IoT Network Attack Severity Classification

Abstract

Lack of network security is a major roadblock for Internet of Things (IoT) implementations. New attacks have emerged in recent times, taking advantage of vulnerabilities in IoT gadgets. The sheer scale of the IoT will also make standard network attacks more potent. Machine learning has found a lot of use in traffic classification and intrusion detection. We present a methodology in this piece that can be used to spot fraudulent communications and determine the identity of IoT devices. To determine the origin of the generated traffic, the nature of the traffic, and the presence of network hazards, this framework collects features per network flow. To achieve this, it relocates the network’s brains to its periphery. In order to discover which of several Machine Learning algorithms is superior to random forest, a number of them are pitted against one another. Using these Machine Learning methods, attacks can be ranked in terms of their potential damage. After running the tests, it was determined that TABNET has the highest accuracy (94.62%) for categorizing the network severity (93.51%) and that CNN has the lowest accuracy (93.51%) of the two.

Keywords

• iot
• attack severity
• network security
• machine learning
• classification