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.

DDoS Attacks and Defense Mechanisms Using Machine Learning Techniques for SDN

2020 ◽  
pp. 193-214 ◽  
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 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 Application Layer DDoS Attack Defense Methods and A new Defense Mechanism against Flooding

2020 ◽  
Vol 8 (6) ◽  
pp. 208-212
Keyword(s):  
Machine Learning ◽  
Defense Mechanisms ◽  
Defense Mechanism ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Network Environment ◽  
Ddos Attacks ◽  
Application Layer ◽  
New Approach ◽  
Ddos Attack

In a network environment, Distributed Denial of Service (DDoS) attacks eemploys a network or server is unavailable to its normal users. Application-layer Distributed Denial of Service (App-DDoS) attacks are serious issues for the webserver itself. The multitude and variety of such attacks and defense approaches are overwhelming. This paper here follows, we analyze the different defense mechanisms for application-layer DDoS attacks and proposes a new approach to defend using machine learning.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

scholarly journals Machine learning for metagenomics: methods and tools

Metagenomics ◽  
2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Hayssam Soueidan ◽  
Macha Nikolski
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Full Range ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Unified Framework ◽  
Comparative Metagenomics ◽  
Learning Techniques ◽  
Ngs Data ◽  
Modern Machine

AbstractOwing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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