Relevant bibliographies by topics / SVM and poisoning attack

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  2. Dissertations / Theses
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  5. Conference papers

Academic literature on the topic 'SVM and poisoning attack'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "SVM and poisoning attack"

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Karvannan L and Dr. V S Thiyagarajan. "Data Poisoning Attacks on Federated Using Machine Learning." International Journal of Scientific Research in Science and Technology 12, no. 4 (2025): 85–90. https://doi.org/10.32628/ijsrst251257.

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Data poisoning attacks are a type of adversarial attack that aims to corrupt the training data used to build machine learning models. In this study, we investigate the effectiveness of data poisoning attacks on Three popular machine learning algorithms: SVM, PCA and Naïve Bayes, and Decision We propose a novel data poisoning attack that selectively manipulates training data to induce Miss Classification. Our attack strategy involves injecting a small number of Malicious examples that are designed to bias the decision boundaries of the classifiers towards a specific class. Our experimental resu
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Mahalle, Sheetal Anil, and Kaushal Kumar Dr. "Optimised curie pre-filter defending technique for SVM against poisoning attack." International Journal of Advance Research in Multidisciplinary 1, no. 2 (2023): 447–51. https://doi.org/10.5281/zenodo.14617570.

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In the contemporary business landscape, sustainability has become a critical factor in customer engagement strategies. This study explores the role of innovative green technologies in enhancing customer engagement, focusing on how businesses can leverage environmentally friendly practices to build stronger connections with their customers. This research used a mixed-methods strategy, drawing both quantitative and qualitative conclusions from customer surveys and case studies of businesses that have effectively implemented green technology. This study provides valuable insights for businesses a
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Jiao, Shuobo. "Impact of SVM-based Poisoning on the Semantic Recognition of Sounds." Applied and Computational Engineering 109, no. 1 (2024): 103–8. http://dx.doi.org/10.54254/2755-2721/109/20241412.

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Abstract. Machine learning is a technique that enables computers to learn from data and make predictions or decisions, data poisoning is the process of machine learning training where malicious samples are put in to make the model predictions or classifications less accurate. Data poisoning attacks help to reveal security vulnerabilities in AI systems. In this paper, we study Support Vector Machine (SVM) poisoning for sound recognition techniques, using AISHELL-3 dataset training data, from which we find the most vulnerable features for SVM poisoning. In the field of speech recognition, SVM ca
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Upreti, Deepak, Hyunil Kim, Eunmok Yang, and Changho Seo. "Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 459. http://dx.doi.org/10.11591/ijai.v13.i1.pp459-466.

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<span lang="EN-US">The user experience can be greatly improved by using learning models that have been trained using data from mobile devices and other internet of things (IoT) devices. Numerous efforts have been made to implement federated learning (FL) algorithms in order to facilitate the success of machine learning models. Researchers have been working on various privacy-preserving methodologies, such as deep neural networks (DNN), support vector machines (SVM), logistic regression, and gradient boosted decision trees, to support a wider range of machine learning models. The capacity
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Upreti, Deepak, Hyunil Kim, Eunmok Yang, and Changho Seo. "Defending against label-flipping attacks in federated learning systems using uniform manifold approximation and projection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 459–66. https://doi.org/10.11591/ijai.v13.i1.pp459-466.

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The user experience can be greatly improved by using learning models that have been trained using data from mobile devices and other internet of things (IoT) devices. Numerous efforts have been made to implement federated learning (FL) algorithms in order to facilitate the success of machine learning models. Researchers have been working on various privacy-preserving methodologies, such as deep neural networks (DNN), support vector machines (SVM), logistic regression, and gradient boosted decision trees, to support a wider range of machine learning models. The capacity for computing and storag
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Rathod, Tejal, Nilesh Kumar Jadav, Sudeep Tanwar, et al. "AI and Blockchain-Based Secure Data Dissemination Architecture for IoT-Enabled Critical Infrastructure." Sensors 23, no. 21 (2023): 8928. http://dx.doi.org/10.3390/s23218928.

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The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT’s sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, w
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Sajid, Maimoona Bint E., Sameeh Ullah, Nadeem Javaid, Ibrar Ullah, Ali Mustafa Qamar, and Fawad Zaman. "Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain." Wireless Communications and Mobile Computing 2022 (January 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/7386049.

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In this paper, a blockchain-based secure routing model is proposed for the Internet of Sensor Things (IoST). The blockchain is used to register the nodes and store the data packets’ transactions. Moreover, the Proof of Authority (PoA) consensus mechanism is used in the model to avoid the extra overhead incurred due to the use of Proof of Work (PoW) consensus mechanism. Furthermore, during routing of data packets, malicious nodes can exist in the IoST network, which eavesdrop the communication. Therefore, the Genetic Algorithm-based Support Vector Machine (GA-SVM) and Genetic Algorithm-based De
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Rawat, Romil, and Shailendra Kumar Shrivastav. "SQL injection attack Detection using SVM." International Journal of Computer Applications 42, no. 13 (2012): 1–4. http://dx.doi.org/10.5120/5749-7043.

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Shah, Zawar, and Steve Cosgrove. "Mitigating ARP Cache Poisoning Attack in Software-Defined Networking (SDN): A Survey." Electronics 8, no. 10 (2019): 1095. http://dx.doi.org/10.3390/electronics8101095.

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Address Resolution Protocol (ARP) is a widely used protocol that provides a mapping of Internet Protocol (IP) addresses to Media Access Control (MAC) addresses in local area networks. This protocol suffers from many spoofing attacks because of its stateless nature and lack of authentication. One such spoofing attack is the ARP Cache Poisoning attack, in which attackers poison the cache of hosts on the network by sending spoofed ARP requests and replies. Detection and mitigation of ARP Cache Poisoning attack is important as this attack can be used by attackers to further launch Denial of Servic
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Zhao, Puning, and Zhiguo Wan. "Robust Nonparametric Regression under Poisoning Attack." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 17007–15. http://dx.doi.org/10.1609/aaai.v38i15.29644.

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This paper studies robust nonparametric regression, in which an adversarial attacker can modify the values of up to q samples from a training dataset of size N. Our initial solution is an M-estimator based on Huber loss minimization. Compared with simple kernel regression, i.e. the Nadaraya-Watson estimator, this method can significantly weaken the impact of malicious samples on the regression performance. We provide the convergence rate as well as the corresponding minimax lower bound. The result shows that, with proper bandwidth selection, supremum error is minimax optimal. The L2 error is o
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Dissertations / Theses on the topic "SVM and poisoning attack"

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Thanh, Bui Tien. "Analysis of Topology Poisoning Attacks in Software-Defined Networking." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172353.

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Software-defined networking (SDN) is an emerging architecture with a great potentialto foster the development of modern networks. By separating the controlplane from the network devices and centralizing it at a software-based controller,SDN provides network-wide visibility and flexible programmability to networkadministrators. However, the security aspects of SDN are not yet fully understood.For example, while SDN is resistant to some topology poisoning attacks inwhich the attacker misleads the routing algorithm about the network structure,similar attacks by compromised hosts and switches are
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Nazari, Mahmood, and Kun Zhou. "Campus Network Design and Man-In-The-Middle Attack." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-25683.

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Security is at the front line of most networks, and most companies apply an exclusive security policy enclosing many of the Open Systems Interconnection (OSI) layers, from application layer all the way down to Internet Protocol (IP) security. On the other hand, an area that is often not protected with high level of security is the second layer of OSI model and this can compromise the entire network to a diversity of attacks.This report presents an experimental performance analysis within the real environment. It focuses on understanding and preventing the Man-In-The-Middle (MITM) also known as
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Loreti, Ludovico. "Pass The Hash attack. Panoramica, sperimentazione ed analisi." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17297/.

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La tesi è incentrata sul noto attacco informatico Pass The Hash e sul suo sviluppo all'interno di un sistema Microsoft Active Directory. Viene descritta una panoramica dell'attacco accennando la sua storia ed evoluzione fino ad oggi, alcuni dei tanti attacchi informatici ad esso propedeutici ed un background tecnico in cui vengono spiegate le sue componenti principali: il tool che viene utilizzato oggigiorno per sperimentare questa tecnica (Mimikatz), le funzioni di hash, i sistemi Single Sign-On, la suddivisione in domini, alberi e foreste di Active Directory. In seguito viene mostrato l'amb
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Ruegg, Evonne Teresa Nicole. "Investigating the porphyrias through analysis of biochemical pathways." Thesis, University of Canterbury. Biochemistry, 2014. http://hdl.handle.net/10092/10257.

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ABSTRACT The porphyrias are a diverse group of metabolic disorders arising from diminished activity of enzymes in the heme biosynthetic pathway. They can present with acute neurovisceral symptoms, cutaneous symptoms, or both. The complexity of these disorders is demonstrated by the fact that some acute porphyria patients with the underlying genetic defect(s) are latent and asymptomatic while others present with severe symptoms. This indicates that there is at least one other risk factor required in addition to the genetic defect for symptom manifestation. A systematic review of the heme biosyn
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Yu-HongLi and 李昱宏. "New Light-Weight Approach to Mitigate DNS Cache Poisoning Attack." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58809500201660231784.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>98<br>Domain Name System (DNS) is one of the most important systems on the Internet since it frees users from the requirement of remembering IP addresses of hosts. Because of the pivotal role of DNS, attack events have become more frequent. Once an attack succeeds, damage is significant and far reaching. As demonstrated by Dan Kaminsky’s poisoning attack in 2008, attackers can guess the correct authentication entries in DNS packets and update the cache information stored in Recursive DNS servers within a short timeframe. The cache mechanism of DNS is significantly
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Jeng, Jian-Ming, and 鄭健明. "A Probabilistic Model Against DNS Cache Poisoning Attack Under Multiple Resolvers." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/59364904777778432906.

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碩士<br>國立清華大學<br>資訊工程學系<br>98<br>Domain name system (DNS) is one of the core services on the Internet. For DNS, the most famous attack is DNS cache poisoning attack. Via cache poisoning, records in DNS cache could be tampered by an adversary. If a client query the compromised DNS server, he would connect to a malicious host located with an incorrect IP address. To prevent DNS cache poisoning, various approaches have been proposed to enhance DNS security. Without using cryptographic techniques, they can only raise the entropy rather than preventing from DNS cache poisoning. Several works raise t
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Lin, Ya-Ching, and 林亞菁. "A Study of Cache Poisoning Attack on Wireless Networks and Its Countermeasure." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/17226249635367404772.

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(6636224), Seunghee Lee. "Incremental Support Vector Machine Approach for DoS and DDoS Attack Detection." Thesis, 2019.

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<div> <div> <div> <p>Support Vector Machines (SVMs) have generally been effective in detecting instances of network intrusion. However, from a practical point of view, a standard SVM is not able to handle large-scale data efficiently due to the computation complexity of the algorithm and extensive memory requirements. To cope with the limitation, this study presents an incremental SVM method combined with a k-nearest neighbors (KNN) based candidate support vectors (CSV) selection strategy in order to speed up training and test process. The proposed incremental SVM method constructs or updates
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Books on the topic "SVM and poisoning attack"

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Dirty Politics: How Attack Politics Is Poisoning New Zealand's Political Environment. Craig Potton Publishing, 2014.

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Furst, John. The Complete First Aid Pocket Guide: Step-by-Step Treatment for All of Your Medical Emergencies Including • Heart Attack • Stroke • Food Poisoning ... • Shock • Anaphylaxis • Minor Wounds • Burns. Adams Media, 2018.

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Furst, John. Complete First Aid Pocket Guide: Step-By-Step Treatment for All of Your Medical Emergencies Including * Heart Attack * Stroke * Food Poisoning * Choking * Head Injuries * Shock * Anaphylaxis * Minor Wounds * Burns. Adams Media Corporation, 2018.

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(Editor), Christophor Dishovsky, Alexander Pivovarov (Editor), and Hendrik Benschop (Editor), eds. Medical Treatment of Intoxications and Decontamination of Chemical Agents in the Area of Terrorist Attack (NATO Science for Peace and Security Series / ... Security Series A: Chemistry and Biology). Springer, 2006.

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Herring, Jonathan. 6. Non-Fatal Non-Sexual Offences Against the Person. Oxford University Press, 2018. http://dx.doi.org/10.1093/he/9780198811817.003.0006.

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This chapter discusses a wide range of offences against the person: from an unwanted touching on an arm to a life-threatening attack. Key to the law is the right to bodily integrity: a person should not be touched against his or her wishes. This right is protected under the common law and Article 8 of the European Convention on Human Rights. Topics covered include assault and battery; assault occasioning actual bodily harm; malicious wounding; wounding with intent; poisoning; racially and religiously aggravated crimes; the Protection from Harassment Act 1997; threats offences; transmitting dis
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Book chapters on the topic "SVM and poisoning attack"

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Chen, Jinyin, Haibin Zheng, Mengmeng Su, Tianyu Du, Changting Lin, and Shouling Ji. "Invisible Poisoning: Highly Stealthy Targeted Poisoning Attack." In Information Security and Cryptology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42921-8_10.

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Huynh, Tran, Anh Tran, Khoa D. Doan, and Tung Pham. "Data Poisoning Quantization Backdoor Attack." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72907-2_3.

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Tang, Yuhan, Zhiyuan Wu, Bo Gao, Tian Wen, Yuwei Wang, and Sheng Sun. "Logits Poisoning Attack in Federated Distillation." In Knowledge Science, Engineering and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5498-4_22.

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Ahmed, Mohiuddin, Jinpeng Wei, Yongge Wang, and Ehab Al-Shaer. "A Poisoning Attack Against Cryptocurrency Mining Pools." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00305-0_11.

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Cheng, Zelei, and Zuotian Li. "Poisoning Attack for Inter-agent Transfer Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90022-9_21.

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Zhong, Junyan, Chang Liu, Huibin Wang, Lele Tian, Han Zhu, and Chan-Tong Lam. "Robust Graph Embedding Recommendation Against Data Poisoning Attack." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2233-8_8.

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Chen, Jinyin, Ximin Zhang, and Haibin Zheng. "Feature Transfer-Based Stealthy Poisoning Attack for DNNs." In Attacks, Defenses and Testing for Deep Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0425-5_2.

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Sato, Teppei, Mitsuyoshi Imamura, and Kazumasa Omote. "Threat Analysis of Poisoning Attack Against Ethereum Blockchain." In Information Security Theory and Practice. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41702-4_9.

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Gajbhiye, Saurabh, Priyanka Singh, and Shaifu Gupta. "Data Poisoning Attack by Label Flipping on SplitFed Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23599-3_30.

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Nabavirazavi, Seyedsina, Rahim Taheri, Mani Ghahremani, and Sundararaja Sitharama Iyengar. "Model Poisoning Attack Against Federated Learning with Adaptive Aggregation." In Adversarial Multimedia Forensics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49803-9_1.

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Conference papers on the topic "SVM and poisoning attack"

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Xiujin, Shi, Sun Naiwen, Gong Jiawei, and Yu Shoujian. "FedGSDW:Enhancing Federated Learning Robustness against Model Poisoning Attack." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825224.

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Sharma, Anshika, and Himanshi Babbar. "Innovative Approaches to Poisoning Attack Detection with Boosting Algorithms." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862074.

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Liao, Fenggui, Yongli Wang, and Dongmei Liu. "Poisoning Attack Detection Method Based on Adaptive Statistical Features." In 2024 4th International Conference on Digital Society and Intelligent Systems (DSInS). IEEE, 2024. https://doi.org/10.1109/dsins64146.2024.10992065.

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De Santis, Marco, and Christian Esposito. "Federated Learning under Attack: Game-Theoretic Mitigation of Data Poisoning." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962524.

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Sinha, Indrajeet Kumar, Avadhoot Bangal, Krishna Pratap Singh, and Shekhar Verma. "MDMFL: Monitored Defence Mechanism for Federated Learning against Data Poisoning Attack." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725046.

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Aristodemou, Marios, Xiaolan Liu, and Sangarapillai Lambotharan. "Bayesian Optimisation-Driven Poisoning Attack Against Personalised Federated Learning in Metaverse." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615551.

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Alsharidah, Ahmad A., Devki Nandan Jha, Bo Wei, Ellis Solaiman, and Rajiv Ranjan. "SecureFed: Blockchain-Based Defence for Data Poisoning Attack in Federated Learning." In 2024 IEEE/ACM 17th International Conference on Utility and Cloud Computing (UCC). IEEE, 2024. https://doi.org/10.1109/ucc63386.2024.00064.

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Chen, Yu-Chun, Hui-Nien Hung, Shun-Ren Yang, Yu-Chen Chou, and Phone Lin. "On Defending against Label Flipping Poisoning Attack for Personalized Federated Learning." In 2025 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2025. https://doi.org/10.1109/iwcmc65282.2025.11059648.

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Wang, Ziyi, and Liyuan Liu. "Manipulation and Destruction: A Novel Hybrid Poisoning Attack Against Federated Recommender Systems." In 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC). IEEE, 2024. https://doi.org/10.1109/icceic64099.2024.10775988.

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Zhang, Lu, Reginald Cushing, and Paola Grosso. "Defending OC-SVM based IDS from poisoning attacks." In 2022 IEEE Conference on Dependable and Secure Computing (DSC). IEEE, 2022. http://dx.doi.org/10.1109/dsc54232.2022.9888908.

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