Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network

Latif, Shahid; Huma, Zil e; Jamal, Sajjad Shaukat; Ahmed, Fawad; Ahmad, Jawad; Zahid, Adnan; Dashtipour, Kia; Aftab, Muhammad Umar; Abbasi, Qammer H.

doi:10.1109/tii.2021.3130248

Cited by 66 publications

(24 citation statements)

References 23 publications

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“…The results are better in comparison with the baseline approach. In [28], Latif et al proposed a dense random neural network (DnRaNN) technique to detect attacks in an IoT environment. The authors obtained an accuracy of 99.14% when using binary classification and 99.05% when using multiclass scenarios.…”

Section: Related Workmentioning

confidence: 99%

A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning

Malta

Pinto

2022

Future Internet

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An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.

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Section: Related Workmentioning

confidence: 99%

A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning

Malta

Pinto

2022

Future Internet

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“…Refs. [21,22], authors found that the conventional Internet of Things (IoT) technology is used in the industrial sector. The extensive expansion of IoT in the industrial sector gives rise to the obstruction of information security.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

et al. 2022

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Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

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“…A proficient and fast algorithm is needed to detect malicious attacks in IoV. DL algorithms provide more efficient performances than traditional ML algorithms [ 21 , 22 , 23 ]. For IDS, some commonly used DL algorithms are convolutional neural network (CNN), recurrent neural network (RNN), LSTM, and GRU.…”

Section: Introductionmentioning

confidence: 99%

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Ullah

Khan

Ahmad

et al. 2022

Sensors

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Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

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Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network

Cited by 66 publications

References 23 publications

A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning

A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning

Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

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