Letteratura scientifica selezionata sul tema "Data exfiltration attack"
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Articoli di riviste sul tema "Data exfiltration attack"
Zimba, Aaron, e Mumbi Chishimba. "Exploitation of DNS Tunneling for Optimization of Data Exfiltration in Malware-free APT Intrusions". Zambia ICT Journal 1, n. 1 (11 dicembre 2017): 51–56. http://dx.doi.org/10.33260/zictjournal.v1i1.26.
Testo completoUllah, Faheem, Matthew Edwards, Rajiv Ramdhany, Ruzanna Chitchyan, M. Ali Babar e Awais Rashid. "Data exfiltration: A review of external attack vectors and countermeasures". Journal of Network and Computer Applications 101 (gennaio 2018): 18–54. http://dx.doi.org/10.1016/j.jnca.2017.10.016.
Testo completoDo, Quang, Ben Martini e Kim-Kwang Raymond Choo. "A Data Exfiltration and Remote Exploitation Attack on Consumer 3D Printers". IEEE Transactions on Information Forensics and Security 11, n. 10 (ottobre 2016): 2174–86. http://dx.doi.org/10.1109/tifs.2016.2578285.
Testo completoMeyers, Vincent, Michael Hefenbrock, Dennis Gnad e Mehdi Tahoori. "Leveraging Neural Trojan Side-Channels for Output Exfiltration". Cryptography 9, n. 1 (7 gennaio 2025): 5. https://doi.org/10.3390/cryptography9010005.
Testo completoSachintha, Shakthi, Nhien-An Le-Khac, Mark Scanlon e Asanka P. Sayakkara. "Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems". Applied Sciences 13, n. 5 (24 febbraio 2023): 2928. http://dx.doi.org/10.3390/app13052928.
Testo completoSingh, Sanjeev Pratap, e Naveed Afzal. "The Mesa Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration". International Journal of Network Security & Its Applications 16, n. 3 (29 maggio 2024): 23–40. http://dx.doi.org/10.5121/ijnsa.2024.16302.
Testo completoChattra, Eka, e Obrin Candra Brillyant. "Implementation of Meltdown Attack Simulation for Cybersecurity Awareness Material". ACMIT Proceedings 7, n. 1 (7 luglio 2021): 6–13. http://dx.doi.org/10.33555/acmit.v7i1.102.
Testo completoRietz, René, Radoslaw Cwalinski, Hartmut König e Andreas Brinner. "An SDN-Based Approach to Ward Off LAN Attacks". Journal of Computer Networks and Communications 2018 (21 novembre 2018): 1–12. http://dx.doi.org/10.1155/2018/4127487.
Testo completoAcar, Gunes, Steven Englehardt e Arvind Narayanan. "No boundaries: data exfiltration by third parties embedded on web pages". Proceedings on Privacy Enhancing Technologies 2020, n. 4 (1 ottobre 2020): 220–38. http://dx.doi.org/10.2478/popets-2020-0070.
Testo completoAksoy, Ahmet, Luis Valle e Gorkem Kar. "Automated Network Incident Identification through Genetic Algorithm-Driven Feature Selection". Electronics 13, n. 2 (9 gennaio 2024): 293. http://dx.doi.org/10.3390/electronics13020293.
Testo completoTesi sul tema "Data exfiltration attack"
Li, Huiyu. "Exfiltration et anonymisation d'images médicales à l'aide de modèles génératifs". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4041.
Testo completoThis thesis aims to address some specific safety and privacy issues when dealing with sensitive medical images within data lakes. This is done by first exploring potential data leakage when exporting machine learning models and then by developing an anonymization approach that protects data privacy.Chapter 2 presents a novel data exfiltration attack, termed Data Exfiltration by Compression (DEC), which leverages image compression techniques to exploit vulnerabilities in the model exporting process. This attack is performed when exporting a trained network from a remote data lake, and is applicable independently of the considered image processing task. By exploring both lossless and lossy compression methods, this chapter demonstrates how DEC can effectively be used to steal medical images and reconstruct them with high fidelity, using two public CT and MR datasets. This chapter also explores mitigation measures that a data owner can implement to prevent the attack. It first investigates the application of differential privacy measures, such as Gaussian noise addition, to mitigate this attack, and explores how attackers can create attacks resilient to differential privacy. Finally, an alternative model export strategy is proposed which involves model fine-tuning and code verification.Chapter 3 introduces the Generative Medical Image Anonymization framework, a novel approach to balance the trade-off between preserving patient privacy while maintaining the utility of the generated images to solve downstream tasks. The framework separates the anonymization process into two key stages: first, it extracts identity and utility-related features from medical images using specially trained encoders; then, it optimizes the latent code to achieve the desired trade-off between anonymity and utility. We employ identity and utility encoders to verify patient identities and detect pathologies, and use a generative adversarial network-based auto-encoder to create realistic synthetic images from the latent space. During optimization, we incorporate these encoders into novel loss functions to produce images that remove identity-related features while maintaining their utility to solve a classification problem. The effectiveness of this approach is demonstrated through extensive experiments on the MIMIC-CXR chest X-ray dataset, where the generated images successfully support lung pathology detection.Chapter 4 builds upon the work from Chapter 4 by utilizing generative adversarial networks (GANs) to create a more robust and scalable anonymization solution. The framework is structured into two distinct stages: first, we develop a streamlined encoder and a novel training scheme to map images into a latent space. In the second stage, we minimize the dual-loss functions proposed in Chapter 3 to optimize the latent representation of each image. This method ensures that the generated images effectively remove some identifiable features while retaining crucial diagnostic information. Extensive qualitative and quantitative experiments on the MIMIC-CXR dataset demonstrate that our approach produces high-quality anonymized images that maintain essential diagnostic details, making them well-suited for training machine learning models in lung pathology classification.The conclusion chapter summarizes the scientific contributions of this work, and addresses remaining issues and challenges for producing secured and privacy preserving sensitive medical data
Capitoli di libri sul tema "Data exfiltration attack"
Savić, Izabela, Haonan Yan, Xiaodong Lin e Daniel Gillis. "Adversarial Example Attacks and Defenses in DNS Data Exfiltration". In Communications in Computer and Information Science, 147–63. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9614-8_10.
Testo completoSai Charan, P. V., P. Mohan Anand e Sandeep K. Shukla. "DMAPT: Study of Data Mining and Machine Learning Techniques in Advanced Persistent Threat Attribution and Detection". In Artificial Intelligence. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99291.
Testo completoSood, Aditya K., e Richard Enbody. "Data Exfiltration Mechanisms". In Targeted Cyber Attacks, 77–93. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-800604-7.00005-x.
Testo completoAtti di convegni sul tema "Data exfiltration attack"
Cao, Phuong. "Jupyter Notebook Attacks Taxonomy: Ransomware, Data Exfiltration, and Security Misconfiguration". In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 750–54. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00106.
Testo completoLiu, Yali, Cherita Corbett, Ken Chiang, Rennie Archibald, Biswanath Mukherjee e Dipak Ghosal. "Detecting sensitive data exfiltration by an insider attack". In the 4th annual workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1413140.1413159.
Testo completo"SIDD: A Framework for Detecting Sensitive Data Exfiltration by an Insider Attack". In 2009 42nd Hawaii International Conference on System Sciences. IEEE, 2009. http://dx.doi.org/10.1109/hicss.2009.390.
Testo completoAggarwal, Palvi, Sridhar Venkatesan, Jason Youzwak, Ritu Chadha e Cleotilde Gonzalez. "Discovering Cognitive Biases in Cyber Attackers’ Network Exploitation Activities: A Case Study". In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004771.
Testo completoLuz, Júlio F., Paulo Freitas de Araujo-Filho, Henrique F. Arcoverde e Divanilson R. Campelo. "Unsupervised SOM-Based Intrusion Detection System for DNS Tunneling Attacks". In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbseg.2023.233583.
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