Detecting Distributed Denial of Service Attacks using Machine Learning Models

Alghoson, Ebtihal Sameer; Abbass, Onytra

doi:10.14569/ijacsa.2021.0121277

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…These metrics support the model analysis and reflect the specific machine learning algorithms' attack detection quality. The metrics mentioned are defined as where TP, TN, FP, and FN are True Positive, True Negative, False Positive, and False Negative [25] [20]:…”

Section: Performance Evaluation and Results Discussionmentioning

confidence: 99%

“…In [25], the DT model for feature ranking was applied on CICDDOS2019 resulting in a list of the top 30 features, in addition to the use of the person correlation coefficient (PCC) approach resulting in a list of 20 features. They tested the selected features on different ML models, including RF, Light Gradient Boosting, Cat Boost, and CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This phase is vital to transfer the data into a format that could be used in the training and testing phase without affecting its essence or performance [25]. It is conducted because most of the data represent different types and formats, making it nearly impossible to handle, making it an important step to standardize the data before using it.…”

Section: Normalizationmentioning

confidence: 99%

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Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Naiem

Khedr

Idrees

et al. 2023

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

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Distributed Denial of Service (DDOS) attacks aim to exploit the capacity and performance of a network's infrastructure, making the cloud environment one of the biggest targets for attackers. Many efforts are being made in the field of technology to prevent them from disrupting the services provided. Machine Learning techniques are a means to protect against DDOS attacks. Data preprocessing, feature selection, and classifiers are the main components of any prevention framework. The focus of this study is to find and enhance the feature selection approach for increasing the accuracy of the classifiers in detecting DDOS attacks from regular traffic. We used four different techniques, including Pearson Correlation Coefficient (PCC), Random Forest Feature Importance (RFFI), Mutual information (MI), and Chi-squared(X2) measure which we tested on different classifiers. The first selection approach was based on the feature’s independency level then the second iteration was based on the feature’s importance. We also examined the claim of dropping attacks from the dataset for better accuracy. The best performing set of features was from using PCC and RFFI together for feature selection with average accuracy and precision of 99.27% and 97.60%, which is higher than the use of PCC for both measures by almost 2%. The accuracy is also higher by nearly 12% from the same approach dropping 50% of the attacks.

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Section: Performance Evaluation and Results Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Normalizationmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Naiem

Khedr

Idrees

et al. 2023

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

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“…Like XGBoost, catBoost uses regularization techniques to minimize overfitting. Although CatBoost is used for categorical features, it works seamlessly with numerical features and can be a good choice for building models using heterogeneous data [44]. Histogram gradient boosting machine (HGBM) is a similar to other gradient boosting algorithms.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Distributed denial of service attacks classification system using features selection and ensemble techniques

Bagdadi,

Messabih

2024

IJEECS

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Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.

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“…To generate DDoS Detection Models, Alghoson et al introduced the Light Gradient Boosting framework [43] which is a framework for gradient boosting that is small, quick, dispersed, and effective. It has the potential to be utilized for a range of machine learning applications, which comprises classification and ranking.…”

Section: Comparative Review Based On Frameworkmentioning

confidence: 99%

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

Zerin Hasan Sahosh,

Azraf Faheem,

Marzana Bintay Tuba

et al. 2024

Malaysian J. Sci. Adv. Tech.

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The rapid growth of the internet and the increasing reliance on digital infrastructures have posed significant challenges to cybersecurity. Among the other variants of attacks, Distributed Denial of Service (DDoS) attacks have emerged as one of the most destructive and common threats. These attacks disrupt or slow down network services by overwhelming the network infrastructure with a massive volume of malicious traffic. To effectively identify and mitigate DDoS attacks, machine learning techniques have been extensively employed in intrusion detection systems. Machine learning approaches offer the advantage of automating the detection process by learning patterns and characteristics of DDoS attacks from historical data. Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to classify and detect DDoS attacks. These algorithms leverage features extracted from network traffic data, including packet size, packet delay patterns, and traffic behaviour, to differentiate between normal and malicious traffic.

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Detecting Distributed Denial of Service Attacks using Machine Learning Models

Cited by 8 publications

References 15 publications

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Distributed denial of service attacks classification system using features selection and ensemble techniques

A Comparative Review on DDoS Attack Detection Using Machine Learning Techniques

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