Abstract
As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.
Highlights
The development of Wireless Sensor Networks (WSNs) was mainly motivated by military applications, such as control and surveillance in battlefields, but over time their deployment has been introduced to other areas, i.e., industrial control and monitoring, environmental monitoring, health monitoring of patients or assistance to disabled people and the emerging field of ambient intelligence.In all of the applications, it is mandatory to maintain the integrity and the correct operation of the deployed network
In this work we further propose a special way to integrate mobile nodes into this approach, given that mobility is a big issue in anomaly detection, as it can lead to observation data that have long range dependency and in this way increase its difficulty
In this work we have proposed a machine learning based anomaly detection approach for detecting unknown attacks in wireless sensor networks
Summary
The development of Wireless Sensor Networks (WSNs) was mainly motivated by military applications, such as control and surveillance in battlefields, but over time their deployment has been introduced to other areas, i.e., industrial control and monitoring, environmental monitoring, health monitoring of patients or assistance to disabled people and the emerging field of ambient intelligence.In all of the applications, it is mandatory to maintain the integrity and the correct operation of the deployed network. WSNs consist of huge numbers of sensor nodes, and since this number is huge, the nodes have to be very cheap This further implies that they possess very limited power and computation resources, small memory size and limited bandwidth usage. The incorporation of any tamper-resistant hardware would assume unacceptable costs All of this makes the security of these networks very challenging, as the resource limited devices cannot support the execution of any complicated algorithms. WSNs use a radio band that is license-free, so anybody with appropriate equipment can listen to the communication. Due to their deployment in areas that are difficult to reach makes them prone to node failures and adversaries
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