Automated DDOS attack detection in software defined networking

Ahuja, Nisha; Singal, Gaurav; Mukhopadhyay, Debajyoti; Kumar, Neeraj

doi:10.1016/j.jnca.2021.103108

Cited by 94 publications

(19 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ahuja et al [45]. compared the performance of Artificial Neural Network (ANN) with various classical ML algorithms for DDoS attacks detection in SDNs.…”

Section: Deep Learning Based Solutionsmentioning

confidence: 99%

“…On the other side, not only the importance of the features varies from one network to another, but the identity of these features can also vary from one environment to another. For example, the flow duration in conventional networks indicates the length of connections in seconds between the source and destination hosts, while the flow duration in SDN networks indicates the time during which the flow entry remains in the switch flow table [45]. Therefore, we can see the duration feature is more specific for DDoS attacks in SDNs.…”

Section: F Feature Selection Processmentioning

confidence: 99%

“…Therefore, we can see the duration feature is more specific for DDoS attacks in SDNs. During the DDoS attacks, the malicious flow spends a long duration in the switch flow table compared to the legitimate traffic [45]. Thus, the flow, which remains active for a larger duration, is a good indicator for malicious DDoS attacks.…”

Section: F Feature Selection Processmentioning

confidence: 99%

See 2 more Smart Citations

A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs

Elsayed

Le-Khac

Azer

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

“…Ahuja et al [45]. compared the performance of Artificial Neural Network (ANN) with various classical ML algorithms for DDoS attacks detection in SDNs.…”

Section: Deep Learning Based Solutionsmentioning

confidence: 99%

Section: F Feature Selection Processmentioning

confidence: 99%

See 1 more Smart Citation

A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs

Elsayed

Le-Khac

Azer

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

View full text Add to dashboard Cite

“…Nisha Ahuja et. Al [8] The Software Defined Networking [SDN] is defined by the software were the traffic is controlled and centralized which direct between hosts. The dataset of SDN are used to trained the model and which can create mininet emulator.…”

Section: G Ancy Sherin Jose Et Al [9]mentioning

confidence: 99%

Review on Detection of DDOS Attack using Machine Learning

Ulemale¹

2022

IJRASET

View full text Add to dashboard Cite

Now days due to digitalization the drastic increment towards Computer technology and Computer network is increased rapidly. Various types of works are turned into Online Mode so due to increment in network connection various attack are been perform. One of the dangerous and powerful attack is Distributed Denial of Service [DDOS] is one of the major issues in the computer network. The Attacker target the server with the help of DDOS and tries to interrupt normal traffic. The Denial-ofservice consist of subclass which includes Distributed denial of service. Therefore, to prevent the DDOS attack many researches have been carried out. To Prevent DDOS common technique which includes Machine learning and deep learning. The main focus is to detect DDOS attack. Keywords: Computer Network, DDOS, Machine Learning, Deep Learning.

show abstract

“…Ahuja et al [52] proposed a hybrid ML model Support Vector Classifier with Random Forest (SVC-RF) for the classification of traffic as BENIGN or DDoS. The authors extracted the number of features from the original dataset and created a new dataset called SDN dataset with novel features.…”

Section: Related Workmentioning

confidence: 99%

Real-Time DDoS Attack Detection System Using Big Data Approach

et al. 2021

View full text Add to dashboard Cite

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

show abstract

Automated DDOS attack detection in software defined networking

Cited by 94 publications

References 21 publications

A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs

A Flow-Based Anomaly Detection Approach With Feature Selection Method Against DDoS Attacks in SDNs

Review on Detection of DDOS Attack using Machine Learning

Real-Time DDoS Attack Detection System Using Big Data Approach

Contact Info

Product

Resources

About