Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
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A new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood), and this work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naive Bayes and Random Forest.Abstract:
Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusion-detection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system should always be updated with the latest intruder attack deterrents to preserve the confidentiality, integrity and availability of the service. In this paper, a new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood). This work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naive Bayes and Random Forest. The experimental results show that MLP achieved the highest accuracy rate (98.63%).read more
Citations
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A Survey of Network-based Intrusion Detection Data Sets
TL;DR: In this article, the authors provide a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet-and flow-based network data in detail, identifying 15 different properties to assess the suitability of individual data sets.
Journal ArticleDOI
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Hongyu Liu,Bo Lang +1 more
TL;DR: A taxonomy of IDS is proposed that takes data objects as the main dimension to classify and summarize machine learning- based and deep learning-based IDS literature, and believes that this type of taxonomy framework is fit for cyber security researchers.
Journal ArticleDOI
Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset
TL;DR: Six machine-learning-based IDS are proposed by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms to increase the efficiency of the system depending on attack types and decrease missed intrusions and false alarms.
Journal ArticleDOI
An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks
Kshira Sagar Sahoo,Bata Krishna Tripathy,Kshirasagar Naik,Somula Ramasubbareddy,Balamurugan Balusamy,Manju Khari,Daniel Burgos +6 more
TL;DR: The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization, and can be embedded within the controller to define security rules to prevent possible attacks by the attackers.
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