Predicting the Attacks in IoT Devices using DP Algorithm

Abstract

The fundamental goal of this study is to predict cyber-attacks before they occur and to protect the network. Most existing attack detection algorithms cannot identify zero day attacks because they lack previously known data patterns to predict the threat, which is one of the biggest issues in the existing approaches. This research work offers a novel prediction method based on Gaussian regression that identifies cyber-attacks utilizing a unique dual data pattern categorization technique with no false positives. To improve the accuracy of the prediction and to reduce the prediction time consumption, this study introduces a dual prediction technique one locally – at the fog level where non-parametric input data is dealt with two functions namely quadratic & reliability function to ease the prediction and the other universally – cloud level where result of skill mechanism is carried out. Even if the local prediction misses an attack, the universal prediction sniffs it and protects the IoT devices and the data. A detailed comparison regarding accuracy and packet drop is carried out by simulating flooding attacks using on varying numbers of dummy nodes and the proposed system found to outscore the existing methods convincingly.