Designing a Virtual Environment Monitoring System to Prevent Intrusions in Future Internet of Things

Patil, Rajendra; Modi, Chirag

doi:10.1007/978-981-10-8639-7_36

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…To prevent intrusions, R Patil et al [42] created a virtual environment monitoring system. Security is a major issue in today's developments in virtualization-based computer systems.…”

Section: Page 305mentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

Thomas¹,

Bhat

2021

IJMTS

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Purpose: The authors attempt to examine the work done in the area of Intrusion Detection System in IoT utilizing Machine Learning/Deep Learning technique and various accessible datasets for IoT security in this review of literature. Methodology: The papers in this study were published between 2014 and 2021 and dealt with the use of IDS in IoT security. Various databases such as IEEE, Wiley, Science Direct, MDPI, and others were searched for this purpose, and shortlisted articles used Machine Learning and Deep Learning techniques to handle various IoT vulnerabilities. Findings/Result: In the past few years, the IDS has grown in popularity as a result of their robustness. The main idea behind intrusion detection systems is to detect intruders in a given region. An intruder is a host that tries to connect to other nodes without permission in the world of the Internet of Things. In the field of IDS, there is a research gap. Different ML/DL techniques are used for IDS in IoT. But it does not properly deal with complexity issues. Also, these techniques are limited to some attacks, and it does not provide high accuracy. Originality: A review had been executed from various research works available from online databases and based on the survey derived a structure for the future study. Paper Type: Literature Review.

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“…To prevent intrusions, R Patil et al [42] created a virtual environment monitoring system. Security is a major issue in today's developments in virtualization-based computer systems.…”

Section: Page 305mentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

Thomas¹,

Bhat

2021

IJMTS

View full text Add to dashboard Cite

show abstract

“…Patil and Modi [33] designed a virtual environment monitoring system to prevent intrusions in T. This system used predefined signature database for known attacks and it applies anomaly-based etection for unknown attacks. Table 3 shows the IDS techniques and datasets covered by this paper and previous research apers.…”

Section: Proposed Hybrid Model For Idsmentioning

confidence: 99%

A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

et al. 2019

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The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

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Designing a Virtual Environment Monitoring System to Prevent Intrusions in Future Internet of Things

Cited by 2 publications

References 13 publications

Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

Machine Learning and Deep Learning Techniques for IoT-based Intrusion Detection Systems: A Literature Review

A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

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