Can Machine Learning Techniques Be Effectively Used in Real Networks against DDoS Attacks?

2018 ◽  
Author(s):  
Jarrod N. Bakker ◽  
Bryan Ng ◽  
Winston K. G. Seah
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
Learning Techniques

Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach

2020 ◽  
pp. 439-446
Author(s):  
Datla Anurag Varma ◽  
Ravi Ashish ◽  
V. Venkata Sai Sandeep ◽  
B. Venkatesh ◽  
R. Kannadasan
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
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 Detection, Defensive and Mitigation of DDoS Attacks through Machine learning Techniques: A Literature

2019 ◽  
Vol 8 (4) ◽  
pp. 2719-2725
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
Systematic Analysis ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Different Types ◽  
Mitigation Techniques

iNowaday ithe iworld iis icompletely idepend on ithe iinternet iand the day ito iday iactivities of ihuman ilife icompletely idepends ion ithe iinternet. The idependency ion ithe iinternet iallows ithe iattackers ido damage ior iharm ito ithe ilegitimate iuser’s itransactions iand ievents iwhich iis icalled ias iSecurity iattack. Distributed iDenial iof iService iis ione itype iof ithe imost vulnerable iattacks iof itoday’s icyber iworld. iIn ithis ipaper, iwe ipresent ia isurvey iof iDistributed iDenial iof iService iattack, idetection, idefensive iand imitigation iof imachine ilearning iapproaches. This isurvey iarticle iprefer itwo ifamous isupervised imachine ilearning ialgorithms isnamely. (i) Decision itrees, (ii)isupport ivector imachine and ipresented ithe irecent iresearch iworks icarried iout. From ithis isurvey iit iis ilearnt ithat iconnecting supervised imachine ilearning ialgorithm iwith iboosting iprocess will iincrease iprediction iefficiency iand ithere iis ia iwide iscope iin ithis iresearch ielement. We provide a systematic analysis of these attacks including so many motivations and evolutions, different types of attacks analysis so far, detection techniques and mitigation techniques, possible constraints and challenges of existing approaches. iFinally isome iimportant iresearch ipoints are outlined ito iensure isuccessful idetection, idefensive iand mitigation iagainst iDistributed iDenial iof iService attacks

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.

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.

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