Security Vulnerabilities, Threats, and Attacks in IoT and Big Data: Challenges and Solutions

Security Vulnerabilities, Threats, and Attacks in IoT and Big Data: Challenges and Solutions

Prabha Selvaraj, Sumathi Doraikannan, Vijay Kumar Burugari
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-5225-9742-1.ch006
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Big data and IoT has its impact on various areas like science, health, engineering, medicine, finance, business, and mainly, the society. Due to the growth in security intelligence, there is a requirement for new techniques which need big data and big data analytics. IoT security does not alone deal with the security of the device, but it also has to care about the web interfaces, cloud services, and other devices that interact with it. There are many techniques used for addressing challenges like privacy of individuals, inference, and aggregation, which makes it possible to re-identify individuals' even though they are removed from a dataset. It is understood that a few security vulnerabilities could lead to insecure web interface. This chapter discusses the challenges in security and how big data can be used for it. It also analyzes the various attacks and threat modeling in detail. Two case studies in two different areas are also discussed.
Chapter Preview
Top

Introduction

The main usage of Big Data is identify and optimize the processes in business, financial trading, enhancing and optimizing smart cities and nations, better relationship management of customers, enhancing healthcare, sports, transport services etc. IoT refers to the connection of a huge number of physical devices that are located all around the world are now connected to the internet in such a way that data could be collected and shared. Nowadays, IoT make sure that the environment which we live in could be made as smart i.e. makes our homes, offices and vehicles to be smarter and chattier. In addition, a few sensors play a vital role in assessing the noise present in the environment, pollution in the environment. A variety of techniques are used by IoT devices in order to connect with other devices for data sharing. Technologies like Standard Wi-Fi, Bluetooth low energy, Local Terminal Equipment (LTE), satellite connections are used for connecting several devices at various levels. Low Power Wide Area Networks (LPWAN) initiated its deployment of IoT devices with Sigfox, LORa and recently LTE Cat-M, Narrow Band IoT (NB-IOT) as discussed by Usman Raza et al (2017) are used and a comparison is given below in the Figure 1.

Figure 1.

Comparison of LPWAN technologies

978-1-5225-9742-1.ch006.f01

Recently enterprises augment the dependency of IoT devices which leads to more focus in security of these devices. IoT security does not alone deals with the security of the device but it also has to care about the web interfaces, cloud services and other devices that interact with it. Hence enterprise IoT systems must be free from vulnerabilities M Mowbray (2017). Therefore, many researchers put their attention on detection and countermeasures of security attacks in IoT systems.

Challenges and Issues in Big Data

Big data challenges and issues are discussed below:

  • •

    Privacy: The large of volume of data need to be safeguarded in order to prevent the misuse of these big data stores.

  • •

    Veracity: Data must meet the trustworthiness.

  • •

    Volume: Large volume of data need to be stored and processed in case of big data but RDBMS tools cannot be used to store or process it. So the traditional SQL based queries are not used to solve this challenge, instead compression technology can be used to compress the data at rest and also in memory.

  • •

    Analysis: The huge data generated are from different types of sites and in different structure so analyzing such data will take lot of resource and time so scaled out architecture can be used for processing. Data can be splitted into small pieces and processed in different computers available in network, and then it can be aggregated.

  • •

    Limitations of Traditional Encryption Approaches: It is difficult to secure configuration information and log files with the encryption in big data.

  • •

    Up to Date Transaction State: Updating the state and data logs is very significant because we are handling with sensitive data so they need to be monitored.

  • •

    Intrusion From the End Devices: It is necessary to secure data not only in the route it ravels but also at the destination to which data is sent.

  • •

    Real Time Data Consideration and Protection: Many companies are handling real time data but they don’t have periodic check so it is equally important to protect like maintaining historical transaction log.

  • •

    Securing the Storage Medium: It is necessary and important to secure data in storage as well as during transmission. So encryption plays an important role here.

  • •

    Data Provenance: Identification of data origin is another security measure is important in order to find the issues related to authentication and authorization and prevent it from attacks.

Complete Chapter List

Search this Book:
Reset