FastTrafficAnalyzer: An Efficient Method for Intrusion Detection Systems to Analyze Network Traffic

Arslan, Recep Sinan

doi:10.24012/dumf.1001881

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…With a 64.43% decrease in processing time per sample, the Random Forest method produced a 98.5% identification rate in the multiclass detection scenario. These results indicate similar categorization rates to previous research, albeit with much shorter test durations [28].…”

Section: Related Worksupporting

confidence: 90%

“…Network Intrusion Detection Systems rely on efficient algorithms and methods for realtime assault detection [28]. One study proposed a preprocessing technique that significantly reduced traffic analysis time and achieved high success rates.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset includes continual characteristics such as labels, timestamps, destination ports, and protocols. It includes six scenarios and fourteen types of assaults, such as botnet, denial of service (DoS), infiltration, Web attack, and DDoS [28,52].…”

Section: Datasetmentioning

confidence: 99%

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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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Section: Related Worksupporting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

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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 [16] the results for multiclass classification were quite similar for the dataset before and after reprocessing. The accuracy was 94% for logistic regression (LR) before reprocessing and 98% after reprocessing, and 99% for decision tree (DT) after and before processing the dataset.…”

Section: Related Workmentioning

confidence: 77%

Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

Maabreh

Obeidat

Elsoud³

et al. 2022

Int. J. Interact. Mob. Technol.

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Cyberattacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. In this study, we evaluate and compare four features selection methods, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of CSE-CIC-IDS2018 big data set for network intrusions. The dataset was preprocessed and cleaned and all learning algorithms were trained on the original values of features. The feature selection methods highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type; benign or attack. Furthermore, the results revealed that whether models are trained on all features or the top 30 features selected by any of the four features selection techniques used in this experiment, there is no significant difference in model performance. Moreover, we may be able to train ML models on only four features and have them perform similarly to models trained on all data,which may result in preferable models in terms of complexity, explainability, and scale for deployment. Furthermore, by choosing four unanimity features instead of all traffic features, training time may be reduced from 10% to 50% of the training time on all features.

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“…In a study carried out by Wei et al [7], they tested the methods of Automatic Encoder and Independent RNN with One-Dimensional CNN (1DCAE-IndRNN) on the CIC And Mal 2017 dataset, achieving a detection success rate of 98%. Arslan [8] performed a research study in which preprocessing data and machine learning techniques were applied to the CSE CIC-IDS2018 dataset, reaching a performance of 99.5% using the ExtraTree method and 98.5% using the Random Forest method. In a study [9], Atay et al preferred to make use of various methods such as LGBM, CNN, LGBM+Random Forest, CNN+Random Forest, and Random Forest+Random Forest on the CSE CIC-IDS2018 dataset.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based Network Intrusion Detection with Hybrid Frequent Item Set Mining

Firat,

Bakal,

Akbaş

2024

Politeknik Dergisi

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With the development and expansion of computer networks day by day and the diversity of software developed, the damage that possible attacks can cause is increasing beyond the predictions. Intrusion Detection Systems (STS/IDS) are one of the effective defense tools against these potential attacks that are constantly increasing and diversifying. The ultimate goal is to train these systems with various artificial intelligence methods, to detect subsequent attacks in real time and to take the necessary precautions. In this study, classical feature selection methods and Frequent Item Set Mining were used in feature selection in a hybrid model, and it was aimed to classify network traffic data for normal and attack by using many machine learning methods, including Logistic Regression, with the final features obtained. The method uses a data set originally containing 85 features to make a decision while making this classification. These attributes are extracted using CICFlowMeter from a PCAP file where network traffic is recorded. The results show that the proposed method in the study classifies more than 225000 records in the data set with a success rate of 97.68%.

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FastTrafficAnalyzer: An Efficient Method for Intrusion Detection Systems to Analyze Network Traffic

Cited by 5 publications

References 22 publications

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

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

Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

Machine Learning based Network Intrusion Detection with Hybrid Frequent Item Set Mining

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