30,656 research outputs found
An Analysis of Optimal Link Bombs
We analyze the phenomenon of collusion for the purpose of boosting the
pagerank of a node in an interlinked environment. We investigate the optimal
attack pattern for a group of nodes (attackers) attempting to improve the
ranking of a specific node (the victim). We consider attacks where the
attackers can only manipulate their own outgoing links. We show that the
optimal attacks in this scenario are uncoordinated, i.e. the attackers link
directly to the victim and no one else. nodes do not link to each other. We
also discuss optimal attack patterns for a group that wants to hide itself by
not pointing directly to the victim. In these disguised attacks, the attackers
link to nodes hops away from the victim. We show that an optimal disguised
attack exists and how it can be computed. The optimal disguised attack also
allows us to find optimal link farm configurations. A link farm can be
considered a special case of our approach: the target page of the link farm is
the victim and the other nodes in the link farm are the attackers for the
purpose of improving the rank of the victim. The target page can however
control its own outgoing links for the purpose of improving its own rank, which
can be modeled as an optimal disguised attack of 1-hop on itself. Our results
are unique in the literature as we show optimality not only in the pagerank
score, but also in the rank based on the pagerank score. We further validate
our results with experiments on a variety of random graph models.Comment: Full Version of a version which appeared in AIRweb 200
A Bag-of-Paths Node Criticality Measure
This work compares several node (and network) criticality measures
quantifying to which extend each node is critical with respect to the
communication flow between nodes of the network, and introduces a new measure
based on the Bag-of-Paths (BoP) framework. Network disconnection simulation
experiments show that the new BoP measure outperforms all the other measures on
a sample of Erdos-Renyi and Albert-Barabasi graphs. Furthermore, a faster
(still O(n^3)), approximate, BoP criticality relying on the Sherman-Morrison
rank-one update of a matrix is introduced for tackling larger networks. This
approximate measure shows similar performances as the original, exact, one
SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection
Sybil attacks are a fundamental threat to the security of distributed
systems. Recently, there has been a growing interest in leveraging social
networks to mitigate Sybil attacks. However, the existing approaches suffer
from one or more drawbacks, including bootstrapping from either only known
benign or known Sybil nodes, failing to tolerate noise in their prior knowledge
about known benign or Sybil nodes, and being not scalable.
In this work, we aim to overcome these drawbacks. Towards this goal, we
introduce SybilBelief, a semi-supervised learning framework, to detect Sybil
nodes. SybilBelief takes a social network of the nodes in the system, a small
set of known benign nodes, and, optionally, a small set of known Sybils as
input. Then SybilBelief propagates the label information from the known benign
and/or Sybil nodes to the remaining nodes in the system.
We evaluate SybilBelief using both synthetic and real world social network
topologies. We show that SybilBelief is able to accurately identify Sybil nodes
with low false positive rates and low false negative rates. SybilBelief is
resilient to noise in our prior knowledge about known benign and Sybil nodes.
Moreover, SybilBelief performs orders of magnitudes better than existing Sybil
classification mechanisms and significantly better than existing Sybil ranking
mechanisms.Comment: 12 page
A Mobile Ambients-based Approach for Network Attack Modelling and Simulation
Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets
A Comparative Study of Ranking-based Semantics for Abstract Argumentation
Argumentation is a process of evaluating and comparing a set of arguments. A
way to compare them consists in using a ranking-based semantics which
rank-order arguments from the most to the least acceptable ones. Recently, a
number of such semantics have been proposed independently, often associated
with some desirable properties. However, there is no comparative study which
takes a broader perspective. This is what we propose in this work. We provide a
general comparison of all these semantics with respect to the proposed
properties. That allows to underline the differences of behavior between the
existing semantics.Comment: Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI-2016), Feb 2016, Phoenix, United State
A Mobile Ambients-based Approach for Network Attack Modelling and Simulation
Attack Graphs are an important support for assessment and subsequent improvement of network security. They reveal possible paths an attacker can take to break through security perimeters and traverse a network to reach valuable assets deep inside the network. Although scalability is no longer the main issue, Attack Graphs still have some problems that make them less useful in practice. First, Attack Graphs remain difficult to relate to the network topology. Second, Attack Graphs traditionally only consider the exploitation of vulnerable hosts. Third, Attack Graphs do not rely on automatic identification of potential attack targets. We address these gaps in our MsAMS (Multi-step Attack Modelling and Simulation) tool, based on Mobile Ambients. The tool not only allows the modelling of more static aspects of the network, such as the network topology, but also the dynamics of network attacks. In addition to Mobile Ambients, we use the PageRank algorithm to determine targets and hub scores produced by the HITS (Hypertext Induced Topic Search) algorithm to guide the simulation of an attacker searching for targets
- …