## 3 Related Works
### Related Studies--Attack Detection Techniques  
Moving on to some state-of-the-art studies, [PERSON] et al. [34] propose a blockchain-based database management framework for malware detection systems. Based on the proposed system flow chart, it is obvious that the proposal utilises the signature-based detection technique for verification purposes. Additionally, the proposed system performs a monitoring technique to detect any new malware through spreading the detection alarm on the network and then recording the malware's hash value on the blockchain, which is known to the other participants via the signature detection technique. Avoiding single-point-of-failure, as well as increasing the end-user's security, are the main benefits of the proposal, according to the authors. The main challenge with this proposal is the lack of ability to detect zero-day malware due to the nature of signature-based detection techniques, which do not support detecting these kinds of attacks. [PERSON] et al. [35] proposed a framework for detecting and classifying android-based mobile device malware, named Consortium Blockchain for Malware Detection and Evidence Extraction. The main idea in this proposal is to involve two interacted blockchains: public blockchain and consortium blockchain. In the public blockchain, a multifeature detection model is proposed to be used by consumers to detect and classify malware; this relies on some AI techniques and then stores the detected malware data on the public blockchain. In consortium blockchain, based on the detected malware data on the public blockchain, fact-based knowledge is created and stored by the members of anti-malware vendors in order to update the malware features. Based on the outcome of the experiment, in which multiple features have been used for malware detection, 92.5% detecting accuracy and a 94.6% recall rate can be achieved. [PERSON] and [PERSON] [36] proposed a blockchain-based web attack detection model using a signature-based detection method. The use of blockchain is to store and share the blacklist of some common and updatable web-based attacks. Consequently, two levels of signature-based detection are performed: the first level is performed locally, based on the local copy of the blacklist, while the second level is performed based on the last copy of the blacklist found on the blockchain. Based on comparison results, the decision will be made and the blacklist on the blockchain will be updated by adding a new block if the detected attack is new. This experiment shows that web-based attack signatures can be detected and that blockchain can be updated by employing the proposal. [PERSON] et al. [37] proposed a blockchain-based malware file detection method. Their proposal seeks to exploit certain blockchain functionalities, specifically the immutability of stored data and the robustness of consensus management. Furthermore, this proposal attempts to combine two types of detection techniques: signature-based techniques, which utilise the blockchain benefit directly, and anomaly-based techniques, which work locally on each user system. Technically, the proposed method aims to detect suspected malware files by calculating the hash value of the downloaded executable file to check whether the hash has been stored on the blockchain or not, which reflects whether the file is suspected to be malware or not, respectively. That is where the signature-based technique appears, along with one of the behaviour-based detection techniques performed locally to determine whether the file is malicious or not. Then, the results will be sent over the blockchain network to inform the other participants and vote about updated record information. Finally, based on the malicious degree, a decision whether to eliminate the detected file or not will be made by behaviour-based detection. Based on their experiment, the proposed method has improved the False Negative Rate and the False-Positive Rate.