A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Ramadan, Rabie A.; Yadav, Kusum

doi:10.33166/aetic.2020.05.004

Cited by 25 publications

(14 citation statements)

References 26 publications

(32 reference statements)

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“…The Internet of Things devices are small, varied, and lack compatibility (Atlam et al, 2022). These qualities broaden the attack surface and complicate the development of any security solution (Ramadan and Yadav, 2020). Not only are IoT devices susceptible to network attacks (Putra et al, 2020), but also powerful hackers from unauthorized internet users.…”

Section: Figurementioning

confidence: 99%

“…Additionally, typical cryptography and IDS approaches may be incompatible with such a network. Moreover, as more people gain access to the Internet, hacking tactics become more sophisticated and accessible (Ramadan and Yadav, 2020). As a result, establishing an efficient monitoring strategy for intrusion detection presents a difficulty.…”

Section: The Motivation For the Present Studymentioning

confidence: 99%

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A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

Saheed

Usman

Sukat

et al. 2023

Front. Comput. Sci.

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The Internet of Things (IoT) represents a paradigm shift in which the Internet is connected to real objects in a range of areas, including home automation, industrial processes, human health, and environmental monitoring. The global market for IoT devices is booming, and it is estimated that there will be 50 billion connected devices by the end of 2025. This explosion of IoT devices, which can be expanded more easily than desktop PCs, has led to an increase in cyber-attacks involving IoT devices. To address this issue, it is necessary to create novel approaches for identifying attacks launched by hacked IoT devices. Due to the possibility that these attacks would succeed, Intrusion Detection Systems (IDS) are required. IDS' feature selection stage is widely regarded as the most essential stage. This stage is extremely time-consuming and labor-intensive. However, numerous machine learning (ML) algorithms have been proposed to enhance this stage to boost an IDS's performance. These approaches, however, did not produce desirable results in terms of accuracy and detection rate (DR). In this paper, we propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural network (DNN) for classification. The PSO with modification of inertia weight was utilized to optimize the parameters of DNN. The experimental analysis was performed on two realistic UNSW-NB15 and BoT-IoT datasets that are suitable for IoT environment. The findings obtained by analyzing the proposed HAEMPSO against the Generic attack in the UNSW-NB15 dataset gave an accuracy of 98.8%, and a DR of 99.9%. While the benign class revealed an accuracy of 99.9% and DR of 99.7%. In the BoT-IoT dataset, the DDoS HTTP attack revealed an accuracy of 99.22% and DR of 97.79%. While the benign class gave an accuracy of 97.54% and DR of 97.92%. In comparison with the state-of-the-art machine learning schemes, our proposed HAEMPSO-DNN achieved a competitive feat in terms of DR and accuracy.

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Section: Figurementioning

confidence: 99%

Section: The Motivation For the Present Studymentioning

confidence: 99%

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

Saheed

Usman

Sukat

et al. 2023

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…HIDS is a system that monitors the most important files in the existing system, while NIDS can analyze the incoming network flows. Other researchers divide IDSs according to the approach of the system, the most known is signature-based detection, which can recognize unknown and bad patterns, and anomalybased detection, which has the capability of detecting deviations from a model of "good" traffic and often relies on artificial intelligence [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

2023

IJIES

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The internet of things (IoT) has gained great importance due to its applicability in various daily life applications and its flexible and scalable framework. The wide and spreading use of IoT in the last few years has attracted intruders, who were able to take advantage of the vulnerabilities of any IoT framework due to the absence of robust security protocols. This discourages current and probable investors. Out-of-date intrusion detection models are mainly developed to support information technology systems using built-in, predefined patterns or highly imbalanced datasets. Over the past decade, deep learning models have outperformed traditional machine learning models in attack detection tasks. The biggest challenge in detecting zero-day attacks is determining the best deep-learning classifier. Numerous research initiatives have combined ensemble learning to improve performance, avoid overfitting, and minimize errors. In this work, to address this research gap, we propose a new enhanced meta-learning ensemble deep learning model based on stacking that combines the baseline deep learning models using two tiers of meta-classifiers. Then, conducting several experiments on two recent huge-size different IoT benchmark datasets to evaluate the performance of the proposed model. Different baseline deep learning classifiers are trained for each dataset, and their performance is compared to the proposed ensemble model. The findings show that the proposed ensemble model significantly enhances the classification accuracy of baseline deep-learning models.

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“…Centred on this, it is essential to maintain individual privacy along with securing the shared data. Therefore, much attention and research interest have been attracted by privacy and also security in the past 10 years [6]. The data transmitted betwixt the sensor and also the IS must not only be authenticated but also be free from alteration, or else the data shall be tracked or attacked by the users [7].…”

Section: Introductionmentioning

confidence: 99%

A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

et al. 2022

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Due to the realization of 5G technology, the Internet of things (IoT) has made remarkable advancements in recent years. However, security along with data balancing issues is proffered owing to IoT data's growth. The universally unique identifier short input pseudo‐random (SiP) hash‐based elliptic curve cryptography (UUDIS‐ECC) centred secure data transfer (DT) and linear scaling Rock Hyraxes swarm‐based convolutional neural network (LSRHS‐CNN) centred 5G IoT data balancing are proposed here to address those issues. Authentication, destination selection, validation, secure DT, and also load balancing are the proposed method's five phases. Initially, during the registration phase, the Length Nano ID (LNanoID) is created in the authentication. The user is permitted to further communicate if the LNanoID is matched with the already saved LNanoID. The destination is selected if the user is authorized. Utilizing the sender and the receiver's public key, the hash code is produced in the validation centre by the SiP hash function after destination selection. After that, by employing the UUDIS‐ECC algorithm, the IoT data is safely transmitted towards the destination. The 5G IoT data is balanced by using the LSRHS‐CNN algorithm during DT. Superior results are attained by the proposed methods analogized to existing research methods.

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A Novel Hybrid Intrusion Detection System (IDS) for the Detection of Internet of Things (IoT) Network Attacks

Cited by 25 publications

References 26 publications

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

An Efficient Intrusion Detection Approach Using Ensemble Deep Learning models for IoT

A secure data transmission and efficient data balancing approach for 5G‐based IoT data using UUDIS‐ECC and LSRHS‐CNN algorithms

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