963 research outputs found
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
ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems
We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network services. The false positives raised by the NIDS analyzing the incoming traffic (which can be either signature- or anomaly-based) are reduced by correlating them with the output anomalies. We designed our architecture for TCP-based network services which have a client/server architecture (such as HTTP). Benchmarks show a substantial reduction of false positives between 50% and 100%
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