An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Adnan, Ahmed; Muhammed, Abdullah; Ghani, Abdul Azim Abd; Abdullah, Azizol; Hakim, Fahrul

doi:10.3390/sym13061011

Cited by 32 publications

(17 citation statements)

References 56 publications

(64 reference statements)

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“…However, none of these works present countermeasures of threats and attacks based on ubiquitous technology such as BC, FC, EC, and ML. Such ubiquitous technologies in analyzing the threats and attacks have been surveyed in scattered ways by the authors in [27][28][29][30][31][32][33][34][35][36][37]. A comprehensive review of all these technologies to combat IoT threats and attacks is not available.…”

Section: Comparison With Existing Surveysmentioning

confidence: 99%

“…The authors also propose a similar approach of using BC with ML to enhance security on the IoT data ecosystem. The state-of-the-art review on the application of ML for intrusion detection in IoT environment has been presented by Adnan in [37]. The authors consider the three dominant attributes, namely, computational complexity, concept drift, and dimensionality, which are mitigated by integrating ML in IoT, envisioning the security of the IoT-based applications.…”

Section: Comparison With Existing Surveysmentioning

confidence: 99%

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State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

et al. 2021

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The Internet of Things (IoT) plays a vital role in interconnecting physical and virtual objects that are embedded with sensors, software, and other technologies intending to connect and exchange data with devices and systems around the globe over the Internet. With a multitude of features to offer, IoT is a boon to mankind, but just as two sides of a coin, the technology, with its lack of securing information, may result in a big bane. It is estimated that by the year 2030, there will be nearly 25.44 billion IoT devices connected worldwide. Due to the unprecedented growth, IoT is endangered by numerous attacks, impairments, and misuses due to challenges such as resource limitations, heterogeneity, lack of standardization, architecture, etc. It is known that almost 98% of IoT traffic is not encrypted, exposing confidential and personal information on the network. To implement such a technology in the near future, a comprehensive implementation of security, privacy, authentication, and recovery is required. Therefore, in this paper, the comprehensive taxonomy of security and threats within the IoT paradigm is discussed. We also provide insightful findings, presumptions, and outcomes of the challenges to assist IoT developers to address risks and security flaws for better protection. A five-layer and a seven-layer IoT architecture are presented in addition to the existing three-layer architecture. The communication standards and the protocols, along with the threats and attacks corresponding to these three architectures, are discussed. In addition, the impact of different threats and attacks along with their detection, mitigation, and prevention are comprehensively presented. The state-of-the-art solutions to enhance security features in IoT devices are proposed based on Blockchain (BC) technology, Fog Computing (FC), Edge Computing (EC), and Machine Learning (ML), along with some open research problems.

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Section: Comparison With Existing Surveysmentioning

confidence: 99%

Section: Comparison With Existing Surveysmentioning

confidence: 99%

State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

et al. 2021

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“…Numerous investigations have demonstrated promising outcomes in the field of IDS through the application of machine learning (ML) and deep learning techniques [ 14 ]. However, many of these studies have relied on static data sources, failing to take into account the dynamic nature of the technology [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

Shyaa

Zainol

Abdullah

et al. 2023

Sensors

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Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup ‘99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX ‘12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup ‘99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.

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“…It makes sense to send out a team of guards to monitor and police a safe space. Therefore, having a monitoring system [1], [2], [3] remains the best engagement solution for well-being. Moreover, thanks to the development of the Internet of Things (IoT), we can now keep in real-time an eye out for our own assets remotely.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Intrusion Detection System For Space Monitoring : Case Study Of Farming In Benin

HOUNGUE

SAGBO

DAHOUE

et al. 2022

IJPSAT

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The Internet of Things (IoT) is one of the major tools of the new era of digital transformation. Through the Internet of Things, we are looking forward to exploring the new technologies in the digital world and how they help in improving the real world. In this work, we provide an overview of the approach used to deploy a surveillance system for monitoring any indoor space in general and specifically for agricultural spaces. The entire process starts after motion detection by motion sensors using Machine Learning techniques. This requires coverage and response processing algorithms implemented in the electronic chain. The electronic part of the system relates to microcontrollers, sensors and communications between them. A mobile application has been created to allow competent authorities to receive alerts for real-time intervention with the aim of preventing the destruction of crops slaughtered near herds passage. We have introduced the monitoring system’s synoptic diagram and its operation along with the power modules description. Prototype has been designed and performance evaluation has been performed to show the system is responsive most of time.

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An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Cited by 32 publications

References 56 publications

State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

Machine Learning-Based Intrusion Detection System For Space Monitoring : Case Study Of Farming In Benin

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