Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques

2021 ◽  
Author(s):  
K.Muthamil Sudar ◽  
M. Beulah ◽  
P. Deepalakshmi ◽  
P. Nagaraj ◽  
P. Chinnasamy
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Learning Techniques

scholarly journals An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

2020 ◽  
Vol 2 (1) ◽  
pp. 18-27
Author(s):  
Arnold Ojugo ◽  
Andrew Okonji Eboka
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Empirical Evaluation ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Secure Systems ◽  
Data Encoding ◽  
Signature Detection ◽  
Learning Techniques

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.

scholarly journals Machine Learning Based Effective Classification of Distributed Denial of Service Attacks

2019 ◽  
Vol 9 (2) ◽  
pp. 1060-1064
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Learning Approach ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Learning Techniques ◽  
Machine Learning Approach

Distributed Denial of Service Attack (DDoS) is a deadliest weapon which overwhelm the server or network by sending flood of packets towards it. The attack disrupts the services running on the target thereby blocking the legitimate traffic accessing its services. Various advanced machine learning techniques have been applied for detection of different types of DDoS attacks but still the attack remains a potential threat to the world. There are mainly two broad categories of machine learning techniques: supervised machine learning approach and unsupervised machine learning approach. Supervised machine learning approach requires labelled attack traffic datasets whereas unsupervised machine learning approach analyses incoming network traffic and then categorizes it. In this paper we have attempted to apply four different classifiers for the detection of DDoS attacks. The four classifiers applied are Logistic Regression, Naïve Bayes, K- Nearest Neighbor and Artificial Neural Network. The chosen classifiers provide stable results when there is a large dataset. We compared their detection accuracy on KDD dataset which is a benchmark dataset in the field of network security. This paper is novel as it explains each pre-processing step with python conversion functions and explained in detail all the classifiers and detection accuracy with their functions in python as well.

scholarly journals A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772110002
Author(s):  
Fahd A Alhaidari ◽  
Alia Mohammed Alrehan
Keyword(s):  
Machine Learning ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Vehicular Ad Hoc Network ◽  
Traffic Density ◽  
Support Vector ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Network Systems

Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

scholarly journals A STUDY ON ADVANCED BOTNETS DETECTION IN VARIOUS COMPUTING SYSTEMS USING MACHINE LEARNING TECHNIQUES

2020 ◽  
pp. 167-173
Author(s):  
K. Vamshi Krishna
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Emerging Technologies ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Network Equipment ◽  
Internet Network

Due to the rapid growth and use of Emerging technologies such as Artificial Intelligence, Machine Learning and Internet of Things, Information industry became so popular, meanwhile these Emerging technologies have brought lot of impact on human lives and internet network equipment has increased. This increment of internet network equipment may bring some serious security issues. A botnet is a number of Internet-connected devices, each of which is running one or more bots.The main aim of botnet is to infect connected devices and use their resource for automated tasks and generally they remain hidden. Botnets can be used to perform Distributed Denial-of-Service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. In this paper we are going to address the advanced Botnet detection techniques using Machine Learning. Traditional botnet detection uses manual analysis and blacklist, and the efficiency is very low. Applying machine learning to batch automatic detection of botnets can greatly improve the efficiency of detection. Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets. According to the difference of detection features, botnet detection based on machine learning technology is divided into network traffic analysis and correlation analysis-based detection technology. KEYWORDS: Botnet, Study, Security, Internet-network, Machine Learning, Techniques.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

2021 ◽  
pp. 248-264
Author(s):  
Rochak Swami ◽  
Mayank Dave ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Generation ◽  
Networking Technology

Distributed denial of service (DDoS) attack is one of the most disastrous attacks that compromises the resources and services of the server. DDoS attack makes the services unavailable for its legitimate users by flooding the network with illegitimate traffic. Most commonly, it targets the bandwidth and resources of the server. This chapter discusses various types of DDoS attacks with their behavior. It describes the state-of-the-art of DDoS attacks. An emerging technology named “Software-defined networking” (SDN) has been developed for new generation networks. It has become a trending way of networking. Due to the centralized networking technology, SDN suffers from DDoS attacks. SDN controller manages the functionality of the complete network. Therefore, it is the most vulnerable target of the attackers to be attacked. This work illustrates how DDoS attacks affect the whole working of SDN. The objective of this chapter is also to provide a better understanding of DDoS attacks and how machine learning approaches may be used for detecting DDoS attacks.

Sign in / Sign up

Export Citation Format

Share Document