Machine Learning Approach to the Internet of Things Threat Detection

Alka Upadhyay; Sameena Naaz; Vinay Thakur; Iffat Rehman Ansari

Machine Learning Approach to the Internet of Things Threat Detection

Alka Upadhyay, Sameena Naaz, Vinay Thakur, Iffat Rehman Ansari

School of Arts, Humanities, and Social Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k- means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data.

Original language	English
Title of host publication	International Conference on Data Science and Network Engineering
Pages	407–418
Number of pages	12
Publication status	Published - 3 Nov 2023

Cite this

@inproceedings{ae19178e830b4004ae76b39a1cbf7de8,

title = "Machine Learning Approach to the Internet of Things Threat Detection",

abstract = "The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k- means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data.",

author = "Alka Upadhyay and Sameena Naaz and Vinay Thakur and Ansari, {Iffat Rehman}",

year = "2023",

month = nov,

day = "3",

language = "English",

pages = "407–418",

booktitle = "International Conference on Data Science and Network Engineering",

}

TY - GEN

T1 - Machine Learning Approach to the Internet of Things Threat Detection

AU - Upadhyay, Alka

AU - Naaz, Sameena

AU - Thakur, Vinay

AU - Ansari, Iffat Rehman

PY - 2023/11/3

Y1 - 2023/11/3

N2 - The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k- means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data.

AB - The development in software, hardware and communication technologies has made the broadcasting of sensory data collected from various devices very easy and simple. Interconnected devices through Internet technology form the Internet of Things (IoT). Applying intelligent methods for the analysis of this big data is the key which develops smart IoT applications. The world today has become increasingly dependent on digitized data which raises various security concerns and the need for advanced and reliable security technologies to deal with the increasing number of cyber-attacks. The work depicted in this paper makes use of machine learning techniques to detect cyber-attacks using the UNSW-NB15 data set and the KDD CUP 1999 dataset. Decision Tree, k- means clustering, multi-layer perception (MLP), Naive Byes and Random Forest classifier are the algorithms used in this work in order to find higher level information about the data.

M3 - Conference contribution

SP - 407

EP - 418

BT - International Conference on Data Science and Network Engineering

ER -