Community structure is an important element in distributed network analysis. It helps in the detection of connectivity structure, the identification of undesired centralization, and the identification of crucial nodes. Most of the existing approaches use only topological information and neglect the rich information obtainable from the content data. In this paper, we propose a method that combines both topological data and content data to detect communities within blockchain public networks mainly the Bitcoin network. the proposed method is based on Feedforward Autoencoders and deep feature representations algorithm. Our results show that the community structure and most of the network properties are x4 higher than expected from a distributed network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics.

The Cosmos uses an Inter-Blockchain Communication protocol (IBC), which achieves interoperability among heterogeneous blockchains, and horizontal scaling. In Cosmos, inter-chain transactions show lower performance than intra-chain transactions. As the inter-chain transaction rate increases, the entire network's transaction processing speed per second (TPS) decreases. Detail performance evaluations on Cosmos are essential for improving interoperability and designing with the trade-offs between scalability and compatibility. Therefore, this paper proposes a new approach to measure and analyze the inter-chain message relay time in Cosmos. Our results focus on the performance of inter-chain in terms of communication between different chains.

This paper presents the development results for the Wireshark dissector of the Ethereum Node Discovery Protocol for monitoring and analyzing Ethereum P2P networks. The new version v5 of the Ethereum Node Discovery Protocol applied encryption to the traffic. Therefore, the Wireshark Node Discovery Protocol based on previous versions, such as the v4 dissector, can no longer be used to analyze the network. This paper develops a dissector that interprets encrypted packets of the Node Discovery Protocol based on Ethereum Node Discovery Protocol v5. Node Discovery Protocol analysis can be used to research network properties and improve network performance.

Bitcoin is a blockchain-based network where thousands of nodes are directly connected and communicate through a gossip-based flooding protocol. Mined blocks are propagated to all participating nodes in the network through compact block relay (CBR) protocol. Therefore, reducing the block relay time between nodes can reduce the block propagation time to all nodes and ultimately improve the performance of Bitcoin. In order to reduce the block relay time, the delivery time between nodes must be measured and analyzed to find the cause of the delay and provide ways to resolve it. Therefore, in this paper, we measure the CBR time between directly connected Bitcoin nodes and analyze the cause of the relay delay. Our results show that the delivery time delay is affected by whether or not a transaction is requested when assembling the compact block. In addition, the reason for requesting a transaction is due to the transaction propagation method and the characteristics of the transaction itself.

The Ethereum network uses Kademlia, a well-known P2P network, which allows the search for new nodes, and the change of connection with neighboring nodes. The Ethereum network must cope with security attacks such as DDoS attacks, 51% attacks, and Sybil attacks, and scalability issues, which slows down the transaction processing speed per second (TPS) as the network expands. A deep analysis of the dynamically changing topology and the connection between the nodes constituting the topology is needed to solve these problems. Therefore, in this paper, we measure the topology in the Ethereum network using a passive probing data collection to search for active nodes in the network and an active probing method to check the activity of nodes participating in the Ethereum network. Our results give a clear insight into the topology properties and topology visualization.

Bitcoin is the most representative UTXO-based blockchain platform, and many studies have been conducted related to it. However, account-based blockchains such as Ethereum are not yet profoundly analyzed. There is an urgent need to track all cryptocurrency transactions involved with illegal activities to deanonymize and identify malicious users. To link users' accounts to real identities in both networks, we first need to examine the differences between Ethereum and Bitcoin to propose an efficient deanonymizing method. Therefore, this paper compares and analyzes the wallet address clustering method of Bitcoin and Ethereum.

In Bitcoin, memory pool is a space of unconfirmed transactions. when a node receives newly generated transactions, the node verifies it and appends it into the local mempool. The transactions are stored in the mempool until they get included in a newly mined block. Since each node has a different capacity for storing unconfirmed transactions; thus, each node has different transactions stored in mempool. In this paper, we examine the mempool similarity using the Jaccard Index among four Bitcoin full nodes.

Ethereum is arguably the second most popular cryptocurrency-based network after Bitcoin, both make use of the distributed ledger technology known as blockchain. The blockchain-based networks are considered to be secure, but the level of provided security is proportional to the number of connected nodes, the number of influential nodes and the supported amount of hash power. Thus, the knowledge of the network properties and nodes behavior is useful to protect the network from the possible attacks such as double spending attacks, DDoS attacks, 51% attacks, and Sybil attacks. In this paper, we propose nodes discovery mechanism, which performs a P2P links discovery on Ethereum main-network. For that, we developed the Search-node, a modified version of Ethereum Client that search for all participating nodes in the network, store the nodes identification in the Bucket, and then process the peer discovery method. We analyze the collected data to discover the relationship between nodes, heavily-connected nodes, nodes geo-distribution and provide network snapshots, as well as some data related to security issues and possible attacks over the influential nodes. Our results show that approximately 300,000 nodes are connected over Ethereum network, and among these roughly 139 nodes show a high-degree.

Bitcoin is designed to allow its users to send and receive payments under a pseudonym. In Bitcoin, A pseudonym is an address from/to which a user sends/receives coins. Owing to users anonymity, one-half of the transactions in Bitcoin are associated with illegal trading or money laundering. Hence, it is necessary to develop an approach that estimates the number of coins owned by Bitcoin users and identifies transactions which deal with illegal activities. Considering that every address involved in the Bitcoin transaction is stored permanently in the ledger. If an address is ever mapped to a user identity, every transaction wrote by that user will be linked. In this study, we proposed a new method to identify the number of coins owned by users, in particular, we estimate the number of bitcoins held by the Bitcoin creator Satoshi Nakamoto, by analyzing the mining hash rate, mining reward and transactions data.

Indonesian National Institute of Aeronautics and Space (LAPAN) concerns to develop a system that provides actual information and prediction related to space weather activities called Space Weather Information and Forecast Services (SWIFtS). SWIFtS is supported by a data storage system that serves data near real-time. Because the data is collected on one single server and is served by a centralized model, problems emerge when the researchers need the server for data processing and making forecasts to update the content on the SWIFtS website. The system is incapable of providing the latest data when the server is down. Therefore, we propose a new system that utilizes the decentralized model for storing data using the Inter Planetary File System (IPFS). Our proposed method focuses on the background process, and its scheme will increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Other unused resources would be useful and no single point of failure. For monitoring, we develop a real-time data flow from each node and information of status nodes. As our expected, performance shown that our proposed system has better throughput than the existing system.

Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).