LSTM deep learning method for network intrusion detection system

Boukhalfa, Alaeddine; Abdellaoui, Abderrahim; Hmina, Nabil; Chaoui, Habiba

doi:10.11591/ijece.v10i3.pp3315-3322

Cited by 33 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result of the proposed model was evaluated using well-known standard evaluation metrics for the classification algorithms namely, detection rate (recall) ( ), precision ( ) , F-measure ( ), and accuracy ( ) as calculated in Eqs. (13) -(16) [25][26][27]. Moreover, the Receiver Operating Characteristic (ROC) curve is used to check the performance of the proposed model cost against recall.…”

Section: Resultsmentioning

confidence: 99%

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Hameed¹,

Ali²

2021

IJIES

View full text Add to dashboard Cite

Over the last decade, the usage of short message services (SMS) as one of the vital communication services on mobile devices has grown. The growth of using this service has correspondingly increased the number of attacks on mobile devices such as SMS Spam. SMS spam is a concern to telecommunications service providers as they annoy the subscribers and cause them to loose commercial. Most current researches have attempted to detect SMS spam using different classifiers. In this paper, we propose a new method that focuses on binary particle swarm optimizationbased fuzzy rules selection for detecting SMS spam messages. First, we extract significant features from the SMS spam dataset. Then, a set of fuzzy rules based on the extracted features is generated. Finally, a binary particle swarm is suggested for picking the more powerful fuzzy rules that reduce the complexity and improve the model's performance. The SMS Spam benchmark dataset was used in the experiment. The attained results of the proposed model provide a recall of 98.8%, precision of 90.8%, F-measure of 94.6% and accuracy of 98.5% that indicate the proposed model can be a promising for detecting SMS spam.

show abstract

Section: Resultsmentioning

confidence: 99%

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Hameed¹,

Ali²

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…The strong point of this technique is that all data is used for training and testing, which makes the assessment more precise. We employed 5-fold cross validation to assess our approach, if we increase the N, the number of attacks for some types like R2L and U2R will decrease for each subset, and they may be neglected during processing [25].…”

Section: Validation Methodsmentioning

confidence: 99%

Parallel processing using big data and machine learning techniques for intrusion detection

Boukhalfa

Hmina

Chaoni

2020

IJ-AI

Self Cite

View full text Add to dashboard Cite

Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

show abstract

“…̅ tested with the number of decision trees used as {10, 40, 60, 80, 100}. Besides, we also conduct experiments to compare the RF algorithm with some algorithms of other studies including decision tree (J48) [9,21] and LSTM [21,28] algorithms. In the study [15], the authors have proven that the KNN and logistic regression algorithms both have less efficiency than the decision tree algorithm, so to see the effectiveness of the RF algorithm, we will only compare it with decision tree and LSTM algorithms…”

Section: ̂ (8)mentioning

confidence: 99%

Optimization of network traffic anomaly detection using machine learning

Dam

Lam

2021

IJECE

View full text Add to dashboard Cite

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

show abstract

LSTM deep learning method for network intrusion detection system

Cited by 33 publications

References 19 publications

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Parallel processing using big data and machine learning techniques for intrusion detection

Optimization of network traffic anomaly detection using machine learning

Contact Info

Product

Resources

About