Anomaly based network intrusion detection for IoT attacks using deep learning technique

Sharma, Bhawana; Sharma, Lokesh; Lal, Chhagan; Roy, Satyabrata

doi:10.1016/j.compeleceng.2023.108626

Cited by 93 publications

(25 citation statements)

References 26 publications

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“…( 2). Subsequently, the revised population position is determined based on two criteria (Criteria I and Criteria II), (47,48,49) which are listed below:…”

Section: Feature Selection Using Bscmmentioning

confidence: 99%

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Lavanya,

Anushiya

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Federated Learning (FL) has established as a potentially effective practice for cyberattack identification in the last decade, particularly for Internet-of-Things (IoT) structures. FL can increase learning effectiveness, lower transmission overheads, and enhance intrusion detection system (IDS) privacy by spreading the learning process amongst IoT gateways. The absence of labeled data and the distinction of data features for training pose significant obstacles to the deployment of FL in IoT networks. In this research, suggest an Autoencoder based Deep Federated Transfer Learning (ADFTL) to conquer these obstacles. Specifically, Create an ADFTL model utilizing two AutoEncoders (AEs) as the basis. Initially the supervised mode is employed to train the first AE (AE1) on the source datasets while the unsupervised mode is employed to train the second AE (AE2) on the target datasets without label information. The bottleneck layer, or latent representation, of AE2 is forced via the transfer learning method in an effort to resemble the latent representation of AE1. Subsequently, assaults in the input in the target domain are identified employing the latent representation of AE2. Particularly, Weighted k-Subspace Network (WkSNC) clustering is proposed for clustering the dataset and Boosted Sine Cos method (BSCM) is used for feature selection. The requirement that the network datasets utilized in current studies have identical properties is significant since it restricts the effectiveness, adaptability, and scalability of IDS. Nonetheless, the suggested structure can tackle these issues by sharing the "knowledge" of learning among distinct deep learning (DL) simulations, even in cases when their datasets possess dissimilar features. Comprehensive tests on current BoT-IoT datasets demonstrate that the suggested structure can outperform the most advanced DL-based methods by more than 6%

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“…( 2). Subsequently, the revised population position is determined based on two criteria (Criteria I and Criteria II), (47,48,49) which are listed below:…”

Section: Feature Selection Using Bscmmentioning

confidence: 99%

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Lavanya,

Anushiya

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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“…Altunay [38], A deep learning architecture combining CNN and LSTM was introduced to detect intrusions in IoT networks, and the accurate detection success rate for attack types in the dataset was effectively evaluated. Additionally, Sharma et al [39] A filter-based feature selection deep neural network model was proposed, which removed highly correlated features and generated a few attack data using GANs to balance the dataset.…”

Section: Intrusion Detection Modelmentioning

confidence: 99%

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Jiang,

Fu,

Wang

2024

IEEE Access

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In today's network environments vulnerable to cyber threats like hackers and viruses, intrusion detection technology is considered the most effective means of detection and defense. Deep neural networks are commonly used in intrusion detection technology. However, improving the model's ability to extract feature information and reducing computational space while retaining local feature information are critical challenges that need to be addressed.To tackle these issues, this paper proposes a model named BBO-CFAT, which combines the Biogeography-Based Optimization algorithm (BBO) for feature selection and an improved Transformer model for preserving context information and reducing computational space. Specifically, the BBO-CFAT model employs a roulette selection method to control the operations of migration and mutation operators. It utilizes feature information entropy to weight updates of adaptive variables in these operators, thereby enhancing the credibility of feature selection. Furthermore, the Transformer framework is hierarchically designed to facilitate the acquisition of context information. Additionally, depthwise separable convolutions are employed to reduce computational space, thereby improving computational efficiency and training speed.Experimental evaluations using the CIC-IDS2017 and NSL-KDD datasets demonstrate promising accuracies for BBO-CFAT on both datasets, achieving 99.1% and 97.5% accuracy, respectively, surpassing the performance of comparative experiments.Overall, the BBO-CFAT model provides a comprehensive solution to the challenges of intrusion detection, effectively balancing feature preservation, computational efficiency, and training accuracy.

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“…In the context of securing IoT networks, a novel anomaly-based intrusion-detection system (IDS) leveraging deep learning techniques is proposed by [21]. The system employs a filter-based deep neural network (DNN) model with feature selection, dropping highly correlated features.…”

Section: Anomaly-based Network-intrusion Detection For Iot Attacks Us...mentioning

confidence: 99%

Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

Mahmood,

Hasan,

Yahaya

et al. 2024

Knowledge

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This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

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Anomaly based network intrusion detection for IoT attacks using deep learning technique

Cited by 93 publications

References 26 publications

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

BBO-CFAT: Network Intrusion Detection Model Based on BBO Algorithm and Hierarchical Transformer

Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment

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