Biblio

NDN has been widely regarded as a promising representation and implementation of information- centric networking (ICN) and serves as a potential candidate for the future Internet architecture. However, the security of NDN is threatened by a significant safety hazard known as an IFA, which is an evolution of DoS and distributed DoS attacks on IP-based networks. The IFA attackers can create numerous malicious interest packets into a named data network to quickly exhaust the bandwidth of communication channels and cache capacity of NDN routers, thereby seriously affecting the routers' ability to receive and forward packets for normal users. Accurate detection of the IFAs is the most critical issue in the design of a countermeasure. To the best of our knowledge, the existing IFA countermeasures still have limitations in terms of detection accuracy, especially for rapidly volatile attacks. This article proposes a TC to detect the distributions of normal and malicious interest packets in the NDN routers to further identify the IFA. The trace back method is used to prevent further attempts. The simulation results show the efficiency of the TC for mitigating the IFAs and its advantages over other typical IFA countermeasures.

With the rapidly increasing number of Internet of Things (IoT) devices and their extensive integration into peoples' daily lives, the security of those devices is of primary importance. Nonetheless, many IoT devices suffer from the absence, or the bad application, of security concepts, which leads to severe vulnerabilities in those devices. To achieve early detection of potential vulnerabilities, network scanner tools are frequently used. However, most of those tools are highly specialized; thus, multiple tools and a meaningful correlation of their results are required to obtain an adequate listing of identified network vulnerabilities. To simplify this process, we propose a modular framework for automated network reconnaissance and vulnerability indication in IP-based networks. It allows integrating a diverse set of tools as either, scanning tools or analysis tools. Moreover, the framework enables result aggregation of different modules and allows information sharing between modules facilitating the development of advanced analysis modules. Additionally, intermediate scanning and analysis data is stored, enabling a historical view of derived information and also allowing users to retrace decision-making processes. We show the framework's modular capabilities by implementing one scanner module and three analysis modules. The automated process is then evaluated using an exemplary scenario with common IP-based IoT components.