Dynamic Intelligence-Driven Engineering Flooding Attack Prediction Using Ensemble Learning
R. Angeline, S. Aarthi, R. Regin, S. Suman Rajest
Abstract
The rapid evolution of the Internet and communication technologies has fueled the proliferation of wireless sensor network (WSN) technology, which is increasingly important in today's interconnected world. For a broad variety of industries and applications, an enormous number of sensing devices continuously create and/or gather copious amounts of sensory data. However, it has been shown that WSN is susceptible to security flaws. These networks' abrasive and unmanaged deployment, along with their limited resources and the amount of data produced, raise serious security issues. The development of trustworthy solutions that include quick and continuous processes for live data stream analysis allowing the identification of flooding assaults is crucial since WSN applications are of the utmost importance. To put it in plain words: The assault is carried out by repeatedly sending pointless requests to the target computer in an effort to overwhelm it, cause the systems to fail, and prevent people from accessing the network or machine.