Classification model for accuracy and intrusion detection using machine learning approach

Agarwal, Arushi; Sharma, Purushottam; Alshehri, Mohammed; Mohamed, Ahmed A.; Alfarraj, Osama

doi:10.7717/peerj-cs.437

Cited by 42 publications

(14 citation statements)

References 25 publications

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“…Kilincer et al [21] provided a detailed review of datasets commonly used in the field of anomaly network traffic detection and examined traditional machine learning methods compared to each other. To further improve the detection accuracy and the processing time of the algorithm on the dataset, Agarwal et al [22] proposed an integrated approach that integrates multiple machine learning methods to improve detection accuracy. Because most machine learning-based anomaly network traffic detection methods nowadays rarely consider data quality, Gu and Lu [23] proposed an effective intrusion detection framework-based SVM with plain Bayesian feature embedding.…”

Section: Methods Based On Machinementioning

confidence: 99%

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Ming

Han

Yin

et al. 2021

Security and Communication Networks

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With the rapid development and widespread application of cloud computing, cloud computing open networks and service sharing scenarios have become more complex and changeable, causing security challenges to become more severe. As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.

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Section: Methods Based On Machinementioning

confidence: 99%

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Ming

Han

Yin

et al. 2021

Security and Communication Networks

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“…Hybrid models have been recommended by Bhattacharya et al (2020) and Agrawal et al (2019); the former is a machine-learning model based on principal component analysis for the classification of intrusion detection system datasets, while the latter is a hybrid ensemble intrusion detection system for anomaly detection using different datasets to detect patterns in network traffic that deviate from normal behaviour. Agarwal et al (2021) used three different machine learning algorithms in their research to find the most suitable for efficiently identifying patterns of suspicious network activity. The UNSW-NB15 dataset was used for this purpose.…”

Section: General Intrusion Detectionmentioning

confidence: 99%

Cyber risk and cybersecurity: a systematic review of data availability

Cremer

Sheehan

Fortmann

et al. 2022

Geneva Pap Risk Insur Issues Pract

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Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

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“…Meanwhile, D'angelo et al [32] proposed an ML model for classification doubling that achieved 94.1% accuracy. Finally, Bru et al [33] used DNNs to detect attacks within the home environments connected to the internet of things similar to [34], [35]. Table 2 summarizes the relevant research that has been conducted on the subject of detecting suspicious behavior in IoT through the use of ML techniques.…”

Section: Previous Studiesmentioning

confidence: 99%

Using deep learning to detecting abnormal behavior in internet of things

Al-Shabi

Abuhamdah

2022

IJECE

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<span lang="EN-US">The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.</span>

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Classification model for accuracy and intrusion detection using machine learning approach

Cited by 42 publications

References 25 publications

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features

Cyber risk and cybersecurity: a systematic review of data availability

Using deep learning to detecting abnormal behavior in internet of things

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