scholarly journals Using Machine Learning to Predict Distributed Denial-of-Service (DDoS) Attack

2021 ◽  
Author(s):  
Qozeem Adeniyi Adeshina ◽  
Baidya Nath Saha
Keyword(s):  
Machine Learning ◽  
Processing Speed ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Normal Network ◽  
Short Time ◽  
Proper Analysis

The IT space is growing in all aspects ranging from bandwidth, storage, processing speed, machine learning and data analysis. This growth has consequently led to more cyber threat and attacks which now requires innovative and predictive security approach that uses cutting-edge technologies in order to fight the menace. The patterns of the cyber threats will be observed so that proper analysis from different sets of data will be used to develop a model that will depend on the available data. Distributed Denial of Service is one of the most common threats and attacks that is ravaging computing devices on the internet. This research talks about the approaches and the development of machine learning classifiers to detect DDoS attacks before it eventually happen. The model is built with seven different selection techniques each using ten machine learning classifiers. The model learns to understand the normal network traffic so that it can detect an ICMP, TCP and UDP DDoS traffic when they arrive. The goal is to build a data-driven, intelligent and decision-making machine learning algorithm model that will use classifiers to categorize normal and DDoS traffic using KDD-99 dataset. Results have shown that some classifiers have very good predictions obtained within a very short time.

scholarly journals Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

2017 ◽  
Vol 9 (1) ◽  
pp. 23-47 ◽  
Author(s):  
Vinai George Biju ◽  
CM Prashanth
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Predictive Modelling ◽  
Wilcoxon Test ◽  
Classification Rate ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multiple Data ◽  
Comparative Review ◽  
Multiple Data Sets

Abstract This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

scholarly journals Comparative Study on TCP SYN Flood DDoS Attack Detection: A Machine Learning Algorithm Based Approach

2021 ◽  
Vol 16 ◽  
pp. 584-591
Author(s):  
S. Sumathi ◽  
R. Rajesh
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Research Goal ◽  
Ddos Attack Detection ◽  
F Measure

A most common attack on the internet network is a Distributed Denial of Service (DDoS) attack, which involves occupying computational resources and bandwidth to suppress services to potential clients. The attack scenario is to massively flood the packets. The attack is called a denial of service (DoS) if the attack originates from a single server, and a distributed denial of service (DDoS) if the attack originates from multiple servers. Control and mitigation of DDoS attacks have been a research goal for many scholars for over a decade, and they have achieved in delivering a few major DDoS detection and protection techniques. In the current state of internet use, how quickly and early a DDoS attack can be detected in broadcasting network transactions remains a key research goal. After the development of a machine learning algorithm, many potential methods of DDoS attack detection have been developed. The work presents the results of various experiments carried out using data mining and machine learning algorithms as well as a combination of these algorithms on the commonly available dataset named CAIDA for TCP SYN flood attack detection. Also, this work analysis the various performance metrics such as false positive rate, precision, recall, F-measure and receiver operating characteristic (ROC) using various machine learning algorithm. One-R(OR) with an ideal FPR value of 0.05 and recall value of 0.95,decision stump(DS) with an ideal precision value of o.93,PART with an excellent F-measure value of 0.91 are some of the performance metric values while performing TCP SYN flood attack detection.

scholarly journals Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

2021 ◽  
Vol 50 (8) ◽  
pp. 2479-2497
Author(s):  
Buvana M. ◽  
Muthumayil K.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Nasal Congestion ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set ◽  
Physiological Measurement ◽  
Machine Learning Classifiers ◽  
Balanced Diet ◽  
Learning Classifiers

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

scholarly journals Decentralized Secure Online Digital Data Registrations

2018 ◽  
Vol 7 (4.6) ◽  
pp. 42 ◽  
Author(s):  
Raman Dugyala ◽  
N Hanuman Reddy ◽  
Raghuram G ◽  
J Lakshminarayana
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Process Models ◽  
Digital Data ◽  
Dos Attacks ◽  
Consensus Algorithms ◽  
Machine Learning Classifiers ◽  
Blockchain Technology ◽  
Learning Classifiers ◽  
Service Sectors

A potential challenge in the present secure transaction lies in illegitimate access to premium digital data available in various formats on various service-sectors for Government and Non-Government process models. This vulnerable scenario leads to identity attacks, man in the middle (MITM) attacks and denial of service (DoS) attacks. Our proposed model uses blockchain technology with machine learning classifiers to identify and detect possible attacks within a peer-to-peer network. Blockchain uses digital signatures, proof-of-work algorithm and consensus algorithms to protect system from possible integrity and denial of service attacks and machine learning classifiers deployed at every node offers confrontation to any intruder’s attack. 

Sign in / Sign up

Export Citation Format

Share Document