14:03:55 (UTC+8)
Abstract:	分析惡意軟體的API呼叫序列是一項重大挑戰，因為這些序列很長、屬於文字、事件型態且包含隱藏訊息，所以使得人在分析上變得困難。此外，與自然語言不同，這些API呼叫序列往往表現出程式相關的特性和結構，如循環和重複呼叫。因此，本研究重點分析這些序列中的結構，旨在解決它們在理解惡意軟體行為方面所呈現的複雜性。本研究提出了一種嵌入框架，旨在利用惡意軟體API呼叫序列的結構進行表徵學習，並點出序列中重要的API呼叫。我們使用了兩種不同提取結構資訊的方法，包括馬爾可夫模型和點矩陣法。為了幫助學習這些擁有複雜程式邏輯結構的長序列，我們的研究使用了圖神經網絡和視覺變換器，將圖結構和點矩陣結構轉換成高維向量。此外，我們利用基於注意力機制的多模態融合技術，將多模態資料融合成單一的表示向量，並顯示出序列中API呼叫的重要性。通過這些方法的整合，我們的框架不僅指出了惡意軟體家族中特定API呼叫的重要性，還提出了基於多模態融合技術的創新應用。 Analyzing malware through its API call sequences presents a significant challenge because it is long, text-based, event-based, and has hidden information, which may be difficult for manual examination. Moreover, unlike natural language, these call sequences often exhibit programming-related properties and structures such as loops and repeated calls. Consequently, this paper focuses on the analysis of such structures within call sequences, aiming to untangle the complexities they present in understanding malware behaviors. In this paper, we propose an embedding framework designed to learn the structure of malware call sequences in multiple ways for representation learning and to pinpoint the important calls in the sequence. Our method introduces two different approaches for structural information extraction including the Markov model and the dot matrix method. To navigate the complexities of variable-length sequences imbued with intricate programming logic, our study leverages Graph Neural Networks (GNN) and Vision Transformer Networks to distill both graph and dot matrix structures into high dimensional vectors. Furthermore, we employ multimodal fusion techniques based on the attention mechanism to fuse multimodal data into a cohesive representation that highlights the importance of the API call within the sequences. Through the integration of these advanced methods, our framework not only indicates the significance of specific calls within the malware family but also introduces the innovative application of multimodal fusion networks.
Reference:	Faitouri A Aboaoja, Anazida Zainal, Fuad A Ghaleb, Bander Ali Saleh Al-Rimy, Taiseer Abdalla Elfadil Eisa, and Asma Abbas Hassan Elnour. Malware detection issues, challenges, and future directions: A survey. Applied Sciences, 12(17):8482, 2022. AV-TEST. Av-atlas malware & pua. https://portal.av-atlas.org/malware. Accessed: 2024-07-02. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014. Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency. Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2):423–443, 2018. Ferhat Ozgur Catak, Ahmet Faruk Yazı, Ogerta Elezaj, and Javed Ahmed. Deep learning based sequential model for malware analysis using windows exe api calls. PeerJ Computer Science, 6:e285, 2020. Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. Cuckoo. Cuckoo sandbox book release 2.0.7. https://readthedocs.org/projects/cuckoo/downloads/pdf/latest/, 2020. Accessed: 2024-03-22. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. Massimo Ficco. Comparing api call sequence algorithms for malware detection. In Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020), pages 847–856. Springer, 2020. Hisham Shehata Galal, Yousef Bassyouni Mahdy, and Mohammed Ali Atiea. Behavior-based features model for malware detection. Journal of Computer Virology and Hacking Techniques, 12:59–67, 2016. Adrian J Gibbs and George A Mcintyre. The diagram, a method for comparing sequences: Its use with amino acid and nucleotide sequences. European journal of biochemistry, 16(1):1–11, 1970. Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017. Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. Jinsoo Hwang, Jeankyung Kim, Seunghwan Lee, and Kichang Kim. Two-stage ransomware detection using dynamic analysis and machine learning techniques. Wireless Personal Communications, 112(4):2597–2609, 2020. M Asha Jerlin and K Marimuthu. A new malware detection system using machine learning techniques for api call sequences. Journal of Applied Security Research, 13(1):45–62, 2018. Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016a. Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016b. Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989. Ce Li, Zijun Cheng, He Zhu, Leiqi Wang, Qiujian Lv, Yan Wang, Ning Li, and Degang Sun. Dmalnet: Dynamic malware analysis based on api feature engineering and graph learning. Computers & Security, 122:102872, 2022. Chen Li and Junjun Zheng. Api call-based malware classification using recurrent neural networks. Journal of Cyber Security and Mobility, 10(3):617–640, 2021. Xiang Ling, Lingfei Wu, Wei Deng, Zhenqing Qu, Jiangyu Zhang, Sheng Zhang, Tengfei Ma, Bin Wang, Chunming Wu, and Shouling Ji. Malgraph: Hierarchical graph neural networks for robust windows malware detection. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications, pages 1998–2007. IEEE, 2022. Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency. Efficient low-rank multimodal fusion with modality-specific factors. arXiv preprint arXiv:1806.00064, 2018. J Mathew and MA Ajay Kumara. Api call based malware detection approach using recurrent neural network—lstm. In Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) held in Vellore, India, December 6-8, 2018, Volume 1, pages 87–99. Springer, 2020. Microsoft. Pua:win32/loadmoney. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=PUA:Win32/LoadMoney&threatId=223699, a. Accessed: 2024-07-02. Microsoft. Win32/allaple. https://www.microsoft.com/en-us/wdsi/threats/threat-search?query=Allaple, b. Accessed: 2024-07-02. Microsoft. Worm:win32/allaple.o. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Worm:Win32/Allaple.O&threatId=-2147255709, c. Accessed: 2024-07-02. Microsoft. Worm:win32/rahack.a. https://www.microsoft.com/en-us/wdsi/threats/malware-encyclopedia-description?Name=Worm:Win32/Rahack.A&ThreatID=2147571243, d. Accessed: 2024-07-02. Fahad Mira. A review paper of malware detection using api call sequences. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pages 1–6. IEEE, 2019. Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, and Chen Sun. Attention bottlenecks for multimodal fusion. Advances in neural information processing systems, 34:14200–14213, 2021. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27, 2014. Talos. Graftor - but i never asked for this.... https://blogs.cisco.com/security/talos/graftor-but-i-never-asked-for-this. Accessed: 2024-07-02. TensorFlow. tf.keras.layers.textvectorization. https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization. Accessed: 2024-07-02. Daniele Ucci, Leonardo Aniello, and Roberto Baldoni. Survey of machine learning techniques for malware analysis. Computers & Security, 81:123–147, 2019. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks. stat, 1050(20):10–48550, 2017. VirusTotal. Virustotal. https://www.virustotal.com/. Accessed: 2024-03-22. Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, and Ion Stoica. Representing long-range context for graph neural networks with global attention. Advances in Neural Information Processing Systems, 34:13266–13279, 2021. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018. Nan Xu, Wenji Mao, and Guandan Chen. Multi-interactive memory network for aspect based multimodal sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 371–378, 2019. Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Tensor fusion network for multimodal sentiment analysis. arXiv preprint arXiv:1707.07250, 2017.
Description:	碩士國立政治大學資訊管理學系 111356021
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111356021
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

Files in This Item:

File	Description	Size	Format
602101.pdf		2892Kb	Adobe PDF	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback