Securing the Internet of Things - Challenges and Complementary Overview of Machine Learning-based Intrusion Detection

Aug 4, 2026

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The rapid growth of IoT device deployments has intensified security and privacy risks, driven in part by the large volumes of data that devices collect, including personal information. Machine learning is presented as an effective approach for detecting vulnerabilities and cyberattacks, enabling models to learn from data and adapt to evolving threats. This review surveys ML algorithms used for IoT network security, highlights connections between upper-layer security and physical layer security, and summarizes current state, key challenges, and research directions based on ongoing work.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Introduction

IoT security and privacy concerns

Machine learning and deep learning for intrusion detection

Types of ML approaches

Background Information

IoT network fundamentals

2024 Innovations in Intelligent Systems and Applications Conference (ASYU) ©2024 IEEE DOI: 10.1109/ASYU62119.2024.10757068| 979-8-3503-7943-3/24/$31.00 | Securing the Internet of Things: Challenges and Complementary Overview of Machine Learning Based Intrusion Detection Latife Ilayda Isin Graduate School of Natural and Applied Science Atilim University Ankara, Turkey [ilaydaisin06@gmail.com](ilaydaisin06@gmail.com) Yaser Dalveren Department of Electrical and Electronics Engineering Izmir Bakircay University Izmir, Turkey [yaser.dalveren@bakircay.edu.tr](yaser.dalveren@bakircay.edu.tr) Ali Kara Department of Electrical and Electronics Enginering Gazi University Ankara, Turkey [akara@gazi.edu.tr](akara@gazi.edu.tr) Elva Leka Department of Applied Geology and Geo-informatics Polytechnic University of Tirana Tirane, Albania [elva.leka@fgjm.edu.al](elva.leka@fgjm.edu.al) Abstract—The significant increase in the number of IoT devices has also brought with it various security concerns. The ability of these devices to collect a lot of data, including personal information, is one of the important reasons for these concerns. The integration of machine learning into systems that can detect security vulnerabilities has been presented as an effective solution in the face of these concerns. In this review, it is aimed to examine the machine learning algorithms used in the current studies in the literature for IoT network security. Based on the authors' previous research in physical layer security, this research also aims to investigate the intersecting lines between upper layers of security and physical layer security. To achieve this, the current state of the area is presented. Then, relevant studies are examined to identify the key challenges and research directions as an initial overview within the authors' ongoing project. Keywords—internet-of-things, security, cyberattacks, machine learning, federated learning, intrusion detection I. INTRODUCTION Nowadays, Internet of Things (IoT) devices are preferred for use in many domains, such as healthcare, transportation, or smart cities, due to their convenience and efficiency [1] . Mainly, the main working principle of IoT devices is to analyze and share the data collected from an operational environment with a server or system [2] . Through a system, the devices can be controlled remotely. Although this can be considered as a significant advantage, the devices might be exposed to cyberattacks, which bring security and privacy issues, such as authentication, access control, and confidentiality [3], [4] . More specifically, if the huge amounts of data collected by IoT devices are not properly secured and protected, they can be stolen or accessed by unauthorized parties, increasing the risk of data leakage [5] . Intrusion detection is one of the most efficient ways to reduce the risk of data leakage. However, due to the complexity of intrusion attacks, the traditional detection systems might be insufficient at some points of attack detection. For this reason, using machine learning (ML)-based methods could be an efficient means for attack detection. ML offers an effective way of detecting cyberattacks on IoT devices due to its advanced features. Unlike traditional methods, ML algorithms can learn from data and adapt to various and growing threats. Thus, ML algorithms can analyze huge amounts of data and detect anomalies in a login or malicious activities in a pattern [6] . On the other hand, the efficiency of deep learning (DL)-based methods has been proven for cyberattack detection. Typically, a DL method can learn from incoming data, and generate new strategies for defense, which may be effective for mitigating cyberattacks. Various ML and DL methods have been developed to detect the cyberattacks on IoT devices [7] . Supervised learning is one of the common approaches. Decision trees (DT), support vector machines (SVM), convolutional neural networks (CNN), recurr

FAQs

Why do IoT devices increase security and privacy concerns?

IoT devices collect and share large amounts of data, including personal information, and enable remote control, which can expose them to authentication, access control, and confidentiality threats.

How do machine learning methods improve intrusion detection in IoT?

ML-based systems can learn from data, adapt to growing threats, and analyze large datasets to detect anomalies or malicious activity patterns that traditional systems may miss.

What types of ML approaches are commonly discussed for detecting IoT cyberattacks?

The text highlights supervised learning methods such as decision trees, SVM, CNN, RNN, and ANN, along with unsupervised learning approaches for protecting IoT devices.

Aug 4, 2026

Upload by Rowan