A Survey on Blockchain Anomaly Detection Using Data Mining Techniques With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars, of which anomaly detection is an important problem. Data mining techniques, including conventional machine learning, deep learning and graph learning, have been concentrated for anomaly detection in the last few years. This paper presents a systematic survey of the blockchain anomaly detection results using data mining techniques. The anomaly detection methods are classified into 2 main categories, namely universal detection methods and specific detection methods, which contain 8 subclasses. For each subclass, the corresponding research are listed and compared, presenting a systematic and categorized overview of the current perspectives for blockchain anomaly detection. In addition, this paper contributes in discussing the advantages and disadvantages for the data mining techniques employed, and suggesting future directions for anomaly detection methods. This survey helps researchers to have a general comprehension of the anomaly detection field and its application in blockchain data.

Consortium BlockchainBased Architecture for Cyberattack Signatures and Features Distribution One of the effective ways of detecting malicious traffic in computer networks is intrusion detection systems (IDS). Though IDS identify malicious activities in a network, it might be difficult to detect distributed or coordinated attacks because they only have single vantage point. To combat this problem, cooperative intrusion detection system was proposed. In this detection system, nodes exchange attack features or signatures with a view of detecting an attack that has previously been detected by one of the other nodes in the system. Exchanging of attack features is necessary because a zeroday attacks (attacks without known signature) experienced in different locations are not the same. Although this solution enhanced the ability of a single IDS to respond to attacks that have been previously identified by cooperating nodes, malicious activities such as fake data injection, data manipulation or deletion and data consistency are problems threatening this approach. In this paper, we propose a solution that leverages blockchainu0027s distributive technology, tamperproof ability and data immutability to detect and prevent malicious activities and solve data consistency problems facing cooperative intrusion detection. Focusing on extraction, storage and distribution stages of cooperative intrusion detection, we develop a blockchainbased solution that securely extracts features or signatures, adds extra verification step, makes storage of these signatures and features distributive and data sharing secured. Performance evaluation of the system with respect to its response time and resistance to the featuressignatures injection is presented. The result shows that the proposed solution prevents stored attack features or signature against malicious data injection, manipulation or deletion and has low latency.