Book ChapterDOI
DDAM: Detecting DDoS Attacks Using Machine Learning Approach
K. Narasimha Mallikarjunan,A. Bhuvaneshwaran,K. Sundarakantham,S. Mercy Shalinie +3 more
- pp 261-273
TLDR
The experimental results on the real-time dataset confirm that the proposed machine learning approach can effectively detect network anomalies with high detection rate and low false positive rate.Abstract:
Dealing the Distributed Denial of Service (DDoS) attack is a continuing challenge in the field of network security. An Intrusion Detection System (IDS) is one of the solutions to detect the DDoS attack. The IDS system should always be updated with the attack disincentive to preserve the network security service. In this paper, we propose a new approach for anomaly detection using machine learning to secure the network and to determine the attack patterns. The major contribution is to create real-time dataset and to use the naive Bayes algorithm as a classifier for detecting and comparing its performance with the existing classifiers like random forest and J48 algorithm. The experimental results on the real-time dataset confirm that the proposed machine learning approach can effectively detect network anomalies with high detection rate and low false positive rate.read more
Citations
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Journal ArticleDOI
The hybrid technique for DDoS detection with supervised learning algorithms
Soodeh Hosseini,Mehrdad Azizi +1 more
TL;DR: A novel hybrid framework based on data stream approach for detecting DDoS attack with incremental learning is proposed and the naive Bayes, random forest, decision tree, multilayer perceptron (MLP), and k-nearest neighbors (K-NN) on the proxy side to make better results.
Book ChapterDOI
DDOS Detection Using Machine Learning Technique
TL;DR: DDoS attack was performed using ping of death technique and detected using machine learning technique by using WEKA tool and 99.76% of the samples were correctly classified.
Journal ArticleDOI
Feature selection and comparison of classification algorithms for wireless sensor networks
TL;DR: A novel technique for feature selection is introduced, which combines five feature selection techniques as a stack, and best accuracy of 99.87% is achieved with the XGBoost classifier after selecting the best eleven features from the KDD dataset.
Journal ArticleDOI
Performance evaluation of Convolutional Neural Network for web security
TL;DR: It is revealed that an adequate tuning of hyper-parameters and the way of pre-processing data input have a significant impact on the attack detection rate.
Journal ArticleDOI
Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks
TL;DR: In this paper, a new model based on random forest and synthetic minority over-sampling technique (RF-SMOTE) was proposed to detect the attacks in an IoT network.
References
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Machine Learning-based Intrusion Detection Algorithms
Hua Tang,Zhuolin Cao +1 more
TL;DR: A new approach to detect network attacks, which aims to study the efficiency of the method based on machine learning in intrusion detection, including artificial neural networks and support vector machine, is proposed.
Proceedings ArticleDOI
A Naive Bayes Approach for Detecting Coordinated Attacks
TL;DR: This paper proposes a Naive Bayes approach to alert correlation that takes advantage of available historical data, and provides efficient algorithms for detecting and predicting most plausible scenarios.
Proceedings ArticleDOI
Performance Analysis of Data Mining Approaches in Intrusion Detection
P. Amudha,H. Abdul Rauf +1 more
TL;DR: Evaluating the performance of data mining classification algorithms namely J48, Naive Bayes, NBTree and Random Forest using KDD CUP'99 dataset and focuses on Correlation Feature Selection (CFS) measure shows that NBTrees outperforms other two algorithms in terms of predictive accuracy and detection rate.
Anomaly Detection using Fuzzy Q-learning Algorithm
TL;DR: An intrusion detection system called Fuzzy Q- learning (FQL) algorithm to protect wireless nodes within the network and target nodes from DDoS attacks to identify the attack patterns and take appropriate countermeasures.
On the kdd'99 dataset: statistical analysis for feature selection
TL;DR: This work presents a contribution to the network intrusion detection process using Adaptive Resonance Theory (ART1), a type of Artifi- cial Neural Networks (ANN) with binary input unsupervised training.