An intelligent ensemble of <scp>long</scp>‐short<scp>‐term</scp> memory with genetic algorithm for network anomaly identification

Thaseen, I. Sumaiya; Chitturi, Arun Krishna; Al‐Turjman, Fadi; Shankar, Achyut; Ghalib, Muhammad Rukunuddin; Abhishek, Kumar

doi:10.1002/ett.4149

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…Lastly, the answer to the third research question which is the comparative analysis of the proposed method against the existing method is provided. The published sophisticated genetic algorithm and deep-learning method [19] reported a 95.23% overall accuracy for detecting generic attack with feature selection. This reported performance [19] which is lower compared to the overall accuracy for the said method for multi-classification, is also lower than the performance of this study"s proposed methods for generic network intrusion detection.…”

Section: Comparative Analysis With Existing Methodsmentioning

confidence: 99%

“…The research work of [19] presented an ensemble of sophisticated deep learning algorithms for detecting different types of network anomalies. The study implemented a majority voting ensemble of three hyper-parameter long-short-term memory deep neural network with an embedded feature extraction module.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Detection Rate), FP Rate (i.e. False Alarm Rate), Precision, Recall, F-Measure, Matthews Correlation Coefficient (MCC), Area Under Curve (AUC) [2], [10], [19], [27]- [30]. Also, the overall accuracy (i.e.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…For emphasis, the MCC metric is arguably the prime metric for evaluating a binary classification as it based on all four values of the confusion matrix [19], [31]. It reveals the correlation coefficient among the detected and expected predictions, having a value ranging from 0 to 1 [30].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

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Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Alsariera¹

2021

IJACSA

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The development Intrusion Detection System (IDS) has a solid impact in mitigating against internal and external cyber threats among other cybersecurity methods. The machine learning-based method for IDS has proven to be an effective approach to detecting either anomaly or multiple classes of intrusion. For the detection of various types of intrusion by a single IDS model, it is discovered that the overall high accuracy of the IDS model does not translate to high accuracy for each attack type. Some intrusion attacks are seen to share similarities with other attacks thereby evading detection, one of which is the generic attack. The notoriety of the generic attack is the ability of a single generic attack to compromise a whole bunch of blockciphers. Therefore, this study proposed a machine learning framework to specifically detect generic network intrusion by implementing two (2) decision tree algorithms. The decision tree methods were developed using two distinct variants namely the J48 and Random Tree algorithms. A balanced generic network dataset was curated and used for model development. A 10-fold cross-validation technique was implemented for model development and performance evaluation, where all obtainable performance scores were extracted and presented. The performances of the decision tree methods for generic network intrusion attack detection were comparative analysis and also evaluated against existing methods. The proposed methods of this study are robust, stable and empirically seen to have outperformed existing methods.

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Section: Comparative Analysis With Existing Methodsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

confidence: 99%

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Detecting Generic Network Intrusion Attacks using Tree-based Machine Learning Methods

Alsariera¹

2021

IJACSA

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“…The proposed feature selection component experiments on KDDCUP99, NSL‐KDD, and UNSW‐NB15 datasets. Thaseen et al 26 present a NIDS using LSTM architecture with a genetic algorithm. The attack classifier scored 99.3% accuracy.…”

Section: Related Workmentioning

confidence: 99%

An intrusion detection system using optimized deep neural network architecture

Ramaiah

Vanmathi

Ravi

et al. 2021

Trans Emerging Tel Tech

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Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks.

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