Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems

Venkatraman, S.; Surendiran, B.

doi:10.1007/s11042-019-7495-6

Cited by 42 publications

(22 citation statements)

References 23 publications

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“…An adaptive hybrid IDS based on a timed automata controller technique was developed by S Venkatraman and B Surendiran [41]. The suggested Hybrid IDS possessed additional knowledge of common multimedia file types, which it applied to a full evaluation of packets containing multimedia…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Adaptive hybrid IDS with a timed automata controller [30] can overcome the challenges introduced by real‐time service changes. The hybrid IDS obtains additional knowledge on frequent multimedia file formats and uses this knowledge in a comprehensive analysis of packets carrying multimedia files.…”

Section: Related Workmentioning

confidence: 99%

“…The environment is represented by a triplet

E \equiv ({}, α, β, C)

in which

α = ({}, α_{1}, α_{2}, \dots, α_{r})

is the set of environmental inputs,

β = ({}, β_{1}, β_{2}, \dots, β_{r})

is the set of environmental outputs, and

C = ({}, c_{1}, c_{2}, \dots, c_{r})

is the set of penalty probabilities [30]. The environment inputs one of r selected automatic functions and outputs a response

β_{i}

to action i .…”

Section: Cellular Learning Automatamentioning

confidence: 99%

A new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata

2021

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The Internet of Things (IoT) is a new paradigm that connects physical and virtual objects from various domains such as home automation, industrial processes, human health, and monitoring. IoT sensors receive information from their environment and forward it to their neighboring nodes. However, the large amounts of exchanged data are vulnerable to attacks that reduce the network performance. Most of the previous security methods for IoT have neglected the energy consumption of IoT, thereby affecting the performance and reducing the network lifetime. This paper presents a new multistep routing protocol based on cellular learning automata. The network lifetime is improved by a performance‐based adaptive reward and fine parameters. Nodes can vote on the reliability of their neighbors, achieving network reliability and a reasonable level of security. Overall, the proposed method balances the security and reliability with the energy consumption of the network.

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“…A good dimensionality reduction method will directly affect machine-based performance of the learned intrusion detection model. Therefore, the problem of highdimensional data processing is the biggest challenge faced by traditional machine learning-based intrusion detection algorithms [2].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

Jin

2021

Journal of Sensors

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As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation-based intrusion detection model for long- and short-term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long- and short-term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning-based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two-way long- and short-term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long- and short-term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

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Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems

Cited by 42 publications

References 23 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 new method to detect attacks on the Internet of Things (IoT) using adaptive learning based on cellular learning automata

Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

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