Comparison of Machine Learning Algorithms for Detection of Network Intrusions

Li, Zhida; Batta, Prerna; Trajković, Ljiljana

doi:10.1109/smc.2018.00719

Cited by 27 publications

(4 citation statements)

References 16 publications

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“…Li et al [27] applied recurrent neural network and BLS learning algorithms to classify known network intrusions. Experimental results indicate that BLS achieved comparable performance and shorter training time because of their wide and deep structure.…”

Section: Nids Based On Blsmentioning

confidence: 99%

Network intrusion detection via tri-broad learning system based on spatial-temporal granularity

Zhang

Liu

et al. 2023

J Supercomput

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Network intrusion detection system plays a crucial role in protecting the integrity and availability of sensitive assets. Network traffic data contains a large amount of time, space, and statistical information. Existing research lacks the utilization of spatial-temporal multi-granularity data features and the mutual support among different data features, thus making it difficult to specifically and accurately identify anomalies. Taking into account the distinctions among different granularities, we propose a framework called Tri-Broad Learning System (TBLS), which can learn and integrate the three granular features. In order to accurately explore the spatial-temporal connotation of the traffic information, a feature dataset containing three granularities is constructed according to the characteristics of time, space, and data content. In this way, we use broad learning basic units to extract abstract features of different granularities, and then express these features in different feature spaces to enhance them separately. We use the "He" instead of the original initialization method in BLS to initialize the weights of feature nodes and enhancement nodes to achieve better detection accuracy. We exhibit the merits of our proposed model on the UNSW-NB15, CIC-IDS-2017, and mixed traffic datasets. Experimental JOURNAL OF SUPERCOMPUTING results show that TBLS outperforms the typical BLS in terms of various evaluation metrics and time consumption. Compared with other machine learning methods, TBLS achieves better performance metrics.

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Section: Nids Based On Blsmentioning

confidence: 99%

Network intrusion detection via tri-broad learning system based on spatial-temporal granularity

Zhang

Liu

et al. 2023

J Supercomput

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“…For both of the datasets, BGRU+MLP provides the highest detection rate of 99.84% and 99.24%, respectively. Li et al [24] implemented LSTM, GRU, Bi-LSTM, and Broad Learning System (BLS) algorithms on the NSL-KDD dataset for various known intrusion classification. The performance analysis determines that the BLS reduces the model training time with an overall accuracy of 84.15% and 72.64%, corresponding to KDDTest+ and KDDTest-21 datasets.…”

Section: State-of-the-artmentioning

confidence: 99%

Towards Machine Learning Based Intrusion Detection in IoT Networks

Islam¹,

Farhin²,

Sultana³

et al. 2021

Computers, Materials &Amp; Continua

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The Internet of Things (IoT) integrates billions of self-organized and heterogeneous smart nodes that communicate with each other without human intervention. In recent years, IoT based systems have been used in improving the experience in many applications including healthcare, agriculture, supply chain, education, transportation and traffic monitoring, utility services etc. However, node heterogeneity raised security concern which is one of the most complicated issues on the IoT. Implementing security measures, including encryption, access control, and authentication for the IoT devices are ineffective in achieving security. In this paper, we identified various types of IoT threats and shallow (such as decision tree (DT), random forest (RF), support vector machine (SVM)) as well as deep machine learning (deep neural network (DNN), deep belief network (DBN), long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM)) based intrusion detection systems (IDS) in the IoT environment have been discussed. The performance of these models has been evaluated using five benchmark datasets such as NSL-KDD, IoTDevNet, DS2OS, IoTID20, and IoT Botnet dataset. The various performance metrics such as Accuracy, Precision, Recall, F1-score were used to evaluate the performance of shallow/deep machine learning based IDS. It has been found that deep machine learning IDS outperforms shallow machine learning in detecting IoT attacks.

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“…Rapid developments in the field of artificial intelligence and deep learning have led to a wide variety of algorithms capable of detecting anomalies in the flow of network traffic and even classify certain attacks present within those network flows. Proposed algorithms in the literature include recurrent neural networks (RNNs) [8], convolutional neural networks (CNNs) [12] [7], support-vector machines (SVNs) [17] and broad learning systems (BLSs) [13] [9]. An additional challenge, that all of those approaches have in common, is the fact that network attacks are ordinarily rather rare events and that undetected attacks can lead to great costs.…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Shallow Neural Network With Improved False Negative Rate for Cyber Intrusion Detection Systems

Ehmer¹,

Granado²,

Denoulet³

et al. 2022

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)

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Economic value creation increasingly takes place online or is tightly coupled to some kind of online service. At the same time, malicious network activities are causing growing losses in the strongly digitalized economies. Hence, protecting network communication infrastructure is an important challenge for companies and public institutions alike. Machine learning algorithm based network intrusion detection systems (NIDS) are often used to detect sophisticated attack patterns. However, the detection quality of those algorithms suffers greatly from the imbalanced nature of network flow data. Undetected attacks can cause great damage, so it is essential that a NIDS performs at its best in order to detect as many attacks as possible. In our article, we propose an improved loss function in order to reduce the number of false negatives produced by an artificial neural network (ANN). Based on the CIC-IDS17 dataset, we show that our proposed algorithm running on a shallow neural network (single layer with 110 neurons) successfully classifies a variety of recent network attacks with a F1-score above 99%.

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Comparison of Machine Learning Algorithms for Detection of Network Intrusions

Cited by 27 publications

References 16 publications

Network intrusion detection via tri-broad learning system based on spatial-temporal granularity

Network intrusion detection via tri-broad learning system based on spatial-temporal granularity

Towards Machine Learning Based Intrusion Detection in IoT Networks

Low Complexity Shallow Neural Network With Improved False Negative Rate for Cyber Intrusion Detection Systems

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