9,123 research outputs found
Adaptive service discovery on service-oriented and spontaneous sensor systems
Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
With the rapid growth of the Internet-of-Things (IoT), concerns about the
security of IoT devices have become prominent. Several vendors are producing
IP-connected devices for home and small office networks that often suffer from
flawed security designs and implementations. They also tend to lack mechanisms
for firmware updates or patches that can help eliminate security
vulnerabilities. Securing networks where the presence of such vulnerable
devices is given, requires a brownfield approach: applying necessary protection
measures within the network so that potentially vulnerable devices can coexist
without endangering the security of other devices in the same network. In this
paper, we present IOT SENTINEL, a system capable of automatically identifying
the types of devices being connected to an IoT network and enabling enforcement
of rules for constraining the communications of vulnerable devices so as to
minimize damage resulting from their compromise. We show that IOT SENTINEL is
effective in identifying device types and has minimal performance overhead
Collecting and Analyzing Failure Data of Bluetooth Personal Area Networks
This work presents a failure data analysis campaign on
Bluetooth Personal Area Networks (PANs) conducted on
two kind of heterogeneous testbeds (working for more than
one year). The obtained results reveal how failures distribution
are characterized and suggest how to improve the
dependability of Bluetooth PANs. Specically, we dene the
failure model and we then identify the most effective recovery
actions and masking strategies that can be adopted for
each failure. We then integrate the discovered recovery actions
and masking strategies in our testbeds, improving the
availability and the reliability of 3.64% (up to 36.6%) and
202% (referred to the Mean Time To Failure), respectively
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Comparison of Empirical Data from Two Honeynets and a Distributed Honeypot Network
In this paper we present empirical results and speculative analysis based on observations collected over a two month period from studies with two high interaction honeynets, deployed in a corporate and an SME (small to medium enterprise) environment, and a distributed honeypots deployment. All three networks contain a mixture of Windows and Linux hosts. We detail the architecture of the deployment and results of comparing the observations from the three environments. We analyze in detail the times between attacks on different hosts, operating systems, networks or geographical location. Even though results from honeynet deployments are reported often in the literature, this paper provides novel results analyzing traffic from three different types of networks and some initial exploratory models. This research aims to contribute to endeavours in the wider security research community to build methods, grounded on strong empirical work, for assessment of the robustness of computer-based systems in hostile environments
Distributed Port Scanning Detection
Conventional Network Intrusion Detection System (NIDS) have heavyweight processing and memory requirements as they maintain per flow state using data structures like linked lists or trees. This is required for some specialized jobs such as Stateful Packet Inspection (SPI) where the network communications between entities are recreated in its entirety to inspect application level data. The downside to this approach is that the NIDS must be in a position to view all inbound and outbound traffic of the protected network. The NIDS can be overwhelmed by a DDoS attack since most of these try and exhaust the available state of network entities. For some applications like port scan detection, we do not require to reconstruct the complete network tra�c. We propose to integrate a detector into all routers so that a more distributed detection approach can be achieved. Since routers are devices with limited memory and processing capabilities, conventional NIDS approaches do not work while integrating a detector in them. We describe a method to detect port scans using aggregation. A data structure called a Partial Completion Filter(PCF) or a counting Bloom filter is used to reduce the per flow state
DISCO: Distributed Multi-domain SDN Controllers
Modern multi-domain networks now span over datacenter networks, enterprise
networks, customer sites and mobile entities. Such networks are critical and,
thus, must be resilient, scalable and easily extensible. The emergence of
Software-Defined Networking (SDN) protocols, which enables to decouple the data
plane from the control plane and dynamically program the network, opens up new
ways to architect such networks. In this paper, we propose DISCO, an open and
extensible DIstributed SDN COntrol plane able to cope with the distributed and
heterogeneous nature of modern overlay networks and wide area networks. DISCO
controllers manage their own network domain and communicate with each others to
provide end-to-end network services. This communication is based on a unique
lightweight and highly manageable control channel used by agents to
self-adaptively share aggregated network-wide information. We implemented DISCO
on top of the Floodlight OpenFlow controller and the AMQP protocol. We
demonstrated how DISCO's control plane dynamically adapts to heterogeneous
network topologies while being resilient enough to survive to disruptions and
attacks and providing classic functionalities such as end-point migration and
network-wide traffic engineering. The experimentation results we present are
organized around three use cases: inter-domain topology disruption, end-to-end
priority service request and virtual machine migration
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