Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system

Ahsan, Muhammad; Mashuri, Muhammad; Lee, Muhammad Hisyam; Kuswanto, Heri; Prastyo, Dedy Dwi

doi:10.1016/j.eswa.2019.113105

Cited by 35 publications

(18 citation statements)

References 52 publications

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“…Even though binary and multi-class classification were done in this study, the studies compared according to multi-class accuracies, since not all studies were contained both classifications. [34] MCD-KDE 91.71 Ieracitano et al [35] AE50 87 Garg et al [36] SVM 87.56 Dong et al [37] MCA-LSTM 82.15 Dey et al [38] GRU-LSTM 87.91 Li et al [39] GINI-GBDT-PSO 86.10 Wu et al [40] CNN 79.48 Le et al [41] LSTM 92 Gogoi et al [42] TUIDS 96.55 Tang et al [43] DNN 91.7 Yin et al [44] RNN 83.28 Tang et al [45] RNN 89 Proposed Technique V-IDS 97.52…”

Section: Binary Training Resultsmentioning

confidence: 99%

A novel approach for intrusion detection systems: V-IDS

Ince¹

2021

Turk J Elec Eng & Comp Sci

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Intrusion Detection System (IDS) is a security mechanism that detects abnormal activities in a network. An ideal IDS must detect intrusion attempts and maybe categorize for further researches and keep false positive analysis at a very low level. IDSs are used in the analysis of network traffic data at all sizes. Studies on this subject focused on machine learning (ML) techniques, even though the performance rates are high, it is seen that processes such as data understanding, preprocessing and consistency tests are time consuming and laborious. For this reason, the use of deep learning (DL) models that automatically perform the mentioned steps has become very popular. In this study, a high-performance approach that can be applied in real time system is proposed which is named V-IDS (Visual IDS). NSL-KDD data set which is one of the large-scale datasets, is used. Data visualization (DV) techniques were applied in order to determine geometric relationships between records and the data were classified by using DL model. The model achieved 98 percentage accuracy in total and even higher in some intrusion categories.

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Section: Binary Training Resultsmentioning

confidence: 99%

A novel approach for intrusion detection systems: V-IDS

Ince¹

2021

Turk J Elec Eng & Comp Sci

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“…In [53] put forward a novel IDS method that utilized the multivariate control chart dependent on the quick Minimum Covariance Determinant (Fast-MCD) calculation for enhancing the capacities of the presented system to rapidly and precisely recognize the exceptions, and Kernel Density Estimation (KDE).…”

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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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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“…In [56], the researchers proposed new IDS approach that employed the multivariate control chart based on the fast minimum covariance determinant (MCD) algorithm to improve the capabilities of the proposed control chart to quickly and accurately detect the outliers, and kernel density estimation (KDE) to adaptively follow the network traffic data pattern, to reduce the occurrence of false alarms.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons

et al. 2020

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One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods. INDEX TERMS Intrusion Detection System (IDS); Multilayer Perceptron (MLP); Metaheuristic Algorithm (MA); Artificial Bee Colony Algorithm (ABC); Dragonfly algorithm (DA)

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Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system

Cited by 35 publications

References 52 publications

A novel approach for intrusion detection systems: V-IDS

A novel approach for intrusion detection systems: V-IDS

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

An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons

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