A Fuzzy Intrusion Detection System for Identifying Cyber-Attacks on IoT Networks

Cristiani, André Luis; Lieira, Douglas D.; Meneguette, Rodolfo I.; Camargo, Heloisa de Arruda

doi:10.1109/latincom50620.2020.9282320

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…The researchers of [41] used a step-wise individually regular classify on a multi-source collection of real-world information concerning cybersecurity to identify infections and their sources. Cristiani et al proposed the Fuzzy Intrusion Detection System for IoT Networks (FROST), which was intended at avoiding and discovering various types of cyberattacks, but it had a high mistake probability and needed modification [12]. Rathore et al, on the other hand, provided an innovative identification approach built upon the ELF-Based Fuzz C-Means (ESFCM) method that utilized the cloud computer concept.…”

Section: Identification Of Cyberattacks In Iot Networkmentioning

confidence: 99%

Screening Cyberattacks and Fraud via Heterogeneous Layering

Alahmadi

2024

IJACSA

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On the Internet of Things (IoT) age, intelligent equipment is employed to give effective and dependable utilization of applications. IoT devices may recognize and provide extensive information while also intelligently processing that data. Data systems, systems for control, plus sensing are growing increasingly vital in contemporary manufacturing processes. The amount of internet of things gadgets and methods used is growing, that has culminated in a rise in assaults. Such assaults have the potential to interrupt international activities and cause major financial losses. Multiple methods, including Machine learning (ML) in addition to Deep Learning (DL), are being utilized for identifying cyberattack. In this investigation, researchers offer an ensemble staking approach that is strong strategy in ML for detecting assaults via the Internet of Things having excellent accuracy. Tests were carried out using three distinct information: credit card data, NSL-KDD, and UNSW. Single fundamental classifications were beaten by the suggested layered ensembles classification. The results show that the cyberattack detection model in this research possessed a 95.15% accuracy percentage, while the credit card fraud detection model achieved a 93.50% accuracy percentage.

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Section: Identification Of Cyberattacks In Iot Networkmentioning

confidence: 99%

Screening Cyberattacks and Fraud via Heterogeneous Layering

Alahmadi

2024

IJACSA

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“…on the UN-SWNB -15 data set to investigate the very effective attack detection mechanisms contained within it. The forecasts that L. Cristiani and his colleagues [4] made on potential attacks turned out to be accurate in a number of different ways. This method is malleable and can be modified to address a wide variety of possible dangers.…”

Section: Related Workmentioning

confidence: 99%

“…If the characteristics of each assault have not been thoroughly explored, categorised, and investigated for discrepancies, it is impossible to accept the conclusions that have been drawn from the investigation. Since this is an example of supervised learning [4] and the output class has already been decided, the methods of machine learning are utilized.…”

Section: Introductionmentioning

confidence: 99%

Detection of various attacks using B-GNB in health care IoT

Arul¹,

Vijayalakshmi²,

Abishek³

2023

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Despite various advances in health care applications, Intrusion detection remains a challenging task in wireless network due to numerous amount of traffic and large amount of sensitive data leading to various cyber attacks (DDoS, MITM, scan etc).The sensors are used to collect the real time data of patients or wearable devices and send the same to doctors for analyzing various diseases in early stages using IoT. Wireless network is vulnerable to intruders ,mainly for carrying sensitive information hence this paper proposes supervised machine learning technique B-GNB to detect various attacks in Health care data in IuT.The proposed model chooses the best features and accurately classifies attacks with 96% accuracy.

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“…[24] describes how researchers used an iterative simple linear classifier to identify intrusions and their sources. Although this was the case, there was a significant amount of incorrect categorization and it has to be reduced [25]. In contrast, [26] created an ESFCM approach using fog cloud technology to identify threats.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

Das

Pramod

2022

IJRITCC

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In the Internet of Things arena, smart gadgets are employed to offer quick and dependable access to services. IoT technology has the ability to recognize extensive information, provide information reliably, and process that information intelligently. Data networks, controllers, and sensors are increasingly used in industrial systems nowadays. Attacks have increased as a result of the growth in connected systems and the technologies they employ. These attacks may interrupt international business and result in significant financial losses. Utilizing a variety of methods, including deep learning (DL) and machine learning (ML), cyber assaults have been discovered. In this research, we provide an ensemble staking approach to efficiently and quickly detect cyber-attacks in the IoT. The NSL, credit card, and UNSW information bases were the three separate datasets used for the experiments. The suggested novel combinations of ensemble classifiers are done better than the other individual classifiers from the base model. Additionally, based on the test outcomes, it could be concluded that all tree and bagging-based combinations performed admirably and that, especially when their corresponding hyperparameters are set properly, differences in performance across methods are not significant statistically. Additionally, compared to other comparable PE (Portable Executable) malware detectors that were published recently, the suggested tree-based ensemble approaches outperformed them.

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A Fuzzy Intrusion Detection System for Identifying Cyber-Attacks on IoT Networks

Cited by 10 publications

References 19 publications

Screening Cyberattacks and Fraud via Heterogeneous Layering

Screening Cyberattacks and Fraud via Heterogeneous Layering

Detection of various attacks using B-GNB in health care IoT

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

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