Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm

Asgharzadeh, Hossein; Ghaffari, Ali; Màsdàrí, Mohàmmàd; Gharehchopogh, Farhad Soleimanian

doi:10.1016/j.jpdc.2022.12.009

Cited by 39 publications

(5 citation statements)

References 59 publications

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“…During comparison experiments, we compare the proposed MSCBL-ADN model with RF 18 , SVM 19 and KNN 20 shallow classifiers on testing set. We also compare it with CNN-based models (CNN-BMECapSA-RF 25 , LRDADF 27 ), LSTM-based models (VLSTM 30 ), and CNN-LSTM-based models (AsyncFL-bLAM 16 , NIDS-CNN-LSTM 33 ) during training and testing phase.…”

Section: Performance Comparison Experimentsmentioning

confidence: 99%

“…10, it displays the validation accuracy and training loss of multi-class scenario during 100 epochs. We can obviously see that the loss curve of CNN-based CNN-BMECapSA-RF 25 As known, we merge the hulk and benign samples as normal sub-flows. As a result, VLSTM 30 model spends lots of epochs to learn time relationship repressions.…”

Section: Performance Comparison Experimentsmentioning

confidence: 99%

“…The FSL-SCNN encoding network was helpful to optimize feature representations and enhance the efficiency of the training process. Asgharzadeh et al 25 worked out CNN-BMECapSA-RF model based on CNN and hybrid layers to enhance classify accuracy, and developed a binary multi-objective enhanced capuchin search algorithm to implement feature extraction automatically. Ren et al 26 proposed a novel CANET network.…”

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confidence: 99%

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A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

Yin,

Fang,

Liu

et al. 2024

Sci Rep

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Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.

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Section: Performance Comparison Experimentsmentioning

confidence: 99%

Section: Performance Comparison Experimentsmentioning

confidence: 99%

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confidence: 99%

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A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

Yin,

Fang,

Liu

et al. 2024

Sci Rep

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“…Asgharzadeh et al [20] presented an IoT Feature Extraction Convolutional Neural Network (IoTFECNN) with hybrid layers to extract both low-level and high-level characteristics and identify IoT anomalies from TON-IoT and NSL-KDD datasets. For effective feature selection, the Binary Multi-objective Enhanced Capuchin Search Algorithm (BME-CapSA) was developed.…”

Section: Related Workmentioning

confidence: 99%

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

Chalichalamala,

Govindan,

Kasarapu

2023

Sensors

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The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.

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“…Researchers have explored various solutions, including conventional cryptography tools, shallow machine learning (ML) algorithms, and deep learning (DL) algorithms, to address the challenges posed by attacks on IoT networks. Among these solutions, convolutional neural networks (CNNs) [ 8 ] have emerged as a promising approach to detecting IoT-based attacks. However, further research is needed to investigate the applicability of CNNs in selecting significant features that aid in detecting attacks on IoT networks.…”

Section: Introductionmentioning

confidence: 99%

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

Alabsi

Anbar

Rihan

2023

Sensors

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The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic. The second CNN utilizes the features identified by the first CNN to build a robust detection model that accurately detects IoT attacks. The proposed approach is evaluated using the BoT IoT 2020 dataset. The results reveal that the proposed approach achieves 98.04% detection accuracy, 98.09% precision, 99.85% recall, 98.96% recall, and a 1.93% false positive rate (FPR). Furthermore, the proposed approach is compared with other deep learning algorithms and feature selection methods; the results show that it outperforms these algorithms.

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Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm

Cited by 39 publications

References 59 publications

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection

Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

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