Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection

Ma, Chencheng; Du, Xuehui; Cao, Lifeng

doi:10.1109/access.2019.2946708

Cited by 45 publications

(17 citation statements)

References 39 publications

(39 reference statements)

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“…The accuracy over the CIC-IDS2017 dataset is more than 90%. The other algorithms include DeepDetect [40], a deep learning combination method [41] and AD-H1CD [42]. In Figure 15, the model proposed in this paper achieves the highest accuracy of 99.91%.…”

Section: The Results Of Multi-target Anomaly Classificationmentioning

confidence: 95%

A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning

Liu

Wang

2021

IEEE Access

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Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an intrusion detection model that combines machine learning with deep learning is proposed in this paper. The model uses the kmeans and the random forest (RF) algorithms for the binary classification, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events. Then, by using the convolutional neural network (CNN), long short-term memory (LSTM), and other deep learning algorithms, the events judged as abnormal are further classified into different attack types finally. At this stage, adaptive synthetic sampling (ADASYN) is adopted to solve the unbalanced dataset. The NSL-KDD and CIS-IDS2017 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed model has better TPR for most of attack events, faster data preprocessing speed, and potentially less training time. In particular, the accuracy of multi-target classification can reach as high as 85.24% in the NSL-KDD dataset and 99.91% in the CIC-IDS2017 dataset.INDEX TERMS Intrusion detection system, machine learning algorithm, k-means, random forest, deep learning algorithm.

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Section: The Results Of Multi-target Anomaly Classificationmentioning

confidence: 95%

A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning

Liu

Wang

2021

IEEE Access

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“…Wang et al [51] transformed two packets into an image and used the 2D-CNN to learn the characteristics of packet bytes while using the LSTM to learn the characteristics of the packet sequence, resulting in the simultaneous learning of two spacing and timing characteristics. Chencheng et al [53] developed a hybrid ID system for the evaluation of multiple types of flow features using a hybrid NN and tested the efficacy of the proposed hybrid ID system in real-time using the ISCX2012 ID dataset. Zeng et al [54] used a hybrid NN with a stack autoencoder (SAE) to evaluate the traffic features and chose the best feature vectors from the network traffic as label results.…”

Section: Related Workmentioning

confidence: 99%

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Khan

2021

Processes

161

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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“…The best achieved overall accuracy for the multiclassifier changes for the two used datasets (87.3% and 93.57%, respectively). In [22], a Hybrid Neural Network approach is proposed and evaluated on two datasets. Similar to [21], the model was tested for multiclassification and binary classification.…”

Section: Related Workmentioning

confidence: 99%

Effective Anomaly Detection Using Deep Learning in IoT Systems

Bernardi

Cimitile

Pecori

et al. 2021

Wireless Communications and Mobile Computing

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Anomaly detection in network traffic is a hot and ongoing research theme especially when concerning IoT devices, which are quickly spreading throughout various situations of people’s life and, at the same time, prone to be attacked through different weak points. In this paper, we tackle the emerging anomaly detection problem in IoT, by integrating five different datasets of abnormal IoT traffic and evaluating them with a deep learning approach capable of identifying both normal and malicious IoT traffic as well as different types of anomalies. The large integrated dataset is aimed at providing a realistic and still missing benchmark for IoT normal and abnormal traffic, with data coming from different IoT scenarios. Moreover, the deep learning approach has been enriched through a proper hyperparameter optimization phase, a feature reduction phase by using an autoencoder neural network, and a study of the robustness of the best considered deep neural networks in situations affected by Gaussian noise over some of the considered features. The obtained results demonstrate the effectiveness of the created IoT dataset for anomaly detection using deep learning techniques, also in a noisy scenario.

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Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection

Cited by 45 publications

References 39 publications

A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning

A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Effective Anomaly Detection Using Deep Learning in IoT Systems

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