Artificial Intelligence Assisted IoT Data Intrusion Detection

Shukla, Amogh; Ahamad, Shahanawaj; Rao, Guozheng; Al-Asadi, Avein Jabar; Gupta, Ankur; Kumbhkar, Makhan

doi:10.1109/iccct53315.2021.9711795

Cited by 28 publications

(6 citation statements)

References 10 publications

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“…Those findings are in line with the current literature that seems to be consistent in the sense that there is no clear go-to model. Shukla et al, [42] also mention that attack detection and mitigation are fundamental parts of model resilience, but their effectiveness is greatly dependent on the assumed context and attack models, which is in line with the findings in this article.…”

Section: Rq1: What Is the Best Ai/ml Model For Anomaly-based Supervis...supporting

confidence: 87%

“…Decision trees, random forests, and K-Nearest Neighbors performed well, while deep learning, MLP, Naïve Bayes, and Logistic Regression showed lower performance. However, a single-layer neural network was found effective in a resource-constrained environment [42]. Fusion methods, such as stacking have shown to outperform base classifiers [43,44].…”

Section: Ai-enabled Intrusion Detectionmentioning

confidence: 99%

“…Shukla et al, [42] for example, have shown that neural networks with a single layer are performing well already. Thus, neural networks are viable for use in attack detection scenarios within an IoT environment.…”

Section: Iot/cps Securitymentioning

confidence: 99%

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Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

Schmitt

2023

Preprint

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Section: Rq1: What Is the Best Ai/ml Model For Anomaly-based Supervis...supporting

confidence: 87%

Section: Ai-enabled Intrusion Detectionmentioning

confidence: 99%

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Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

Schmitt

2023

Preprint

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“…Typically, a security mechanism must handle large amounts of data generated by IoT devices in a distributed way and apply the proper statistical methods in a viable architecture. Shukla et al [28] applied an artificial intelligence (AI)-based intrusion detection methodology for IoT security. The work examined the usage of several machine learning methods inside IoT.…”

Section: Literature Reviewmentioning

confidence: 99%

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

Mahalingam,

Perumal,

Subburayalu

et al. 2023

Sensors

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The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.

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“…Regression trees [22] are then fitted using training data. Additionally, replacement is performed on every sample to ensure uniformity and the highest level of accuracy [27]. Fig.…”

Section: Figure 6 Sequential Minimal Optimization Flow Diagram B Clas...mentioning

confidence: 99%

Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest

Gupta

Janani

et al. 2022

Int. j. commun. netw. inf. secur.

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In our everyday life records, human activity identification utilizing MotionNode sensors is becoming more and more prominent. A difficult issue in ubiquitous computing and HCI is providing reliable data on human actions and behaviors. In this study, we put forward a practical methodology for incorporating statistical data into Sequential Minimization Optimization-based random forests. In order to extract useful features, we first prepared a 1-Dimensional Hadamard transform wavelet and a 1-Dimensional Local Binary Pattern-dependent extraction technique. Over two benchmark datasets, the University of Southern California-Human Activities Dataset, and the IM-Sporting Behaviors datasets, we employed sequential minimum optimization together with Random Forest to classify activities. Experimental findings demonstrate that our suggested model may successfully be utilized to identify strong human actions for matters related to efficiency and accuracy, and may challenge with existing cutting-edge approaches.

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Artificial Intelligence Assisted IoT Data Intrusion Detection

Cited by 28 publications

References 10 publications

Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

Securing the Digital World: Protecting Smart Infrastructures and Digital Industries with Artificial Intelligence (Ai)-Enabled Malware and Intrusion Detection

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

Wearable Sensors for Evaluation Over Smart Home Using Sequential Minimization Optimization-based Random Forest

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