DDoS attack detection in software defined networking controller using machine learning techniques

Altamemi, Abbas Jasem; Abdulhassan, Aladdin; Obeis, Nawfal Turki

doi:10.11591/eei.v11i5.4155

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…g. Error rate: it can be defined as the number of all wrong predictions divided by the entire number of the dataset [24], [25]. h. ERR = b+c a+ b+c+d…”

Section: Resultsmentioning

confidence: 99%

Security of private cloud using machine learning and cryptography

Jabbar

Bhaya²

2023

Bulletin EEI

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There are increased security challenges that target cloud systems. One of the most important requirements of users in cloud storage is protecting their cloud from attacks and keeping data secure. Modern technologies of machine learning are providing the ability to analyze and classify data perfectly. This paper proposes a model placed between users and the cloud, which is based on two phases. The first of which is protecting the cloud from different types of network attacks and detecting normal and abnormal flow. The second one is categorizing the users' data and then encrypting it based on its importance using different encryption algorithms. The accuracy results of random forest (RF) and decision tree (DT) are 100% of attack detection for each one. For the second phase of classifying data, the algorithms used are the logistic regression (LR) and stochastic gradient descent (SGD) learning which resulted in 98% accuracy for both. Besides, the encryption algorithms that have been adopted are rivest cipher (RC4), triple data encryption (3DES), and advanced encryption standard (AES) for encryption of the classified data according to the importance which will be then stored in the cloud in its secure form.

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“…g. Error rate: it can be defined as the number of all wrong predictions divided by the entire number of the dataset [24], [25]. h. ERR = b+c a+ b+c+d…”

Section: Resultsmentioning

confidence: 99%

Security of private cloud using machine learning and cryptography

Jabbar

Bhaya²

2023

Bulletin EEI

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“…Moreover, the presence of irrelevant and redundant features can obscure the underlying patterns and hinder accurate defect detection. 7,32 Another challenge is the need to handle interdependencies and interactions among features, as defects may be influenced by complex relationships among software metrics. Automated methods, such as filter, wrapper, or embedded techniques, have been proposed to overcome these limitations.…”

Section: Introductionmentioning

confidence: 99%

“…The challenges in FS for defect detection include the curse of dimensionality, where the number of features is much larger than the number of instances, leading to overfitting and reduced generalization performance. Moreover, the presence of irrelevant and redundant features can obscure the underlying patterns and hinder accurate defect detection 7,32 …”

Section: Introductionmentioning

confidence: 99%

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Sekaran,

Lawrence

2024

Trans Emerging Tel Tech

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Software defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

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“…This shows the predictability nature of DT as a probabilistic classifier. In another study conducted by Altamemi et al [23] aimed to mitigate DDoS occurrence in SDN using machine learning for rapid detection employed algorithms such as Logistic regression, NB and DT and utilised of real-time dataset in the building of the model. This provided an up-to-date and realistic data for DDoS attack detection by the model.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach to Classification of DDoS Attacks on a Computer Network Infrastructure

Effah,

Osei,

Maxwell Dorgbefu Jnr.

et al. 2024

AJRCoS

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The advancement in technology, its ease of use, and the competitive nature of its deployment in business operations have led to the wide spread of networking systems globally, and Ghana is not an exception. Most business operations and even personal activities are now conducted online leading to increased network connectivity, access to networked resources, and the corresponding cyber-attacks on these network systems. Distributed Denial-of-Service (DDoS) is one of the sophisticated attacks in the cyberspace. In DDOs, the attacker floods the network with massive and unsolicited traffic, causing the network infrastructure to exhaust all its resources in responding to the attacker’s request, thereby denying access to legitimate users of such resources. In this study, we designed and implemented a hybrid deep learning model (CRNN-Infusion) for detection and classification of DDoS attacks. Our model utilized the CNN, and RNN models, with the CICDDoS2019 dataset obtained from the Canadian Institute of Cybersecurity (CIC) for its training, with Random Search Hyperparameter Tuning (RSHT) and Feature Selection (FS) techniques for model efficiency and dimensionality reduction. Cybersecurity (CIC) for the model’s training, with Random Search Hyperparameter Tuning (RSHT) and FS techniques for model efficiency and dimensionality reduction. The results showed that, our proposed model is a better classifier for DDoS attacks compared to other deep learning (DL) models trained on the same dataset. With the highest accuracy of 98.92%, hybrid deep learning models are suitable for detecting and classifying DDoS attacks on network infrastructures. The findings point out that, with the appropriate choice of feature selection and hyperparameter tuning techniques, hybrid deep learning models perform optimally, with 98.92% accuracy, 99.02% precision, 98.92% recall, and 98.93% F1 score for our proposed model.

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DDoS attack detection in software defined networking controller using machine learning techniques

Cited by 11 publications

References 23 publications

Security of private cloud using machine learning and cryptography

Security of private cloud using machine learning and cryptography

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

Hybrid Approach to Classification of DDoS Attacks on a Computer Network Infrastructure

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