Network intrusion detection model based on neural network feature extraction and PSO-SVM

Chen, Chen; Xu, Xiaodong; Wang, Guanghua; Yang, Lintao

doi:10.1109/icsp54964.2022.9778404

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…The results of a comparative analysis of the proposed MLDN model with the HBM model, the APID model, and models proposed by [5,6], [7], [8], and [9] are shown in Fig. 20 to 22.…”

Section: Resultsmentioning

confidence: 99%

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Srikrishnan¹,

Raaza²,

B³

et al. 2022

IJACSA

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The practise of recognising unauthorised abnormal actions on computer systems is referred to as intrusion detection. The primary goal of an Intrusion Detection System (IDS) is to identify user behaviours as normal or abnormal based on the data they communicate. Firewalls, data encryption, and authentication techniques were all employed in traditional security systems. Current intrusion scenarios, on the other hand, are very complex and capable of readily breaching the security measures provided by previous protection systems. However, current intrusion scenarios are highly sophisticated and are capable of easily breaking the security mechanisms imposed by the traditional protection systems. Detecting intrusions is a challenging aspect especially in networked environments, as the system designed for such a scenario should be able to handle the huge volume and velocity associated with the domain. This research presents three models, APID (Adaptive Parallelized Intrusion Detection), HBM (Heterogeneous Bagging Model) and MLDN (Multi Layered Deep learning Network) that can be used for fast and efficient detection of intrusions in networked environments. The deep learning model has been constructed using the Keras library. The training data is preprocessed and segregated to fit the processing architecture of neural networks. The network is constructed with multiple layers and the other required parameters for the network are set in accordance with the input data. The trained model is validated using the validation data that has been specifically segregated for this purpose.

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“…The results of a comparative analysis of the proposed MLDN model with the HBM model, the APID model, and models proposed by [5,6], [7], [8], and [9] are shown in Fig. 20 to 22.…”

Section: Resultsmentioning

confidence: 99%

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Srikrishnan¹,

Raaza²,

B³

et al. 2022

IJACSA

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“…Additionally, it demonstrates enhanced learning speed when processing a large volume of data. Similar research using the feature selection technique was conducted by Chen et al [23]. The research applied CNN to extract the essential features of the dataset, and PSO was used to optimize the SVM parameters as the classifiers.…”

Section: Related Workmentioning

confidence: 97%

“…Several approaches implement the common machine learning methods, such as random forest, support vector machine, and decision tree [20,21]. Besides, the other research focuses on reducing the dimensionality of features in the dataset using several feature selection techniques such as particle swarm optimization, Chi-Square, and grasshopper optimization [1,[22][23][24]. Another research implemented a hybrid algorithm to improve the accuracy and efficiency of IDS [25].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

2024

IJIES

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The evolution of technology and the internet has accelerated the pace of communication and information exchange. Despite technological advancements, the significant weakness lies in the persistent threat of cybercrime, manifesting in various forms like malware, phishing, and ransomware. To solve the cybercrime problems, this research aims to create an intrusion detection system model using a novel framework. In general, the proposed method consists of 3 stages: Data preprocessing, feature selection using ANOVA F-value combined with cross validation, and classification using weight-based voting classifier. Some machine learning methods used in the weight-based voting classifier are random forest, K-nearest neighbour, and logistic regression. The experiment results show that weight order and weight combination affect the detection performance. The proposed method produces an excellent precision value of 98.66%, higher than the single voting classifier.

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“…Traffic is divided into sessions and monitored, and its nature (intrusion or normal traffic) is determined. Therefore, to convert traffic into features, it is divided into a specific session, and then features for the session are created using traffic belonging to the session [7][8][9][10][11][12][13]. A session is a concept that exists in the Transmission Control Protocol (TCP), but the User Datagram Protocol (UDP) and Internet Control Message Protocol (ICMP) also extend the concept of TCP to define sessions.…”

Section: Existing Work 21 Session-based Nidsmentioning

confidence: 99%

High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features

Han

Pak

2023

Electronics

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Currently, most network intrusion detection systems (NIDSs) use information about an entire session to detect intrusion, which has the fatal disadvantage of delaying detection. To solve this problem, studies have been proposed to detect intrusions using only some packets belonging to the session but have limited effectiveness in increasing the detection performance compared to conventional methods. In addition, space complexity is high because all packets used for classification must be stored. Therefore, we propose a novel NIDS that requires low memory storage space and exhibits high detection performance without detection delay. The proposed method does not need to store packets for the current session and uses only some packets, as in conventional methods, but achieves very high detection performance. Through experiments, it was confirmed that the proposed NIDS uses only a small memory of 25.8% on average compared to existing NIDSs by minimizing memory consumption for feature creation, while its intrusion detection performance is equal to or higher than those of existing ones. As a result, this method is expected to significantly help increase network safety by overcoming the disadvantages of machine-learning-based NIDSs using existing sessions and packets.

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Network intrusion detection model based on neural network feature extraction and PSO-SVM

Cited by 8 publications

References 13 publications

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

Analysis of Weight-Based Voting Classifier for Intrusion Detection System

High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features

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