DDoS Attack Identification
Figure 2-1 : IoT connected device forecast .
Most IoT devices lack a user interface to observe UE behavior , accept software updates and other human-enabled oversight applied to mobile phones . This , plus the sheer number of IoT devices , drives the need for mechanized IoT device management platforms . Such automation enables scale but presents a risk if a single compromised device management platform can rapidly deploy faulty or malicious software to a massive number of IoT devices .
Legacy DDoS detection , countermeasure , and mitigation mechanisms , designed for mobile phone networks , will therefore be unable to handle the pace and intensity of automated IoT DDoS attacks . The risk of such massive automated IoT DDoS attacks drives the need for intelligent , automated network DDoS detection , countermeasure , and mitigation mechanisms .
Much progress has been made with DDoS detection and countermeasures for traditional IP networks . We aim to address incremental vulnerabilities and opportunities associated with DDoS attacks over wireless networks serving IoT devices . Traditional DDoS detection mechanisms ( for example ieeexplore . ieee . org / document / 9246616 : April 2021 ) detect and classify DDoS through IP flow observation from a central cloud platform connected to network core nodes . As wireless
IIC Journal of Innovation 57