Intelligent Botnet Detection Approach in Modern Applications

Alheeti, Khattab M. Ali; Alsukayti, Ibrahim S.; Alreshoodi, Mohammed

doi:10.3991/ijim.v15i16.24199

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…To evaluate and measure system performance, four types of alarms were needed: true positive (TP), false positive (FP), true negative (TN) and false negative (FN) [32].…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study for SDN Security Based on Machine Learning

Alheeti

Alzahrani

Alamri

et al. 2023

Int. J. Interact. Mob. Technol.

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In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.

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“…To evaluate and measure system performance, four types of alarms were needed: true positive (TP), false positive (FP), true negative (TN) and false negative (FN) [32].…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study for SDN Security Based on Machine Learning

Alheeti

Alzahrani

Alamri

et al. 2023

Int. J. Interact. Mob. Technol.

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show abstract

Deep learning model for intrusion detection system utilizing convolution neural network

Kamil,

Mohammed

2023

Open Engineering

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An integral part of any reliable network security infrastructure is the intrusion detection system (IDS). Early attack detection can stop adversaries from further intruding on a network. Machine learning (ML) and deep learning (DL) techniques to automate intrusion threat detection at a scale never previously envisioned have snowballed during the past 10 years. Researchers, software engineers, and network professionals have been encouraged to reconsider the use of ML techniques, notably in cybersecurity. This article proposes a system for detecting intrusion with two approaches, the first utilizing a proposed hybrid convolutional neural network (CNN) and Dense layers. The second utilizes naïve Bayes (NB) ML techniques and compares the two approaches to determine the best detection accuracy. The preprocessing of network data is necessary. The suggested technique is evaluated using the UNSW-NB15 Dataset to create a reliable classifier and an effective IDS. The experimental results for the proposed CNN-dense classifier outperformed the ML and DL models. CNN has a 99.8% accuracy rate compared to previous studies. At the same time, the Gaussian naïve Bayes, which is considered the best among the ML-utilized classifiers, yielded an 83% accuracy rate.

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Intelligent Botnet Detection Approach in Modern Applications

Cited by 2 publications

References 44 publications

A Comparative Study for SDN Security Based on Machine Learning

A Comparative Study for SDN Security Based on Machine Learning

Deep learning model for intrusion detection system utilizing convolution neural network

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