A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression
Swathi Sambangi,Lakshmeeswari Gondi +1 more
- Vol. 63, Iss: 1, pp 51
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TLDR
The research objective is to study the problem of DDoS attack detection in a Cloud environment by considering the most popular CICIDS 2017 benchmark dataset and applying multiple regression analysis for building a machine learning model to predict DDoS and Bot attacks through considering a Friday afternoon traffic logfile.Abstract:
The problem of identifying Distributed Denial of Service (DDos) attacks is fundamentally a classification problem in machine learning. In relevance to Cloud Computing, the task of identification of DDoS attacks is a significantly challenging problem because of computational complexity that has to be addressed. Fundamentally, a Denial of Service (DoS) attack is an intentional attack attempted by attackers from single source which has an implicit intention of making an application unavailable to the target stakeholder. For this to be achieved, attackers usually stagger the network bandwidth, halting system resources, thus causing denial of access for legitimate users. Contrary to DoS attacks, in DDoS attacks, the attacker makes use of multiple sources to initiate an attack. DDoS attacks are most common at network, transportation, presentation and application layers of a seven-layer OSI model. In this paper, the research objective is to study the problem of DDoS attack detection in a Cloud environment by considering the most popular CICIDS 2017 benchmark dataset and applying multiple regression analysis for building a machine learning model to predict DDoS and Bot attacks through considering a Friday afternoon traffic logfile.read more
Citations
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Journal ArticleDOI
A Feature Similarity Machine Learning Model for DDoS Attack Detection in Modern Network Environments for Industry 4.0
TL;DR: In this paper , a Gaussian based network traffic similarity function for similarity computation between network traffic instances and a machine learning model SWASTHIKA which uses feature transformation traffic for detection of low rate and high-rate network attacks.
Journal ArticleDOI
Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method
Mona A. Alduailij,Qazi Waqas Khan,Muhammad Zeeshan Tahir,Muhammad Sardaraz,Mai A. Alduailij,Fazila Malik +5 more
TL;DR: This article presents a method for DDoS attack detection in cloud computing by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods, and concludes that the RF performed well in DDoS attacks detection and misclassified only one attack as normal.
Journal ArticleDOI
Predicting DOS-DDOS Attacks: Review and Evaluation Study of Feature Selection Methods based on Wrapper Process
TL;DR: Three important dashboards are presented that are essential to understand the performance of three wrapper strategies commonly used in DOS-DDOS ML systems: heuristic search algorithms, meta-heuristic search and random search methods.
Journal ArticleDOI
Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things
Kuburat Oyeranti Adefemi Alimi,Khmaies Ouahada,Adnan M. Abu-Mahfouz,Suvendi Rimer,Oyeniyi Akeem Alimi +4 more
TL;DR: Experimental results obtained show that the proposed IDS based on redefined long short-term memory deep learning approach can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.
Journal ArticleDOI
Detecting DDoS attacks using adversarial neural network
Ali M. Mustapha,Rida Khatoun,Sherali Zeadally,Fadlallah Chbib,Ahmad Fadlallah,Walid Fahs,Ali El Attar +6 more
TL;DR: Wang et al. as discussed by the authors proposed a DDoS detection method based on the Long Short-Term Memory (LSTM) model, which is a type of Recurrent Neural Networks (RNNs) capable of learning long-term dependencies.
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