Increasing the Performance of an IDS using ANN model on the realistic cyber dataset CSE-CIC-IDS2018

Ayachi, Yassine; Mellah, Youssef; Berrich, Jamal; Bouchentouf, Toumi

doi:10.1109/isaect50560.2020.9523662

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…The basic concept is to leverage the initial SMOTE to build fresh synthetic minority samples to achieve a balanced distribution 9 . Tomek links will be utilized to remove samples near the boundary between the two classes to enhance the separation between the two classes.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Further, traditional IDS are primarily based on patterns or signatures and use apps to see APT attacks 8 . Machine learning‐based technology is one way to improve APT attack detection rate based on the network behavior anomaly detection (NBAD) 9 . The approaches for identifying APT attacks are based on various characteristics researched so far and are listed below.…”

Section: Related Workmentioning

confidence: 99%

“…Yassine et al 9 devised an experimental approach using the CSE‐CIC‐IDS2018 dataset and artificial neural networks (ANN). According to experimental results, the proposed technique enhances overall accuracy considerably.…”

Section: Related Workmentioning

confidence: 99%

“…The basic concept is to leverage the initial SMOTE to build fresh synthetic minority samples to achieve a balanced distribution. 9 Tomek links will be utilized to remove samples near the boundary between the two classes to enhance the separation between the two classes. Combining SMOTE with several under-sampling strategies was discovered to be successful and may be classified as a new sampling approach called SMOTETomek.…”

Section: Preprocessingmentioning

confidence: 99%

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A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

Saini,

Bhat Kasaragod,

Prakasha

et al. 2023

Concurrency and Computation

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SummaryA persistent, targeted cyber attack is called an advanced persistent threat (APT) attack. The attack is mainly launched to gain sensitive information, take over the system, and for financial gain, which creates nowadays more hurdles and challenges for the organization in preventing, detecting, and recovering from such attacks. Due to the nature of APT attacks, it is difficult to detect them quickly. Therefore machine learning techniques come into these research areas. This study uses deep and machine learning models such as random forest, decision tree, convolutional neural network, multilayer perceptron and so forth to categorize and effectively detect APT attacks by utilizing publicly accessible datasets. The datasets used in this study are CSE‐CIC‐IDS2018, CIC‐IDS2017, NSL‐KDD, and UNSW‐NB15. This study proposes the hybrid ensemble machine learning model, a mixed approach of random forest and XGBoost classifiers. It has obtained the maximum prediction accuracy of 98.92%, 99.91%, 99.24%, and 97.11% for datasets CSE‐CIC‐IDS2018, CIC‐IDS2017, NSL‐KDD, and UNSW‐NB15, with a false positive rate of 0.52%, 0.12%, 0.62%, and 5.29% respectively. These results are compared to other closely related recent studies in the literature. Our experiment's findings show that our model has performed significantly better for all datasets.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

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A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

Saini,

Bhat Kasaragod,

Prakasha

et al. 2023

Concurrency and Computation

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“…The intelligent Intrusion Detection System described in [20] dramatically enhanced detection performance using an Artificial Neural Network (ANN) model. The ANN-based classifier showed 100% sensitivity on the test dataset with high precision in minimizing false positives.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

Najafi Mohsenabad,

Tut

2024

Applied Sciences

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In computer network security, the escalating use of computer networks and the corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) to the forefront of research in computer science. IDSs are a crucial security technology that diligently monitor network traffic and host activities to identify unauthorized or malicious behavior. This study develops highly accurate models for detecting a diverse range of cyberattacks using the fewest possible features, achieved via a meticulous selection of features. We chose 5, 9, and 10 features, respectively, using the Artificial Bee Colony (ABC), Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO) feature-selection techniques. We successfully constructed different models with a remarkable detection accuracy of over 98.8% (approximately 99.0%) with Ant Colony Optimization (ACO), an accuracy of 98.7% with the Flower Pollination Algorithm (FPA), and an accuracy of 98.6% with the Artificial Bee Colony (ABC). Another achievement of this study is the minimum model building time achieved in intrusion detection, which was equal to 1 s using the Flower Pollination Algorithm (FPA), 2 s using the Artificial Bee Colony (ABC), and 3 s using Ant Colony Optimization (ACO). Our research leverages the comprehensive and up-to-date CSE-CIC-IDS2018 dataset and uses the preprocessing Discretize technique to discretize data. Furthermore, our research provides valuable recommendations to network administrators, aiding them in selecting appropriate machine learning algorithms tailored to specific requirements.

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Towards a Webshell Detection Approach Using Rule-Based and Deep HTTP Traffic Analysis

Nguyen

et al. 2022

Computational Collective Intelligence

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Increasing the Performance of an IDS using ANN model on the realistic cyber dataset CSE-CIC-IDS2018

Cited by 6 publications

References 15 publications

A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

A hybrid ensemble machine learning model for detecting APT attacks based on network behavior anomaly detection

Optimizing Cybersecurity Attack Detection in Computer Networks: A Comparative Analysis of Bio-Inspired Optimization Algorithms Using the CSE-CIC-IDS 2018 Dataset

Towards a Webshell Detection Approach Using Rule-Based and Deep HTTP Traffic Analysis

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