An empirical model in intrusion detection systems using principal component analysis and deep learning models

Rajadurai, Hariharan; Gandhi, Usha Devi

doi:10.1111/coin.12342

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…FS method is viewed as perhaps the most essential technique which is applied in network security, especially in IDS. IDS is needed to manage tremendous volume of data that are likely made up of random, repetitive, and redundant features [32]. The main explanation behind the slowdown of the training and testing process can be because of logical inconsistency in features data, which brings about expanded resource consumption, just as in the decline of the execution of classification precision, and subsequently, rate of detection becomes low [33].…”

Section: Related Workmentioning

confidence: 99%

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Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

et al. 2022

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The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapperapproach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques. INDEX TERMSIntrusion detection system (IDS), bat algorithm (BAT), metaheuristic algorithm (MA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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Section: Related Workmentioning

confidence: 99%

“…In [32] developed a hybrid model for network IDS that includes a combined approach Principle Component Analysis and Deep Learning (PCA DL) to improve attack detection. By evaluating the method using KDD-CUP 99 dataset it has achieved a 92% accuracy in detecting the attacks.…”

Section: Related Workmentioning

confidence: 99%

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

et al. 2022

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“…Furthermore, the IoT-ID20 dataset is used for training purposes [ 45 ]. Another effective threat classification scheme is presented [ 46 ], where authors mainly target the less frequently occurring suspicious events in industrial networks. They have observed the real-life scenario for a sustainable period and have organized a customized dataset.…”

Section: Related Workmentioning

confidence: 99%

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Attique

Wang

2022

Sensors

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The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature.

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“…The practical results imply that this strategy can increase malware detection and classifier classification performance. In [17], H. Rajadurai and U. D. Gandhi,2021, The Principle Component Analysis and Deep Learning (PCA-DL) methodology are presented in this paper as a hybrid technique. The suggested PCA-DL technique has a 92.6 percent accuracy rate in accurately detecting malicious.…”

Section: Related Workmentioning

confidence: 99%

Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers

kadom

Hashem

Jafer

2022

J. Phys.: Conf. Ser.

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Ransomware has become one of the major threats to private organizations, state and local organizations in the last three years, especially since the WannaCry malware. Ransomware is a child in the malware family that hijacks users’ data and related resources using security measures such as cryptographic and then demands bitcoin in compensation for the data that has been encrypted. Network Intrusion Detection Systems (NIDS) are now needed to protect data from hackers and keep information systems and networks from getting hurt as much as possible. It examines and predicts user behavior, which is then classified as an assault or expected behavior. This study will present a suggestion to improve hybrid (anomaly-misuse) NIDS by experimenting with three different Nave Bayes (NB) classifiers: Gaussian Naive Bayes (GNB), Multinomial Naive Bayes (MNB), and Complement Naive Bayes (CNB). To improve accuracy and reduce the computational time in NIDS, Principal Component Analysis (PCA) will be exploited. The UNSW-NB15 dataset was used to evaluate the suggested system’s performance, and the outcomes of the experiments are as follows: when using all features of UNSW-NB15 Datasets the Accuracy of GNB, MNB and CNB is 97%, 92%, and %94, when using PCA feature extraction with 10 PC the Accuracy of GNB, MNB and CNB is 100%, 88% and %100, when using PCA feature extraction with 20 PC the Accuracy of GNB, MNB and CNB is 100%, 92% and %100. When using PCA feature extraction with 30 PC, GNB, MNB, and CNB is 100%, 99%, and %100. According to the findings, 30 PCs improved the accuracy of the three algorithms and gave the best results, meaning that the increase in the number of PC leads to a rise in the system’s accuracy.

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An empirical model in intrusion detection systems using principal component analysis and deep learning models

Cited by 11 publications

References 23 publications

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Optimize network intrusion detection system based on PCA feature extraction and three naïve bayes classifiers

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