Book ChapterDOI
DDOS Detection Using Machine Learning Technique
Sagar Pande,Aditya Khamparia,Deepak Gupta,Dang N. H. Thanh +3 more
- pp 59-68
TLDR
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.Abstract:
Numerous attacks are performed on network infrastructures. These include attacks on network availability, confidentiality and integrity. Distributed denial-of-service (DDoS) attack is a persistent attack which affects the availability of the network. Command and Control (C & C) mechanism is used to perform such kind of attack. Various researchers have proposed different methods based on machine learning technique to detect these attacks. In this paper, DDoS attack was performed using ping of death technique and detected using machine learning technique by using WEKA tool. NSL-KDD dataset was used in this experiment. Random forest algorithm was used to perform classification of the normal and attack samples. 99.76% of the samples were correctly classified.read more
Citations
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Journal ArticleDOI
Intelligent Techniques for Detecting Network Attacks: Review and Research Directions.
Malak Saleh Aljabri,Sumayh S. Aljameel,Rami Mustafa A. Mohammad,Sultan H. Almotiri,Samiha Mirza,Fatima M. Anis,Menna Aboulnour,Dorieh M. Alomari,Dina H. Alhamed,Hanan S. Altamimi +9 more
TL;DR: In this paper, the authors evaluate contemporary intelligent-based research directions to address the gap that still exists in the field and provide a rich source of references for scholars seeking to determine their scope of research in this field.
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
Intrusion Detection System on IoT with 5G Network Using Deep Learning
TL;DR: A smart intrusion detection system suited to detect Internet of Things-based attacks is implemented and the autoencoder model, which effectively reduces detection time as well as effectively improves detection precision, has outperformed.
Journal ArticleDOI
A Time-Efficient Approach Toward DDoS Attack Detection in IoT Network Using SDN
TL;DR: In this article , the authors proposed a novel SDN-based secure IoT framework that can detect the vulnerabilities in IoT devices or malicious traffic generated by IoT devices using the session IP counter and IP Payload analysis.
Journal ArticleDOI
Attack Detection in IoT using Machine Learning
TL;DR: A framework is recommended for the detection of malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy.
References
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Journal ArticleDOI
A Deep Learning Approach to Network Intrusion Detection
TL;DR: This paper presents a novel deep learning technique for intrusion detection, which addresses concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks and details the proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning.
Journal ArticleDOI
Semi-supervised machine learning approach for DDoS detection
TL;DR: This paper presents an online sequential semi-supervised ML approach for DDoS detection based on network Entropy estimation, Co-clustering, Information Gain Ratio and Exra-Trees algorithm which allows to reduce the irrelevant normal traffic data for D attacks detection, reduce false positive rates and increase accuracy.
Journal ArticleDOI
DDoS detection and defense mechanism based on cognitive-inspired computing in SDN
TL;DR: This mechanism can realize real-time detection and defense at the preliminary stage of the DDoS attack and can restore normal communication in time and can take appropriate defense and recovery measures in the time after the attack has been identified.
Book ChapterDOI
Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments
Olivier Brun,Olivier Brun,Yonghua Yin,Erol Gelenbe,Y. Murat Kadioglu,Javier Augusto-Gonzalez,Manuel Ramos +6 more
TL;DR: Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.
Journal ArticleDOI
Deep Radial Intelligence with Cumulative Incarnation approach for detecting Denial of Service attacks
TL;DR: Deep Radial Intelligence (DeeRaI) with Cumulative Incarnation (CuI) approach is proposed to detect the DoS attacks and it is evident that the proposed approach converges faster and provides best weights compared to the existing optimization methods.
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