A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks

Ullah, Imtiaz; Mahmoud, Qusay H.

doi:10.1007/978-3-030-47358-7_52

Cited by 165 publications

(145 citation statements)

References 10 publications

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“…However, these IoT datasets lack the novelty attack techniques that emerged in recent years and have an insufficient number of features. Therefore, modern datasets are presented to solve such problem like IoTID20 [ 26 ] and LITNET-2020 [ 27 ]. On one hand, the IoTID20 dataset was collected from different sources namely, laptops, smartphones, smart home devices, tablets, and so on.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

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Section: Introductionmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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“…The IoTID20 dataset [29] contains intrusion and normal activities generated from laptops, tablets, and smartphone devices in a smart home IoT network with a Wi-Fi router connected to SKT NGU device and EZVIZ camera. The dataset includes 80 features and 625,783 instances.…”

Section: Iot Imbalanced Datasetmentioning

confidence: 99%

“…Besides, available IoT intrusion datasets lack a large number of features. Thus, recent datasets are introduced such as LITNET-2020 [28] and IoTID20 [29]. The LITNET-2020 dataset was collected from the KTU LITNET network to present the normal and attack network traffic, while the data gathered for IoTID20 were generated from various sources such as smartphones, laptops, tablets, and smart home devices.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

et al. 2021

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Intrusion detection of IoT-based data is a hot topic and has received a lot of interests from researchers and practitioners since the security of IoT networks is crucial. Both supervised and unsupervised learning methods are used for intrusion detection of IoT networks. This paper proposes an approach of three stages considering a clustering with reduction stage, an oversampling stage, and a classification by a Single Hidden Layer Feed-Forward Neural Network (SLFN) stage. The novelty of the paper resides in the technique of data reduction and data oversampling for generating useful and balanced training data and the hybrid consideration of the unsupervised and supervised methods for detecting the intrusion activities. The experiments were evaluated in terms of accuracy, precision, recall, and G-mean and divided into four steps: measuring the effect of the data reduction with clustering, the evaluation of the framework with basic classifiers, the effect of the oversampling technique, and a comparison with basic classifiers. The results show that SLFN classification technique and the choice of Support Vector Machine and Synthetic Minority Oversampling Technique (SVM-SMOTE) with a ratio of 0.9 and the k value of 3 for k-means++ clustering technique give better results than other values and other classification techniques.

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“…(['Flow_ID', 'Src_IP', 'Src_Port', 'Dst_IP', 'Dst_Port', 'Protocol', 'Timestamp', 'Flow_Duration', 'Tot_Fwd_Pkts', 'Tot_Bwd_Pkts', 'TotLen_Fwd_Pkts', 'TotLen_Bwd_Pkts', 'Fwd_Pkt_Len_Max', 'Fwd_Pkt_Len_Min', 'Fwd_Pkt_Len_Mean', 'Fwd_Pkt_Len_Std', 'Bwd_Pkt_Len_Max', 'Bwd_Pkt_Len_Min', 'Bwd_Pkt_Len_Mean', 'Bwd_Pkt_Len_Std', 'Flow_Byts/s', 'Flow_Pkts/s', 'Flow_IAT_Mean', 'Flow_IAT_Std', 'Flow_IAT_Max', 'Flow_IAT_Min', 'Fwd_IAT_Tot', 'Fwd_IAT_Mean', 'Bwd_IAT_Mean', 'Fwd_IAT_Max', 'Fwd_IAT_Min', 'Bwd_IAT_Tot', 'Bwd_IAT_Mean.1', 'Bwd_IAT_Std', 'Bwd_IAT_Max', 'Bwd_IAT_Min', 'Fwd_PSH_Flags', 'Bwd_PSH_Flags', 'Fwd_URG_Flags', 'Bwd_URG_Flags', 'Fwd_Header_Len', 'Bwd_Header_Len', 'Fwd_Pkts/s', 'Bwd_Pkts/s', 'Pkt_Len_Min', 'Pkt_Len_Max', 'Pkt_Len_Mean', 'Pkt_Len_Std', 'Pkt_Len_Var', 'FIN_Flag_Cnt', 'SYN_Flag_Cnt', 'RST_Flag_Cnt', 'PSH_Flag_Cnt', 'ACK_Flag_Cnt', 'URG_Flag_Cnt', 'CWE_Flag_Count', 'ECE_Flag_Cnt', 'Down/Up_Ratio', 'Pkt_Size_Avg', 'Fwd_Seg_Size_Avg', 'Bwd_Seg_Size_Avg', 'Fwd_Byts/b_Avg', 'Fwd_Pkts/b_Avg', 'Fwd_Blk_Rate_Avg', 'Bwd_Byts/b_Avg', 'Bwd_Pkts/b_Avg', 'Bwd_Blk_Rate_Avg', 'Sub ow_Fwd_Pkts', 'Sub ow_Fwd_Byts', 'Sub ow_Bwd_Pkts', 'Sub ow_Bwd_Byts', 'Init_Fwd_Win_Byts', 'Init_Bwd_Win_Byts', 'Fwd_Act_Data_Pkts', 'Fwd_Seg_Size_Min', 'Active_Mean', 'Active_Std', 'Active_Max', 'Active_Min', 'Idle_Mean', 'Idle_Std', 'Idle_Max', 'Idle_Min', 'Label', 'Cat', 'Sub_Cat']) [11]. Among these three target variables, one is 'Label', another is 'Category' and the third target feature is 'Sub_Category'.…”

Section: Exploratory Data Analysis Of An Iot-23 Datasetmentioning

confidence: 99%

Artificial Intelligence for anomaly detection by employing deep learning strategies in IoT networks using the trendy IoT-23 big data from Google's Tensorflow2.2

Kanimozhi

Jacob

2021

Preprint

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Although numerous profound learning models have been proposed, this research article contributed to symbolize the investigation of artificial deep learning models on sensible IoT gadgets to perform online protection in IoT network traffic by using the realistic IoT-23 dataset. This dataset is a recent network traffic dataset generated from the real-time network traffic data of IoT appliances. IoT products are utilized in various program applications such as home, commercial, mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spots of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. Henceforth, IoT anomaly detection systems based on the realistic Iot-23 big data for detecting IoT-based attacks on the artificial neural networks of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Multilayer perceptron (MLP) in IoT- cybersecurity has implemented and executed in this research article. As a result, Convolutional Neural Networks produces an outstanding performance of metric accuracy score is 0.998234, and minimal loss function is 0.008842, compare to Multilayer perceptron and Recurrent Neural Networks in IoT Anomaly Detection. Also generated well-displayed graph plots of Model_Accuracy, Learning curve of artificial Intelligence deep learning algorithms such as MLP, CNN, and RNN.

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A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks

Cited by 165 publications

References 10 publications

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Stage Classification Approach for IoT Intrusion Detection Based on Clustering with Oversampling

Artificial Intelligence for anomaly detection by employing deep learning strategies in IoT networks using the trendy IoT-23 big data from Google's Tensorflow2.2

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