WITHDRAWN: Improved security using machine learning for IoT intrusion detection system

Mandal, Kunal Kumar; Rajkumar, M.; Ezhumalai, P.; Jayakumar, D.; Yuvarani, R.

doi:10.1016/j.matpr.2020.10.187

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…6 provides a complete list of various forms of attacks, including their effects on IoT devices. When designing a security protocol for the IoT device's attacks, the following features must be considered [30,59]. The only element that both active and passive attacks face in the IoT scheme is privacy.…”

Section: Affects Of Attacks In Iotmentioning

confidence: 99%

“…Nowadays, everything, including classified and personal documents, medical records, and national security data, is safely encrypted and transmitted over the Internet by various IoT devices that are not meant to be exposed by unauthorized users [62,63] is challenging to keep most data secret from unwanted third parties because attackers can trace the physical location of the IoT computer and decode the information. [59].…”

Section: Affects Of Attacks In Iotmentioning

confidence: 99%

“…Indeed, data may be changed without an attacker if the contact channel is not secure. Integrity control guarantees that modifications to the message are detected accidentally and intentionally [39,59,67].…”

Section: Affects Of Attacks In Iotmentioning

confidence: 99%

See 2 more Smart Citations

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Haji

Ameen

2021

AJRCoS

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The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

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Section: Affects Of Attacks In Iotmentioning

confidence: 99%

Section: Affects Of Attacks In Iotmentioning

confidence: 99%

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Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Haji

Ameen

2021

AJRCoS

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“…Moreover, its techniques are working as a systematic way to obtain the data pattern from distinctive inputs without considering any algorithmic steps for rationalized findings. These days, machine learning has gain more attention to enhancing electronic gadgets throughout the day-to-day process regarding security concerns [9]. Anomaly detection based on machine learning mainly focuses on solving binary classification problems with two classes of activities such as abnormal or malware and normal [10].…”

Section: Introductionmentioning

confidence: 99%

DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

Santhadevi¹,

Janet²

2021

Preprint

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Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

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“…Mandal et al [1] focused on security difficulties to detect intrusion in IoT system in their study. They carried out a study on the machine learning classification algorithm applied in the IoT system network to analyze diverse attacks for the IDS on effects, types, and surface attacks over the IoT network and to increase the efficiency in identifying attacks.…”

Section: Introductionmentioning

confidence: 99%

Saldırı Tespit Sistemlerinde Makine Öğrenimi Algoritmalarının Performans Analizi

ÇİMEN

Sönmez

İlbaş³

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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With the developing technology, the need for the dissemination and protection of information is becoming increasingly important. Recently, attacks on information systems have increased significantly. In addition to the rise in the number of attacks, attacks of different types pose a great threat to systems. As a result of these attacks, institutions and users suffer serious damages. At this point, Intrusion Detection Systems (IDS) have a very important position. The pre-detection of these attacks on the systems and the preparation of the necessary reports can reduce the impact of the threats that may be encountered in the future. Recent studies are carried out so as to increase the performance of IDS. In this paper, classification was made using NSL-KDD dataset and SVM, KNN, Bayesnet, NavieBayes, J48 and Random Forest algorithms, and it was aimed to compare performance of these classifications by using WEKA. Consequently, it has been reached that the KNN algorithm had the best performance with an accuracy rate of 98.1237 %. In addition, the effect of increasing the number of folds and neighborhoods on the classification result has been examined comparatively.

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WITHDRAWN: Improved security using machine learning for IoT intrusion detection system

Cited by 17 publications

References 26 publications

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

Saldırı Tespit Sistemlerinde Makine Öğrenimi Algoritmalarının Performans Analizi

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