11,868 research outputs found
Analyzing Cascading Failures in Smart Grids under Random and Targeted Attacks
We model smart grids as complex interdependent networks, and study targeted
attacks on smart grids for the first time. A smart grid consists of two
networks: the power network and the communication network, interconnected by
edges. Occurrence of failures (attacks) in one network triggers failures in the
other network, and propagates in cascades across the networks. Such cascading
failures can result in disintegration of either (or both) of the networks.
Earlier works considered only random failures. In practical situations, an
attacker is more likely to compromise nodes selectively.
We study cascading failures in smart grids, where an attacker selectively
compromises the nodes with probabilities proportional to their degrees; high
degree nodes are compromised with higher probability. We mathematically analyze
the sizes of the giant components of the networks under targeted attacks, and
compare the results with the corresponding sizes under random attacks. We show
that networks disintegrate faster for targeted attacks compared to random
attacks. A targeted attack on a small fraction of high degree nodes
disintegrates one or both of the networks, whereas both the networks contain
giant components for random attack on the same fraction of nodes.Comment: Accepted for publication in 28th IEEE International Conference on
Advanced Information Networking and Applications (AINA) 201
Octopus: A Secure and Anonymous DHT Lookup
Distributed Hash Table (DHT) lookup is a core technique in structured
peer-to-peer (P2P) networks. Its decentralized nature introduces security and
privacy vulnerabilities for applications built on top of them; we thus set out
to design a lookup mechanism achieving both security and anonymity, heretofore
an open problem. We present Octopus, a novel DHT lookup which provides strong
guarantees for both security and anonymity. Octopus uses attacker
identification mechanisms to discover and remove malicious nodes, severely
limiting an adversary's ability to carry out active attacks, and splits lookup
queries over separate anonymous paths and introduces dummy queries to achieve
high levels of anonymity. We analyze the security of Octopus by developing an
event-based simulator to show that the attacker discovery mechanisms can
rapidly identify malicious nodes with low error rate. We calculate the
anonymity of Octopus using probabilistic modeling and show that Octopus can
achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab
with 207 nodes and show that Octopus has reasonable lookup latency and
manageable communication overhead
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
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