Intrusion Detection In IoT Using Artificial Neural Networks On UNSW-15 Dataset

Hanif, Sohaib; Ilyas, Tuba; Zeeshan, Muhammad

doi:10.1109/honet.2019.8908122

Cited by 62 publications

(31 citation statements)

References 16 publications

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“…Content may change prior to final publication. higher than the method in [33] and 3.71% greater than TAC obtained in [26].In the case of the multiclass classification process, the GA-ET-g5 obtained a TAC that is 5.11% greater than the TAC obtained in [33] and 1.87% higher than the TAC obtained in [34]. Furthermore, the methods that were proposed in this research were superior to the DL-based algorithms that were reviewed in the literature.…”

Section: B Experimental Results and Discussionmentioning

confidence: 63%

“…Content may change prior to final publication. [25] 76.1% 65.1% ANN [26] 83.9% -GA-SVM [29] 86.38% -GWO-SVM [29] 84.48% -FFA-SVM [29] 85.42% -IG-TS [31] 85.78% -GA-LR-DT [32] 81.42% -XGBoost-LR [33] 75.51% 72.53% SVM-NIDS [34] 85.99% 75.77% IG-Tree [35] 84.83% -Deep learning -LSTM [36] 85.42% -Deep learning -LSTM [37] 80.72% 72.26% Deep learning -CNN-RNN [38] 86.64% -GA -RF [39] 86.70% -GA -RF [40] -64.23% GA -RF (Proposed) 87.61% -GA -ET (Proposed) -77.64%…”

Section: Discussionmentioning

confidence: 99%

“…Hanif et al [26] presented an IDS for IoT networks using artificial neural networks (ANN). This system was implemented to overcome the issue of security that is a major concern in IoT networks.…”

Section: Related Workmentioning

confidence: 99%

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An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Kasongo

2021

IEEE Access

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The evolution of the Internet and cloud-based technologies have empowered several organizations with the capacity to implement large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). The IoT and, by virtue, the IIoT, are vulnerable to new types of threats and intrusions because of the nature of their networks. So it is crucial to develop Intrusion Detection Systems (IDSs) that can provide the security, privacy, and integrity of IIoT networks. In this research, we propose an IDS for IIoT that was implemented using the Genetic Algorithm (GA) for feature selection, and the Random Forest (RF) model was employed in the GA fitness function. The models used for the intrusion detection processes include classifiers such as the RF, Linear Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Extra-Trees (ET), and Extreme Gradient Boosting (XGB). The GA-RF generated 10 feature vectors for the binary classification scheme and seven feature vectors for the multiclass classification procedure. The UNSW-NB15 is used to assess the effectiveness and the robustness of our proposed approach. The experimental outcomes demonstrated that for the binary modeling process, the GA-RF achieved a test accuracy (TAC) of 87.61% and an Area Under the Curve (AUC) of 0.98, using a feature vector that contained 16 features. These results were superior to existing IDS frameworks.

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Section: B Experimental Results and Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

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An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Kasongo

2021

IEEE Access

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“…These include the solutions proposed in [13,18], which introduced Support Vector Machine (SVM) models for IoT intrusion detection. The Artificial Neural Network (ANN) method was also adopted in various IDSs proposals to secure IoT resources against different attacks [6,[19][20][21]. Other machine learning methods such as Naïve Bayesian [5,22], random forest [23][24], optimum-path forest [25], and logistic regression [13] were also considered.…”

Section: Related Workmentioning

confidence: 99%

“…For example, NSL-KKD was considered in [2, 17, 20-21, 24, 27, 37] to develop different machine learning and deep learning-based IDSs. UNSW-NB15 was also a common dataset among many IoT-oriented IDS solutions [19,22,28,[38][39]. Other examples also include CICID2017 [40] which contains traces for network flows and was utilized to build different machine learning IDSs in [41][42].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Botnet Detection Approach in Modern Applications

Alheeti

Alsukayti

Alreshoodi

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>

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Video Analytics Using Deep Learning Models

Bhatt,

Srinivasan

2024

Reshaping Intelligent Business and Industry

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Intrusion Detection In IoT Using Artificial Neural Networks On UNSW-15 Dataset

Cited by 62 publications

References 16 publications

An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms

Intelligent Botnet Detection Approach in Modern Applications

Video Analytics Using Deep Learning Models

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