IFogLearn++: A new platform for fog layer's IoT attack detection in critical infrastructure using machine learning and big data processing

Abstract

Nowadays, with an increasing expansion of the internet of things (IoT) that has created massive data streams, online attack detection via stream processing has become a matter of extensive attention. Current encryption and authentication methods cannot satisfy the security requirements of IoT critical infrastructures because of many heterogeneous connected devices, a wide network geographical scope, rapid software development, the possibility of security bugs, and the emergence of new attacks. Therefore, an extended method based on machine learning, which can process data stream to detect the fog layer's IoT attacks and prevent the spread of intrusion to other network segments, was proposed in this paper. The IFogLearn++ uses a fog layer to facilitate fast data stream processing in the fog layer. Using a fog layer helps the network tolerate more attacks due to the annexation of an extra security layer before the cloud layer. Based on the results, the IFogLearn++ has similar accuracy to competitors and 18 and 8 times faster performance compared to SVM and Learn++. This characteristic helps secure massive data streams in IoT.