Poisoning Complete-Linkage Hierarchical Clustering

By Battista Biggio, Samuel Rota Bulò, Ignazio Pillai, M. Mura, E. Mequanint, Marcello Pelillo, Fabio Roli. 2014

62 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning (2017, methodology): “While this framework only considers attacks against supervised learning algorithms, we refer the reader to similar threat models to evaluate the security of clustering [58–60], and feature selection algorithms [27, 61] under di ﬀ erent attack settings.”
Certified Defenses for Data Poisoning Attacks (2017, background): “There have also been a number of demonstrated vulnerabilities in deployed systems (Newsome et al., 2006; Laskov and Šrndi`c, 2014; Biggio et al., 2014b).”
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization (2017, background): “Here we do not follow their naming convention, as it can cause confusion with the interpretation of targeted and indiscriminate attacks introduced in previous work [1, 2, 4, 7, 9, 10, 16, 39].”
Poisoning behavioral malware clustering (2014, methodology): “…goal, knowledge and capabilities, we can ﬁnally deﬁne the optimal strategy for attacking a clustering algorithm as: In this formulation, as in [4, 9], the adversary’s knowledge is characterized by a parameter vector θ , whose elements embed information about the input data D , the clustering…”
Adversarial Feature Selection Against Evasion Attacks (2016, background): “Previous work on evasion attacks has mainly investigated the vulnerability of supervised [7], [11], [20] and unsupervised learning techniques [21], [22] in different applications, including spam filtering [5], [23], [24], intrusion detection [25], and biometric recognition [1], [2].”
Support vector machines under adversarial label contamination (2015, background): “Recently, poisoning and evasion attacks against clustering algorithms have also been formalized to show that malware clustering approaches can be significantly vulnerable to well-crafted attacks [18, 19].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, background): “They can be applied to both supervised learning and unsupervised learning [5, 9, 12, 106].”
On the Security of Machine Learning in Malware C&C Detection (2016, methodology): “However, in reality the attacker may not behave as expected, for example using the feedback of the learning algorithm to formulate future attacks [Biggio et al. 2014a].”
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples (2016, background): “Multiple recent papers (Alfeld et al., 2016; Mei & Zhu, 2015b; Biggio et al., 2014; 2012; Mei & Zhu, 2015a) have considered the problem of an adversary being able to pollute the training data with the goal of inﬂuencing learning systems including support vector machines (SVM), autoregressive models…”
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View (2018, methodology): “However, some also introduced how to launch the poisoning attack against single-linkage and completelinkage hierarchical clustering [25], [26], [28].”
Attacks on Machine Learning: Lurking Danger for Accountability (2019, methodology): “Complete-linkage Hierarchical Clustering Unsupervised No Poisoning Attack [9] Single-Linkage Hierarchical Clustering Unsupervised No Poisoning Attack [13] Obfuscation Attack [13, 14] Decision Tree/Random Forest Supervised Yes/No Poisoning Attack [46] No Path-finding Attack [72] Model Inversion [26] ”
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018, methodology): “]. We show in Sects. 2.5–2.6 how this general formulation encompasses both evasion and poisoning attacks against supervised learning algorithms, even though it has also been used to attack clustering [6, 12, 13] and feature selection algorithms [59, 61]. Attack Algorithm. Before detailing the specif”