# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 5 Discussion
### Security Analysis
#### 5.1.1 Theoretical Analysis  
In this section, we examine the REiSCH scheme theoretically with the set of threats mentioned in the threat model. We provide a theoretical analysis of REiSCH resistance to known attacks as follows:  
Figure 8: Data storage protocol. * **MITM and replay Proof 1:** An intruder tries to change or delete part of data/information when transferred between the network's entities. This situation is not possible because REISCH applies the ECDSA algorithm to sign data as well as some information such as \(SN_{Pseud}\). Additionally, an intruder cannot replay a message late due to the REISCH's entities use of timestamps such as \(SN_{TS}\) and \(CH_{TS}\). Consequently, REISCH resists MITM and replay attacks successfully. \(\square\)
* **DoS Proof 2:** An intruder applies a DoS attack to destroy availability service in servers such as the \(LS\) and \(CS\). The servers in REISCH initially check lightweight parameters such as \(SigLsE_{i}\) in \(LS\) and \(CS\) before completion of the authentication process. Moreover, these parameters change randomly in the communication process between entities. This procedure allows servers to check small parameters and prevent DoS duplicate messages. Therefore, REISCH withstands DoS threats. \(\square\)
* **Localization Proof 3:** An intruder tries to use the Sybil attack by using many legitimate SN IDs with fake data. Since \(SN_{i}\) waits for random \(SigLsE_{i}\) from \(LS\) each round, the intruder cannot deceive \(LS\) with fake data. Additionally, an intruder uses Wormhole attack by using many \(SN_{i}\) to camouflage communications between network entities. Each \(SN_{i}\) sends implicitly \(SN_{iS_{i}}\) to \(LS\) and \(Dif\) to \(CH_{i}\) as well as a timestamp. These parameters prevent counterfeit communications. Furthermore, if an intruder aims to apply a Sinkhole attack using node as a sink to attract all patient data from \(SN_{i}\), it cannot apply to REISCH because \(LS\) sends a unique \(LS_{OTP}\) including \(SigLS_{i}(SN_{Pseud_{i}})\) for all \(SN_{i}\). That intruder fails to detect \(SigLS_{i}\) and \(SN_{Pseud_{i}}\). Hence, REISCH strongly overcomes localization attacks. \(\square\)
* **EMR repository Proof 4:** Assume that an intruder can penetrate datasets in \(LS\). First, \(LS\) does not contain real patient information (real information such as the name is stored in \(AS\)). When the intruder gets these data, he/she cannot disclose that they belong to a particular patient. Second, the \(LS\)'datasets tremendously are difficult to penetrate. Furthermore, \(LS\) contains partial data for patients because the total data and patient history are transferred to \(DS\) by \(CS\) periodically. Thereupon, REISCH resists the EMR repository attack. \(\square\)
* **Eavesdropping Proof 5:** When an intruder eavesdrops and gets some of the messages transferred among \(SN_{i}\), \(CH_{i}\), \(LS\), and \(CS\), this intruder will not benefit from these messages that are being trapped because these messages contain no real information. Furthermore, the secret parameters, are completely hidden. Thus, REISCH prevents eavesdropping attacks from revealing patient information. \(\square\)
* **Node replication Proof 6:** An intruder applies a node replication attack using more than one \(SN_{i}\) with the the same legitimate ID. In REISCH, we suppose that all \(SN_{i}\) are inside a specific area in the hospital or clinic. Therefore, any \(SN_{i}\) outside this area finds it extremely difficult to send messages from fake \(SN_{i}\) with same legitimate ID. In addition, \(LS\) waits \(SN_{m}\) by \(CH_{i}\) at the same number of \(SN_{i}\) and the \(LS\) removes replicated \(SN_{m}\) or \(SigSmT_{3}\). In addition, when \(SN_{i}\) dies, \(LS\) records this situation in the dataset to prevent replication risks. As a result, REISCH effectively resists replication attacks.