Machine Learning Techniques to Mitigate Security Attacks in IoT

Machine Learning Techniques to Mitigate Security Attacks in IoT

Kavi Priya S. (Mepco Schlnek Engineering College, India), Vignesh Saravanan K. (Ramco Institute of Technology, Rajapalayam, India) and Vijayalakshmi K. (Ramco Institute of Technology, Rajapalayam, India)
Copyright: © 2020 |Pages: 29
DOI: 10.4018/978-1-7998-0373-7.ch003

Abstract

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.
Chapter Preview
Top

Technologies Connecting Various Iot Devices

The main objective of the Internet of Things is to provide an environment in which the connected devices are able to transfer information without any manual interference. Thus, the exchange of information between two devices is possible under some well-established communication technologies, which are discussed below.

Wireless Sensor Networks (WSN)

Wireless Sensor Networks are comprised of set of independent nodes with limited bandwidth and frequency through which it can communication wirelessly with other nearby devices. In traditional wireless sensor network environment, the sensor node consists of the following parts:

  • 1.

    Sensor

  • 2.

    Microcontroller

  • 3.

    Memory

  • 4.

    Radio Transceiver

  • 5.

    Battery

The sensor nodes in the wireless sensor network has very limited communication range (short range communication). Hence the communication becomes multi-hop relay of information between the source and the base station. The required data is collected by the wireless sensors through collaboration amongst the various nodes, which is then sent to the sink node through a suitable routing strategy. The communication network formed dynamically by the use of wireless radio transceivers and it facilitates data transmission between nodes. Multi-hop transmission of data demands different nodes to take diverse traffic loads.

Radio Frequency Identification (RFID)

In context to the Internet of Things (IoT), RFID technology is mainly used in information tags interacting with each other automatically. For exchanging information between one another and interaction between them the radio frequency waves are used. There are some components being used in this RFID technology. The major two components are:

Key Terms in this Chapter

K-NN: K-nearest Neighbor.

IoT: Internet of Things.

SVM: Support Vector Machine.

Flooding: The deliberate congestion of communication channels through relay of unnecessary messages and high traffic.

Machine Learning: A field of information technology that has the ability to learn data insights by using statistical techniques.

Jamming: The communication channel between the nodes is compromised and occupied and jammed, thus preventing them the sensor nodes from communicating with each other.

DoS: Denial of Service.

Masquerade: A malicious node may act as another legitimate node to capture the message in the network.

RFID: Radio Frequency Identification.

Complete Chapter List

Search this Book:
Reset