Securing Software-Defined Network Topology Discovery: A Comprehensive Review of Attack Detection
DOI:
https://doi.org/10.22399/ijcesen.3194Keywords:
Deep Learning, Machine Learning, Software-Defined Network, Topology DiscoveryAbstract
Software-Defined Network (SDN) has altered interconnected system operation by dividing data layer and control layers, which allows flexible, efficient, and programmable network configurations. To protect the integrity of geospatial data, centralized control, and transformational architecture of SDN is required. SDN controller service topology discovery is important for network services which can be susceptible to malicious activities. This Paper thoroughly examines the current attack detection methods during the topology discovery process. It reviews several topology discovery threats consisting of host location hijacking, topology poisoning, and link spoofing attacks. This paper summarizes valuable scopes, challenges, and future research scope, which can be a strong foundation for the development of a strong and resilient detection system to secure SDN networks against attacks done during topology discovery process.
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