Design and Development of RNN Anomaly Detection Model for IoT Networks

Ullah, Imtiaz; Mahmoud, Qusay H.

doi:10.1109/access.2022.3176317

Cited by 107 publications

(25 citation statements)

References 91 publications

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“…The comparative evaluation test is used to estimate the accuracy, precision, recall, and F-measure of the CNN-LSTM IDS against related IDSs for detecting the DDoS attack in the Cloud environment. The CNN-LSTM IDS is compared with the CNN-IDS [59], the RNN-IDS [60], the LSTM-IDS [61], and the LigthGBM-IDS [62], as they are used as benchmark models due to their comparable performance. Table 3 shows the evaluation metrics of CNN-LSTM IDS and state-of-the-art IDSs.…”

Section: Results and Findingsmentioning

confidence: 99%

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Sanjalawe¹,

Althobaiti²

2023

Computers, Materials &Amp; Continua

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Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper, a hybrid deep learning mode based on hybridizing Convolutional Neural Network (CNN) with Long-Short-Term Memory (LSTM) is used due to its robustness and efficiency in detecting normal and attack traffic. Besides, the ensemble feature selection, mutualization aggregation between Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (PSO), Krill Hird (KH), and Whale Optimization Algorithm (WOA), is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC. A benchmark dataset proposed by the Canadian Institute of Cybersecurity (CIC), called CICIDS 2017 is used to evaluate the proposed IDS. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 97.9%, 98.3%, 97.9%, 98.1%, respectively. As a result, the proposed IDS achieves the requirements of getting high security, automatic, efficient, and self-decision detection of DDoS attacks.

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Section: Results and Findingsmentioning

confidence: 99%

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Sanjalawe¹,

Althobaiti²

2023

Computers, Materials &Amp; Continua

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“…By using a number of parameters and open-source datasets, the performance and outcomes of the suggested ROAST-IOT framework are validated in this part. In this work, system validation and performance evaluation have been carried out using some of the more recent and well-liked benchmarking datasets [37][38][39], including ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. These are the emerging and modern datasets used to improve the security of IoT networks, which comprises the recent classes and types of intrusions.…”

Section: Resultsmentioning

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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“…Ullah et al [37] proposed a deep learning model approach for detecting anomalous behavior in IoT networks. [42] also designed a proactive collaborative malware detection system.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Deep Learning Approaches for Anomaly Detection in Internet of Things Systems

2023

IRJMETS

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The utilization of Internet of Things (IoT) technology has become widespread across various industries and government services. Regrettably, IoT devices and networks are known for their susceptibility to security attacks that threaten data integrity and service reliability. The diversity of data collected from different IoT devices, combined with disruptions within the IoT system, make the identification of anomalous behavior and compromised nodes more challenging compared to traditional Information Technology (IT) networks.To address this issue, there is an urgent need for a reliable and efficient method for anomaly detection to prevent malicious data from being used in IoT-based decision support systems. In this study, we present a deep learning-based solution for anomaly detection in IoT systems. The proposed deep learning model is capable of learning and capturing features that are robust against unstable conditions and can provide useful information even in the presence of heterogeneity.The deep learning model is designed based on a denoising autoencoder and aims to obtain features that are resistant to the diverse environment of IoT. Empirical results, based on real-world IoT datasets, demonstrate the efficacy of the proposed framework in enhancing the accuracy of detecting malicious data compared to the state-of-the-art anomaly detection models in IoT systems.

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Design and Development of RNN Anomaly Detection Model for IoT Networks

Cited by 107 publications

References 91 publications

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

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

Deep Learning Approaches for Anomaly Detection in Internet of Things Systems

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