IoT botnets have been used to launch Distributed Denial-of-Service (DDoS) attacks affecting the Internet infrastructure. To protect the Internet from such threats and improve security mechanisms, it is critical to understand the botnets’ intents and characterize their behavior. Current malware analysis solutions, when faced with IoT, present limitations in regard to the network access containment and network traffic manipulation. In this paper, we present an approach for handling the network traffic generated by the IoT malware in an analysis environment. The proposed solution can modify the traffic at the network layer based on the actions performed by the malware. In our study case, we investigated the Mirai and Bashlite botnet families, where it was possible to block attacks to other systems, identify attacks targets, and rewrite botnets commands sent by the botnet controller to the infected devices.

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Darknets are sets of IP addresses that are advertised but do not host any client or server. By passively recording the incoming packets, they assist network monitoring activities. Since packets they receive are unsolicited by definition, darknets help to spot misconfigurations as well as important security events, such as the appearance and spread of botnets, DDoS attacks using spoofed IP address, etc. A number of organizations worldwide deploys darknets, ranging from a few dozens of IP addresses to large /8 networks. We here investigate how similar is the visibility of different darknets. By relying on traffic from three darknets deployed in different contintents, we evaluate their exposure in terms of observed events given their allocated IP addresses. The latter is particularly relevant considering the shortage of IPv4 addresses on the Internet. Our results suggest that some well-known facts about darknet visibility seem invariant across deployments, such as the most commonly contacted ports. However, size and location matter. We find significant differences in the observed traffic from darknets deployed in different IP ranges as well as according to the size of the IP range allocated for the monitoring.