## 2 Overview of Cyber Threats in EM
### Sensing Technologies
#### 2.2.2 Terrestrial Sensor Networks  
Sensor networks are used for local environmental and safety monitoring, as well as in body area networks for health monitoring [95]. There are a number of ways for a cyber attack to affect an EM sensor network. It may aim to capture sensor nodes, inject bad data, disrupt connection or exhaust sensor batteries [56], so as to reduce the situational awareness of the first responders, delay the detection of an emergency and provide false or obsolete data to decision makers. The confidentiality of data from other nodes can be compromised with a sinkhole attack, where legitimate traffic is enticed through a compromised node [96] or with a Sybil attack, where fake identities are generated, so as to make multiple nodes appear using a number of different layers in the protocol stack [42]. The fake multiple identities can be used to vouch for one another to overcome cryptography techniques, as the node at the source of the attack may have access to multiple encryption keys [59, 60]. The fake nodes can then send false link layer acknowledgements or inject false data into the network [97]. An excellent survey of the information security vulnerabilities of wireless sensor networks can be found in [98] and a survey of proposed defence mechanisms has been provided in [99]. Here, we will focus on research carried out specifically on the information security of sensor networks that have been used or proposed for EM.  
[PERSON] and [PERSON] have used DHS Glanser to experiment on detection mechanisms that would be applicable to cyber-physical systems used in emergencies [56]. DHS Glanser is a collection of human-portable sensors and vehicle-mounted base stations used by the US emergency services. Their approach proposed in [57] uses a voting system to dynamically choose the optimal detection interval and number of sensor nodes participating in detection of cyber threats, based on a given set of false alarm probabilities and compromise rates. In principle, the approach that makes the best use of the dual nature of cyber-physical systems in EM is to use such data in combination with physical monitoring data. For example, by linking cyber detection with video surveillance and a central security room to monitor and report incidents, one may facilitate detection of suspicious cyber-physical behaviour [100]. [PERSON] _et al_[58]. have proposed to use fuzzy logic to combine real-time network data and physical input features, including the differences between the values reported by neighbouring sensors. It has been argued that in the cyber-physical systems of the near future, such as the ones currently proposed for emergency response, detection can only be effective if the behaviour of devices and users, as well as the inferred context, are integrated into the decision making process [101].  
_Open issues in Sensor Networks used in EM_: The CodeBlue ad hoc sensor network infrastructure proposed in [61] for emergency medical care involves a decentralised security system that provides authentication and encryption. It implements an Elliptic Curve Cryptography-based system that takes up to 34 seconds to generate a key. The specific system may be prohibitively slow in an actual emergency, but improved light-weight cryptographic mechanisms that could be incorporated to body area networks have been proposed since and surveyed in [62]. Yet, it is interesting that wireless medical sensor networks used not only in EM, but generally in healthcare, have provisions only for authentication and privacy, as this is what is required for them to be accepted by the general public [102]. However, during an emergency these may be less important than availability. The intrusion detection mechanism for body area networks presented in [63] may be applicable, but it has been evaluated only in simulation. The protection of medical EM sensor networks against denial of service and other availability attacks remains an open issue for research.