Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT

Narayanasami, Satheesh; Sengan, Sudhakar; Khurram, Saira; Arslan, Farrukh; Murugaiyan, Suresh Kumar; Regin, R.; Vijayakumar, P.; Dubey, Anil Kumar; Srinivasan, Sujatha; Sharma, Dilip Kumar

doi:10.1007/s11277-021-08721-8

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Data cleaning, reduction, integration, and transformation are commonly used data preprocessing techniques. Dimension reduction, numerosity reduction, and data compression are the methods used during the data reduction process [11,12]. Feature selection and principal component analysis are two commonly used data reduction and dimension reduction techniques.…”

Section: Needs For Feature Selectionmentioning

confidence: 99%

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Ozkan-Okay,

Samet,

Aslan

et al. 2023

Applied Sciences

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The fast development of communication technologies and computer systems brings several challenges from a security point of view. The increasing number of IoT devices as well as other computing devices make network communications more challenging. The number, sophistication, and severity of network-related attacks are growing rapidly. There are a variety of different attacks including remote-to-user (R2L), user-to-remote (U2R), denial of service (DoS), distributed DDoS, and probing. Firewalls, antivirus scanners, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) are widely used to prevent and stop cyber-related attacks. Especially, IDPSs are used to stop and prevent intrusions on communication networks. However, traditional IDSs are no longer effective in detecting complicated cyber attacks from normal network traffic. Because of this, new promising techniques, which specifically utilize data mining, machine learning, and deep learning, need to be proposed in order to distinguish intrusions from normal network traffic. To effectively recognize intrusions, the feature generation, feature selection, and learning processes must be performed delicately before the classification stage. In this study, a new feature selection method called FSAP (Feature Selection Approach) is proposed. In addition, a hybrid attack detection model called SABADT (Signature- and Anomaly-Based Attack Detection Technique) is suggested, which utilizes different classification metrics to recognize attacks. The proposed general method FSACM (Feature Selection and Attack Classification Method) is tested on KDD ’99, UNSW-NB15, and CIC-IDS2017 datasets. According to the experiment results, the proposed method outperformed the state-of-the-art methods in the literature in terms of detection, accuracy, and false-alarm rates.

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Section: Needs For Feature Selectionmentioning

confidence: 99%

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

Ozkan-Okay,

Samet,

Aslan

et al. 2023

Applied Sciences

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show abstract

“…As a result, generic-type attacks in the UNSW-NB15 dataset were detected with an accuracy rate of 80.1%. Narayanasami et al [29] developed an IDS using a hybrid method consisting of SVM and BAT methods. The KDDCup99 dataset was preferred to assess the effectiveness of the proposed IDS.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid PCA-MAO Based LSTM Model for Intrusion Detection in IoT Environments

Karamollaoglu

Yücedağ

Doğru

2022

Preprint

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With the rapid advances in Internet of Things (IoT) technologies, the number of smart objects connected to IoT networks is increasing day by day. Parallel to this exponential growth, attacks against IoT networks are also increasing rapidly. Various Intrusion Detection Systems (IDS) have been proposed by researchers to improve accuracy in detecting attacks with different behaviors and reduce intrusion detection time. This work presents a novel IDS based on the combination of the Principal Component Analysis and Mayfly Optimization methods (PCA-MAO) for dimensionality reduction, the Borderline Synthetic Minority Oversampling Technique (BSMOTE) for data balancing, and the Long Short-Term Memory (LSTM) method for classification. A new dataset was created by combining IoTID20, CIC-ToN-IoT and USB-IDS-1 datasets to be used in the performance test of the proposed model. Thus, the performance evaluation of the proposed model was performed for more attack types with different behaviors. As a result of classification using the proposed hybrid PCA-MAO based LSTM model, an accuracy of 99.51% was obtained. It has been observed that the proposed IDS provides superior intrusion detection performance for high-dimensional, complicated, and imbalanced data compared to classical machine learning (ML) methods.

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“…However, it suffers from premature convergence and requires computational time in most cases [49,50]. Unlike PSO, BA is good at controlling the search space's exploration and exploitation, and it takes less time to compute [51,52]. In this paper, an attempt is made to improve PSO by using BA to select the most relevant subset of features for improving the performance of breast cancer's classification model.…”

Section: Patients Detection Phase (Pdp)mentioning

confidence: 99%

Insight into breast cancer detection: new hybrid feature selection method

Shaban¹

2022

Neural Comput & Applic

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Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

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Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT

Cited by 11 publications

References 16 publications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

A Novel Feature Selection Approach to Classify Intrusion Attacks in Network Communications

A Hybrid PCA-MAO Based LSTM Model for Intrusion Detection in IoT Environments

Insight into breast cancer detection: new hybrid feature selection method

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