Popular Blockchain-based cryptocurrencies, like Bitcoin, are increasingly being used maliciously for illegal trades. In order to trace and analyze suspected Bitcoin transactions and addresses, address clustering methods and Bitcoin flow analysis methods are gaining attention recently. However, existing methods only focus on Bitcoin addresses and flow, and neglect other important information, such as transaction structure and behavior features. In order to exploit all useful features of transactions, this paper proposes a Bitcoin transaction network analytic method for facilitating Blockchain forensic investigation based on an extended safe Petri Net. The structural features and dynamic semantics of Petri net are used in our proposed model to define the static and dynamic features of Bitcoin transactions. Nineteen features have been identified to define Bitcoin transaction patterns for analyzing and finding suspected addresses. Bitcoin gene has been embedded into the Petri net transitions to trace and analyze Bitcoin flow accurately. Finally, marginal distribution analysis of Bitcoin transaction features and data visualization techniques are used to eliminate some false positive samples further and to improve the accuracy of identifying suspected addresses. The proposed Bitcoin transaction network analytic method provides a reliable forensic investigation model along with a prototype platform which is beneficial for financial security. The efficiency of our proposed method is empirically verified based on a real-life case study analysis.

The substantial amount of IoT traffic, coupled with control messages, places a heavy burden on SDN controllers, which compromises their capacity. We investigate how SDN can revolutionize the conventional approach, aiming to overcome the limitations of communication overhead. Additionally, we delve into the essential optimizations required to minimize control overhead and migrations. Determining the appropriate controller necessitates the implementation of a mechanism that justifies the selection. Once the optimal controller has been identified, migration can be initiated. This paper introduces a solution that employs the NSGA-II algorithm to achieve the optimal selection of controllers. We assess the performance of the NSGA-II migration approach linking with the length-based same destination aggregation proposed in our previous work, in terms of packet delivery, packet loss, performance metrics, and the cost associated with the selected optimal controller.