MTD, Where Art Thou? A Systematic Review of Moving Target Defense Techniques for IoT

Navas, Renzo E.; Cuppens, Frédéric; Cuppens, Nora; Toutain, Laurent; Papadopoulos, Georgios Z.

doi:10.1109/jiot.2020.3040358

Cited by 39 publications

(18 citation statements)

References 61 publications

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“…Machine learning classifiers have also been trained using unigrams and weighted unigrams. The results demonstrate that machine learning methods perform well on weighted unigrams, with SVM achieving the highest level of reliability [ 29 , 30 , 31 , 32 , 33 , 34 ]. The formulae used in the calculation of the classification report are given below:

where TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative.…”

Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

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Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

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where TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative.…”

Section: Results and Comparative Analysismentioning

confidence: 99%

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Kaushik

Bhardwaj²,

Dahiya³

et al. 2022

Sensors

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“…SLR uses analytical methods to answer research questions about the literature on a specific topic. Existing SLR-based articles help to structure and to format this work, like [52] or [50].…”

Section: Methodsmentioning

confidence: 99%

The Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A Survey

Lavaur

Pahl

Busnel

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on the application of FL to Intrusion Detection Systems (IDSs). There, common criteria to compare existing solutions are missing. In particular, this survey shows: (i) how FL-based IDSs are used in different domains; (ii) what differences exist between architectures; (iii) the state of the art of FL-based IDS. With a structured literature survey, this work identifies the relevant state of the art in FL-based intrusion detection from its creation in 2016 until 2021. It provides a reference architecture and a taxonomy to serve as guidelines to compare and design FLbased IDSs. Both are validated with the existing works. Finally, it identifies research directions for the application of FL to intrusion detection systems.

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“…Recently, the use of MTD in addressing security challenges of IoT networks has been explored [6]. Mercado et al [7] introduce a MTD-based strategy for implementing Internet of Things (IoT) cybersecurity.…”

Section: Moving Target Defensementioning

confidence: 99%

Reinforcement Learning for Generating Secure Configurations

Dass¹,

Namin²

2021

Electronics

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Many security problems in software systems are because of vulnerabilities caused by improper configurations. A poorly configured software system leads to a multitude of vulnerabilities that can be exploited by adversaries. The problem becomes even more serious when the architecture of the underlying system is static and the misconfiguration remains for a longer period of time, enabling adversaries to thoroughly inspect the software system under attack during the reconnaissance stage. Employing diversification techniques such as Moving Target Defense (MTD) can minimize the risk of exposing vulnerabilities. MTD is an evolving defense technique through which the attack surface of the underlying system is continuously changing. However, the effectiveness of such dynamically changing platform depends not only on the goodness of the next configuration setting with respect to minimization of attack surfaces but also the diversity of set of configurations generated. To address the problem of generating a diverse and large set of secure software and system configurations, this paper introduces an approach based on Reinforcement Learning (RL) through which an agent is trained to generate the desirable set of configurations. The paper reports the performance of the RL-based secure and diverse configurations through some case studies.

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MTD, Where Art Thou? A Systematic Review of Moving Target Defense Techniques for IoT

Cited by 39 publications

References 61 publications

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

The Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A Survey

Reinforcement Learning for Generating Secure Configurations

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