IoT Wireless Intrusion Detection and Network Traffic Analysis

Vasaki Ponnusamy,Maram Fahhad Almufareh,Nz Jhanjhi,Mamoona Humayun,Aun Yichiet

doi:10.32604/csse.2022.018801

Abstract

Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router. Almost every Internet of Things (IoT) devices such as smartphones, drones, and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11, etc.) to establish multiple inter-device connections simultaneously. With the flexibility of the wireless network, one can set up numerous ad-hoc networks on-demand, connecting hundreds to thousands of users, increasing productivity and profitability significantly. However, the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming. Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication. One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks (Network-based IDS) or at the node level (Host-based IDS). The challenge here is that there is no readily available benchmark data available for IoT network traffic. Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks, and crafting and labelling such dataset is very labor-intensive. This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD, and their suitability for wireless IDS implementation. We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications. This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network. Combining domain heuristcs and early classification results, the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS.

Highlights

Humayun et al [1] has mentioned that the automatic exchange of information between two systems or two devices without any manual input is the main objective of the Internet of Things (IoT)
There is no fundamental research in IoT intrusion detection (ID) that mainly focuses on wireless networks to the best of our knowledge
Careful selection of datasets is important in training ML-based wireless intrusion detection systems

Summary

Introduction

Humayun et al [1] has mentioned that the automatic exchange of information between two systems or two devices without any manual input is the main objective of the Internet of Things (IoT). To improve the IoT security on the network, an Intrusion Detection System (IDS) can be deployed to analyze the network traffic [2]. The IDS helps the network administrator detect any malicious activity on the network and alerts the administrator to secure the data by taking appropriate actions against those attacks. To implement an effective IDS in a wireless environment, careful selection of datasets or network traffic is of utmost importance. This research presents an analysis of network traffic from the wired and wireless (IEEE802.11) environment. The study presented here can be contributing to future research, mainly for IoT and wireless security and researchers who wish to implement intrusion detection systems for their IoT networks. A careful selection of network traffic features can contribute towards an exemplary implementation of wireless networks IDS. A comparison between the wired and wireless network and traffic characteristics is presented followed by traffic characteristics for wireless (IEEE802.11) networks

Objectives

Methods

Findings

Discussion

Conclusion