Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
65
Some Submodular Data-Poisoning Attacks on Machine Learners
, 2015
"... We study data-poisoning attacks using a machine teaching framework. For a family of NP-hard attack problems we pose them as submodular function maximization, thereby inheriting efficient greedy algorithms with theoretical guarantees. We demonstrate some attacks with experiments. 1 ..."
Abstract
- Add to MetaCart
We study data-poisoning attacks using a machine teaching framework. For a family of NP-hard attack problems we pose them as submodular function maximization, thereby inheriting efficient greedy algorithms with theoretical guarantees. We demonstrate some attacks with experiments. 1
The Index Poisoning Attack in P2P File Sharing Systems
- In INFOCOM
, 2006
"... Abstract — P2P file-sharing systems have indexes, which users search to find locations of desired titles. In the index poisoning attack, the attacker inserts massive numbers of bogus records into the index for a set of targeted titles. As a result, when a user searches for a targeted title, the inde ..."
Abstract
-
Cited by 51 (0 self)
- Add to MetaCart
-sharing system. Applying our methodology to harvested data, we find that index poisoning is pervasive in both systems. We also outline a distributed blacklisting procedure for countering the index poisoning and pollution attacks. I.
Poisoning attacks against Support Vector Machines
- In International Conference on Machine Learning (ICML
, 2012
"... We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM’s test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes fro ..."
Abstract
-
Cited by 20 (11 self)
- Add to MetaCart
We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM’s test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes
A “Poisoning ” Attack Against Online Anomaly Detection
"... Introduction. Online anomaly detection techniques are steadily gaining attention in the security community, as the need grows to identify novel exploits in highly non-stationary data streams. The primary goal of online anomaly detection is to dynamically adjust the concept of normality while still d ..."
Abstract
-
Cited by 8 (6 self)
- Add to MetaCart
Introduction. Online anomaly detection techniques are steadily gaining attention in the security community, as the need grows to identify novel exploits in highly non-stationary data streams. The primary goal of online anomaly detection is to dynamically adjust the concept of normality while still
Bagging classifiers for fighting poisoning attacks in adversarial environments
- Roli (Eds.), 10th Int’l Workshop on Multiple Classifier Systems, volume 6713 of LNCS, Springer-Verlag
"... Abstract. Pattern recognition systems have been widely used in ad-versarial classification tasks like spam filtering and intrusion detection in computer networks. In these applications a malicious adversary may successfully mislead a classifier by “poisoning ” its training data with carefully design ..."
Abstract
-
Cited by 7 (3 self)
- Add to MetaCart
Abstract. Pattern recognition systems have been widely used in ad-versarial classification tasks like spam filtering and intrusion detection in computer networks. In these applications a malicious adversary may successfully mislead a classifier by “poisoning ” its training data with carefully
Stealthy Poisoning Attacks on PCAbased Anomaly Detectors
"... We consider systems that use PCA-based detectors obtained from a comprehensive view of the network’s traffic to identify anomalies in backbone networks. To assess these detectors’ susceptibility to adversaries wishing to evade detection, we present and evaluate short-term and long-term data poisonin ..."
Abstract
-
Cited by 5 (2 self)
- Add to MetaCart
poisoning schemes that trade-off between poisoning duration and the volume of traffic injected for poisoning. Stealthy Boiling Frog attacks significantly reduce chaff volume, while only moderately increasing poisoning duration. ROC curves provide a comprehensive analysis of PCA-based detection
Solutions to Swamp Poisoning Attacks in BitTorrent Networks
"... Abstract – Swamp poisoning in BitTorrent corrupts files sharing between peers. The worst case causes the swamp unusable as the protocol does not provide sufficient data integrity checking. This paper proposes two solutions in order to resolve this attack. Index Terms: Peer-to-peer systems, BT networ ..."
Abstract
- Add to MetaCart
Abstract – Swamp poisoning in BitTorrent corrupts files sharing between peers. The worst case causes the swamp unusable as the protocol does not provide sufficient data integrity checking. This paper proposes two solutions in order to resolve this attack. Index Terms: Peer-to-peer systems, BT
Poisoning completelinkage hierarchical clustering
- In Structural, Syntactic, and Statistical Pattern Recognition, 2014, In
"... Abstract. Clustering algorithms are largely adopted in security appli-cations as a vehicle to detect malicious activities, although few attention has been paid on preventing deliberate attacks from subverting the clus-tering process itself. Recent work has introduced a methodology for the security a ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
-crafted poisoning attacks into the input data, highlighting that the clustering algorithm may be itself the weakest link in a security sys-tem. In this paper, we extend this analysis to the case of complete-linkage hierarchical clustering by devising an ad hoc poisoning attack. We verify its effectiveness
ANTIDOTE: Understanding and Defending against Poisoning of Anomaly Detectors
"... Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security. For intrusion detection, such methods build a model for normal behavior from training data and detect attacks as deviations from that model. This process invites adv ..."
Abstract
-
Cited by 31 (5 self)
- Add to MetaCart
adversaries to manipulate the training data so that the learned model fails to detect subsequent attacks. We evaluate poisoning techniques and develop a defense, in the context of a particular anomaly detector—namely the PCA-subspace method for detecting anomalies in backbone networks. For three poisoning
Is Feature Selection Secure against Training Data Poisoning?
"... Learning in adversarial settings is becoming an important task for application domains where at-tackers may inject malicious data into the train-ing set to subvert normal operation of data-driven technologies. Feature selection has been widely used in machine learning for security applica-tions to i ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
to improve generalization and computa-tional efficiency, although it is not clear whether its use may be beneficial or even counterproduc-tive when training data are poisoned by intelli-gent attackers. In this work, we shed light on this issue by providing a framework to investi-gate the robustness
Results 1 - 10
of
65
