RETRACTED: A machine learning based IoT for providing an intrusion detection system for security

Atul, Dhanke Jyoti; Kamalraj, R.; Ramesh, G.; Sankaran, Kris; Sharma, Sudhir; Khasim, Syed

doi:10.1016/j.micpro.2020.103741

Cited by 45 publications

(18 citation statements)

References 19 publications

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“…This help in proper classification based on the parameters related to possible attacks. Dhanke JyotiAtul et al [48] proposes Energy Aware Smart Home (EASH) framework tested on real-time sensor data for selected IoT devices. The study is experimented with J48, Naive Bayes , Multi-Layer Perceptron (MLP), multi-nominal logistic regression for classification and detection on anomalies.…”

Section: Review On Ml-based Ids Models For Iotmentioning

confidence: 99%

“…According to the results mentioned in Table 5 random forest and K-Nearest Neighbour models (KNN) show high accuracy compared to the other classification techniques [43] [50]. Many of the integrated models with federated learning and/or self-learning methods show competitive performance than the traditional methods [44] [48]. Multi-layer framework [53] [56] with different levels of testing has more impact, where the data is filtered for multiple times and the identification becomes much stronger with clustering techniques [53] [55].…”

Section: Artificial Neuralmentioning

confidence: 99%

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Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

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Section: Review On Ml-based Ids Models For Iotmentioning

confidence: 99%

Section: Artificial Neuralmentioning

confidence: 99%

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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“…The valuation and verification of their proposed method are performed based on IoT-BoT, and KDD Cup 1999 datasets with a JRip classifier. Atul et al [5] have exposed that digital transmission is offered an efficient communication stage to share and relocate information. Some of the system challenges they mentioned are security barriers, abnormality, and failure in service.…”

Section: Related Workmentioning

confidence: 99%

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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“…Owing to their IDSs, AI methods are widely used for providing security to IoT devices and networks to overcome the challenges, security issues and abnormalities [22]. Recent studies by Ghosh et al [23] claimed that the application of AI in IoT is a breakthrough for reducing human effort in providing security.…”

Section: Introductionmentioning

confidence: 99%

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

et al. 2022

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In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

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RETRACTED: A machine learning based IoT for providing an intrusion detection system for security

Cited by 45 publications

References 19 publications

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

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