1,245 research outputs found
In-Network Volumetric DDoS Victim Identification Using Programmable Commodity Switches
Volumetric distributed Denial-of-Service (DDoS) attacks have become one of
the most significant threats to modern telecommunication networks. However,
most existing defense systems require that detection software operates from a
centralized monitoring collector, leading to increased traffic load and delayed
response. The recent advent of Data Plane Programmability (DPP) enables an
alternative solution: threshold-based volumetric DDoS detection can be
performed directly in programmable switches to skim only potentially hazardous
traffic, to be analyzed in depth at the controller. In this paper, we first
introduce the BACON data structure based on sketches, to estimate
per-destination flow cardinality, and theoretically analyze it. Then we employ
it in a simple in-network DDoS victim identification strategy, INDDoS, to
detect the destination IPs for which the number of incoming connections exceeds
a pre-defined threshold. We describe its hardware implementation on a
Tofino-based programmable switch using the domain-specific P4 language, proving
that some limitations imposed by real hardware to safeguard processing speed
can be overcome to implement relatively complex packet manipulations. Finally,
we present some experimental performance measurements, showing that our
programmable switch is able to keep processing packets at line-rate while
performing volumetric DDoS detection, and also achieves a high F1 score on DDoS
victim identification.Comment: Accepted by IEEE Transactions on Network and Service Management
Special issue on Latest Developments for Security Management of Networks and
Service
Tracking Normalized Network Traffic Entropy to Detect DDoS Attacks in P4
Distributed Denial-of-Service (DDoS) attacks represent a persistent threat to
modern telecommunications networks: detecting and counteracting them is still a
crucial unresolved challenge for network operators. DDoS attack detection is
usually carried out in one or more central nodes that collect significant
amounts of monitoring data from networking devices, potentially creating issues
related to network overload or delay in detection. The dawn of programmable
data planes in Software-Defined Networks can help mitigate this issue, opening
the door to the detection of DDoS attacks directly in the data plane of the
switches. However, the most widely-adopted data plane programming language,
namely P4, lacks supporting many arithmetic operations, therefore, some of the
advanced network monitoring functionalities needed for DDoS detection cannot be
straightforwardly implemented in P4. This work overcomes such a limitation and
presents two novel strategies for flow cardinality and for normalized network
traffic entropy estimation that only use P4-supported operations and guarantee
a low relative error. Additionally, based on these contributions, we propose a
DDoS detection strategy relying on variations of the normalized network traffic
entropy. Results show that it has comparable or higher detection accuracy than
state-of-the-art solutions, yet being simpler and entirely executed in the data
plane.Comment: Accepted by TDSC on 24/09/202
Partout: A Distributed Engine for Efficient RDF Processing
The increasing interest in Semantic Web technologies has led not only to a
rapid growth of semantic data on the Web but also to an increasing number of
backend applications with already more than a trillion triples in some cases.
Confronted with such huge amounts of data and the future growth, existing
state-of-the-art systems for storing RDF and processing SPARQL queries are no
longer sufficient. In this paper, we introduce Partout, a distributed engine
for efficient RDF processing in a cluster of machines. We propose an effective
approach for fragmenting RDF data sets based on a query log, allocating the
fragments to nodes in a cluster, and finding the optimal configuration. Partout
can efficiently handle updates and its query optimizer produces efficient query
execution plans for ad-hoc SPARQL queries. Our experiments show the superiority
of our approach to state-of-the-art approaches for partitioning and distributed
SPARQL query processing
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