Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Alhenawi, Esra’a; Alazzam, Hadeel; Al-Sayyed, Rizik; AbuAlghanam, Orieb; Adwan, Omar

doi:10.2478/cait-2022-0040

Cited by 5 publications

(2 citation statements)

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“…This study employed a one-dimensional convolutional neural network as a classification model. The classification results in a CNN-based model are directly influenced by the number of convolution kernels and the learning rate [30][31][32][33]. We conducted experiments on multiple convolution kernels with different learning rates to obtain the optimal set of parameters.…”

Section: Classificationmentioning

confidence: 99%

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Deep Learning-based DDoS Detection in Network Traffic Data

Hussein Hadi

2024

Int. j. electr. comput. eng. syst. (Online)

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In today's society, the cloud is essential for communication since it allows access to important information anytime and anywhere. However, cloud services also attract hackers who want to exploit online details. This has caused significant changes in the cyber-attack landscape. Distributed Denial of Service (DDoS) is the most common attack. Traditional tools like firewalls and encryption can mitigate these risks, but new models are needed to cope with the changing nature of cyber-attacks. Detecting DDoS attacks is particularly challenging since network traffic data is complex and often contains unnecessary features. To address this, a new approach is proposed using Denoising AutoEncoder (DAE) and a Convolutional Neural Network (CNN) for feature selection and classification. The NSL-KDD dataset is used to evaluate the performance of this new model with three main steps: Data Pre- processing, Hyper-parameter Optimization, and Classification. Our method performed better in all four metrics, such as Accuracy, Recall, Precision, and F1-score, with rates of 97.7, 98.1, 97.7, and 97.8, respectively. The multiclass classification detection rate for DOS was 100%. Similarly, the detection rates for Probe, R2L, and U2R were 98%, 95%, and 80%, respectively. Python version 3.6 with Keras 2.2.4 and TensorFlow Engine was used in this paper.

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Section: Classificationmentioning

confidence: 99%

“…The classification metrics used in this paper are Accuracy, Precision, Recall, and F1-score. The calculations for each metric are given by equations 2, 3, 4, and 5, respectively [28][29][30][31][32][33].…”

Section: Classificationmentioning

confidence: 99%

Deep Learning-based DDoS Detection in Network Traffic Data

Hussein Hadi

2024

Int. j. electr. comput. eng. syst. (Online)

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User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Jurišić,

Tomičić,

Grd

2023

Cybernetics and Information Technologies

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The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.

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Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

Allu,

Padmanabhuni

2023

Cybernetics and Information Technologies

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A startup is a recently established business venture led by entrepreneurs, to create and offer new products or services. The discovery of promising startups is a challenging task for creditors, policymakers, and investors. Therefore, the startup survival rate prediction is required to be developed for the success/failure of startup companies. In this paper, the feature selection using the Convex Least Angle Regression Least Absolute Shrinkage and Selection Operator (CLAR-LASSO) is proposed to improve the classification of startup survival rate prediction. The Swish Activation Function based Long Short-Term Memory (SAFLSTM) is developed for classifying the survival rate of startups. Further, the Local Interpretable Model-agnostic Explanations (LIME) model interprets the predicted classification to the user. Existing research such as Hyper Parameter Tuning (HPT)-Logistic regression, HPT-Support Vector Machine (SVM), HPT-XGBoost, and SAFLSTM are used to compare the CLAR-LASSO. The accuracy of the CLAR-LASSO is 95.67% which is high when compared to the HPT-Logistic regression, HPT-SVM, HPT-XGBoost, and SAFLSTM.

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Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Cited by 5 publications

References 50 publications

Deep Learning-based DDoS Detection in Network Traffic Data

Deep Learning-based DDoS Detection in Network Traffic Data

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Convex Least Angle Regression Based LASSO Feature Selection and Swish Activation Function Model for Startup Survival Rate

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