Intrusion Detection System Based on RNN Classifier for Feature Reduction

Deore, Bhushan; Bhosale, Surendra

doi:10.1007/s42979-021-00991-0

Cited by 8 publications

(4 citation statements)

References 18 publications

(26 reference statements)

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“…[31][32][33][34]), such as Recurrent Neural Networks (RNN) (ref. [35,36]) and Convolutional Neural Networks (CNN) (ref. [37]), have been developed to enhance traditional detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Ying,

Pan,

Chen

et al. 2024

Mathematics

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The field of network security is highly concerned with intrusion detection, which safeguards the security of computer networks. The invention and application of intrusion detection technology play indispensable roles in network security, and it is crucial to investigate and comprehend this topic. Recently, with the continuous occurrence of intrusion incidents in virus propagation networks, traditional network detection algorithms for virus propagation have encountered limitations and have struggled to detect these incidents effectively and accurately. Therefore, updating the intrusion detection algorithm of the virus-spreading network is imperative. This paper introduces a novel system for virus propagation, whose core is a graph-based neural network. By organically combining two modules—a standardization module and a computation module—this system forms a powerful GNN model. The standardization module uses two methods, while the calculation module uses three methods. Through permutation and combination, we obtain six GNN models with different characteristics. To verify their performance, we conducted experiments on the selected datasets. The experimental results show that the proposed algorithm has excellent capabilities, high accuracy, reasonable complexity, and excellent stability in the intrusion detection of virus-spreading networks, making the network more secure and reliable.

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Section: Related Workmentioning

confidence: 99%

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Ying,

Pan,

Chen

et al. 2024

Mathematics

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“…As in [ 31 ], where the hybrid deep learning model CNN-OLSTM is used to detect DDos attacks and the grey wolf optimization method is present to choose the best features for detection, but it obtains a very low specificity of 51%. In [ 11 ], a feature reduction model based on correlation and information gain, followed by using a RNN classifier for the detection of attacks and non-attacks in a reduced-feature dataset, where 90% of the NSLKDD dataset is used for training. In [ 32 ] suggested that using the whale algorithm to optimize the weights of LSTM networks to develop an effective model is called WILS, the abbreviation for whale integrated long short term memory to detect a variety of threats on IoT networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This makes DL models more adaptable and flexible, as they can handle a wide range of input types and sizes. In the domain of intrusion detection, the most recent papers are using DL [8,[11][12][13]. RNN is one of the most popular deep learning algorithms for the classification of sequential data due to its recurrent (circular) manner of connections.…”

Section: Introductionmentioning

confidence: 99%

An improved long short term memory network for intrusion detection

Awad,

Ali,

Gaber

2023

PLoS ONE

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Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

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“…In (Deore & Bhosale, 2021), a feature selection algorithm has been proposed using a recurrent neural network (RNN). The proposed algorithm combines correlation and information gain for feature reduction.…”

Section: -Related Workmentioning

confidence: 99%

A Hybrid Evolutionary Algorithm for Anomaly Detection in Computer Networks

Rafeh

Najafi

2022

Preprint

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With the rapid expansion of computer networks, their security has become an attractive topic for researchers. Intrusion detection is known as one of the main techniques for retaining security in computer networks. Intrusion detection systems are divided into two categories: misuse detection and anomaly detection. Misuse detection techniques work based on the known attack patterns and have high detection rates. However, they are unable to detect new attacks. Anomaly detection techniques can detect new attacks, but they suffer from a high false alarm rate. An anomaly detection system often uses many features some of which may have no impact on detection. Some features may even adversely affect the detection rate. Feature selection can significantly improve the performance of an intrusion detection system. The main task of a feature selector is finding an optimal subset of features to improve the prediction accuracy. In this paper, a new hybrid evolutionary algorithm based on the imperialist competitive and genetic algorithm is proposed to select the optimal features for an anomaly detection system. The algorithm has been evaluated using the KDD99 dataset. The experimental results show that the proposed algorithm has a high detection rate and a low false alarm rate and outperforms the existing similar algorithms in terms of prediction accuracy. In addition, the proposed algorithm converges very fast and can quickly find optimal features which can make it suitable for being used in lightweight intrusion detection systems.

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Intrusion Detection System Based on RNN Classifier for Feature Reduction

Cited by 8 publications

References 18 publications

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network

An improved long short term memory network for intrusion detection

A Hybrid Evolutionary Algorithm for Anomaly Detection in Computer Networks

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