Authors:
Nicolas M. Müller
;
Pascal Debus
;
Daniel Kowatsch
and
Konstantin Böttinger
Affiliation:
Cognitive Security Technologies, Fraunhofer AISEC, Garching near Munich and Germany
Keyword(s):
Intrusion Detection, IoT, Machine Learning, RPL.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Intrusion Detection & Prevention
;
Network Security
;
Sensor and Mobile Ad Hoc Network Security
;
Wireless Network Security
Abstract:
RPL, a protocol for IP packet routing in wireless sensor networks, is known to be susceptible to a wide range of attacks. Especially effective are ’single mote attacks’, where the attacker only needs to control a single sensor node. These attacks work by initiating a ’delayed denial of service’, which depletes the motes’ batteries while maintaining otherwise normal network operation. While active, this is not detectable on the application layer, and thus requires detection on the network layer. Further requirements for detection algorithms are extreme computational and resource efficiency (e.g. avoiding communication overhead) and the use of machine learning (if the drawbacks of signature based detection are not acceptable). In this paper, we present a system for anomaly detection of these kinds of attacks and constraints, implement a prototype in C, and evaluate it on different network topologies against three ’single mote attacks’. We make our system highly resource and energy effi
cient by deploying pre-trained models to the motes and approximating our choice of ML algorithm (KDE) via parameterized cubic splines. We achieve on average 84.91 percent true-positives and less than 0.5 percent false-positives. We publish all data sets and source code for full reproducibility.
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