## 1 Introduction  
In any network, assessing the security level of transmitted information is an essential step for decision making. Especially in sensor networks, the security level of sensed data allows users to distinguish whether the data is actually secure, so that they can properly decide on its use. In order to estimate the security level of data from a network, it is necessary to evaluate factors that change dynamically, such as different network configurations (e.g., connectivity) and different security mechanism settings (e.g., cryptographic keys shared by the sensors). Therefore, security level quantification can also help security professionals make optimal decisions regarding network configuration adjustments, regarding the security mechanisms used and their respective settings [1]. The basis for estimating data security level in sensor networks is to assess both the vulnerabilities and strengths of the different security mechanisms installed, such as key management schemes (KMSs) and intrusion detection systems (IDSs) [2]. This means that the impact of an attack in a sensor network depends on the effectiveness and vulnerabilities of the security mechanisms deployed. Quantifying the probability of the resistance of security mechanisms against attacks is exactly the goal of security level estimation activity. A network that has security mechanisms, but that does not have a system to estimate the security level provided by these mechanisms, may lead users to have a false sense of security. This happens because of the simple fact that a network has security mechanisms, but this does not necessarily imply that this network will be totally safe, exactly due to the vulnerabilities of these mechanisms. Unlike sensor networks, in traditional networks, there is a considerable amount of research (e.g., [1; 2; 3]) and availability of standardized techniques for measuring security based on the evaluation of security mechanisms and on the analysis of network vulnerabilities, such as the Common Vulnerability Scoring System (CVSS) standard [4], which is used to quantify the severity of vulnerabilities. However, as sensor networks have vulnerabilities, attacks and security mechanisms different from those of traditional networks (due to the peculiar characteristics of WSNs, such as limited energy, memory and storage), it is not possible that the solutions proposed for traditional networks can be directly applied to WSNs. Thus, since sensor network security is a relatively new field compared to traditional network security, there are still few security level estimation schemes proposed so far, such as the works in [5; 6; 7; 8]. Moreover, none of these schemes validates the security metrics they propose. Furthermore, these proposals do not address the intrusion detection systems, taking into account only the attack prevention mechanisms. Some of them even propose subjective estimation models, by defining weights that must be set by network administrators based solely on their opinion, as the works in [5; 7; 8]. Another problem is that some of these works focus on calculating only the security level provided by a single security mechanism, as is the case with the works in [5; 8], which only estimate the security provided by the mechanisms proposed by the authors themselves. For all of these reasons, these proposals are not well suited for realistic WSN applications. Hence, a security level estimation scheme that tackles these issues is needed. In this paper, we propose a new security level estimation scheme for WSNs, the sensor data security estimator (SDSE). SDSE has the following main features:1. Unlike existing works, which use subjective weights, the security metrics defined by SDSE to estimate security level are calculated based on the configuration parameters of both the sensor network and the security mechanisms present in it and on information about the vulnerabilities and attacks of the sensor network;
2. Both prevention and intrusion detection mechanisms are addressed;
3.