Application Layer Denial of Services Attack Detection Based on StackNet

Smadi, Sami

doi:10.30534/ijatcse/2020/215932020

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…In reference to the second scenario of detection that has been designed to ensure the existence of DoS attack, which is considered as continuing of the primary scenario. All IP addresses that are contained in the suspected list will be examined through the secondary scenario to complete their detection against DoS attacks [25]. These results activate detection even through the operational phase of the network.…”

Section: Security Analysis Of the Proposed Detection Algorithmmentioning

confidence: 99%

“…Over the years, various security mechanisms have been proposed to overcome the DoS attack such as statistical-based approaches, intrusion detection system (IDS) and machine learning (ML) approaches, etc [12], but they still suffer from limitations of detection accuracy, require more learning time to produce accurate results, and increase the false negative rate. To solve this problem, NTDA technique has been employed which will distinguish the legitimate traffic from attack traffic in the sense of the appropriate DoS attack based on the request message counter and mean deviation in the network traffic and then, the detection operations will determine the transmissions rate per second (TPS).…”

Section: Introductionmentioning

confidence: 99%

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NTDA: The Mitigation of Denial of Service (DoS) Cyberattack Based on Network Traffic Detection Approach

Tahboush,

Hamdan,

Alzobi

et al. 2024

IJACSA

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Security is one of the important aspects which is used to protect data availability from being compromised. Denial of service (DoS) attack is a common type of cyberattack and becomes serious security threats to information systems and current computer networks. DoS aims to explicit attempts that will consume and disrupt victim resources to limit access to information services by flooding a target system with a high volume of traffic, thereby preventing the availability of the resources to the legitimate users. However, several solutions were developed to overcome the DoS attack, but still suffer from limitations such as requiring additional hardware, fail to provide a unified solution and incur a high delay of detection accuracy. Therefore, the network traffic detection approach (NTDA) is proposed to detect the DoS attack in a more optimistic manner based on various scenarios. First, the high network traffic measurements and mean deviation, second scenario relied on the transmission rate per second (TPS) of the sender. The proposed algorithm NTDA was simulated using MATLAB R2020a. The performance metrics taken into consideration are false negative rate, accuracy, detection rate and true positive rate. The simulation results show that the performance parameters of proposed NTDA algorithm outperformed in DoS detection the other well-known algorithms.

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Section: Security Analysis Of the Proposed Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NTDA: The Mitigation of Denial of Service (DoS) Cyberattack Based on Network Traffic Detection Approach

Tahboush,

Hamdan,

Alzobi

et al. 2024

IJACSA

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“…• The Area Under Curve (AUC) [23]: ROC is a curve of probability, and AUC is the separability indicator. It demonstrates the possibility of differentiating classes in the proposed model.…”

Section: Evaluation Metricsmentioning

confidence: 99%

VPN Encrypted Traffic classification using XGBoost

2021

IJETER

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Classification network traffic are becoming ever more relevant in understanding and addressing security issues inInternet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms

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“…Precision is a metric that quantifies the number of correct positive predictions made [22]. In imbalanced classification Precision is the ratio between true positive / total predicted positive, as shown in (2).…”

Section: A Precisionmentioning

confidence: 99%

Recognizing Credit Card Fraud Transaction using Spending Behavior-based Transaction Features

Simbolon¹

2020

IJETER

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Flexibility and easiness for making financial transaction has continued to make credit card transaction a popular alternative financial payment especially during Corona-19 Pandemic that hamper almost worldwide. However, recognizing credit card fraudulent transaction is still an open problem despite many methods have been proposed. The main problems, among others, are imbalanced nature of the credit card transaction data and no agreed upon features to represent the transaction. This paper presents a novel approach to address such problem by exploiting card user spending behavior as the main feature to represent the transaction. in our experiment with Random forest we put max features parameter value 18 and 75 as number of trees in Random forest. While our experiment with OCSVM is by using 4 different kernels which are RBF, Polynomial, Linear and Sigmoid, and, we tuned nu and gamma parameter to get the optimal precision, recall and AUC for our Classifier. The experiment results found that One-Class SVM model achieve 0.984 AUC for training and 0.985 AUC for testing; whilst, Random Forest model achieves 0.991 AUC for training and 0.943 AUC for testing.

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Application Layer Denial of Services Attack Detection Based on StackNet

Cited by 9 publications

References 29 publications

NTDA: The Mitigation of Denial of Service (DoS) Cyberattack Based on Network Traffic Detection Approach

NTDA: The Mitigation of Denial of Service (DoS) Cyberattack Based on Network Traffic Detection Approach

VPN Encrypted Traffic classification using XGBoost

Recognizing Credit Card Fraud Transaction using Spending Behavior-based Transaction Features

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