scholarly journals Prediction of Denial of Service Attack using Machine Learning Algorithms

2020 ◽  
Vol 9 (5) ◽  
pp. 1601-1606
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Graphical Representation ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Ddos Attack ◽  
Time Accuracy

DDoS attack is one of the significant security threats in today’s Internet world. The main intention of the network thread is to make the resource unavailable such as flooding attacks. Here, Machine learning algorithms have been used for detecting DDoS attacks. Generally, the success of any algorithm has depended on the selection of appropriate data sets and the identification of attack parameters. The KDD-CUP dataset has been taken for a detail investigation of the DDoS attack. The K-nearest neighbor, ID3, Naive Bayes and C4.5 algorithms are compared in a single platform concluding with the positives with Naive Bayes. The main objective of the paper is to compare and predict the error rate, computation time, Accuracy of the algorithms using the Tanagra tool. Finally, these correlative algorithms have been compared and verified through experimental verification and graphical representation.

scholarly journals Using Machine Learning to Build a Classification Model for IoT Networks to Detect Attack Signatures

2020 ◽  
Vol 12 (6) ◽  
pp. 99-116
Author(s):  
Mousa Al-Akhras ◽  
Mohammed Alawairdhi ◽  
Ali Alkoudari ◽  
Samer Atawneh
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Security And Privacy ◽  
K Nearest Neighbors ◽  
Detection Model

Internet of things (IoT) has led to several security threats and challenges within society. Regardless of the benefits that it has brought with it to the society, IoT could compromise the security and privacy of individuals and companies at various levels. Denial of Service (DoS) and Distributed DoS (DDoS) attacks, among others, are the most common attack types that face the IoT networks. To counter such attacks, companies should implement an efficient classification/detection model, which is not an easy task. This paper proposes a classification model to examine the effectiveness of several machine-learning algorithms, namely, Random Forest (RF), k-Nearest Neighbors (KNN), and Naïve Bayes. The machine learning algorithms are used to detect attacks on the UNSW-NB15 benchmark dataset. The UNSW-NB15 contains normal network traffic and malicious traffic instants. The experimental results reveal that RF and KNN classifiers give the best performance with an accuracy of 100% (without noise injection) and 99% (with 10% noise filtering), while the Naïve Bayes classifier gives the worst performance with an accuracy of 95.35% and 82.77 without noise and with 10% noise, respectively. Other evaluation matrices, such as precision and recall, also show the effectiveness of RF and KNN classifiers over Naïve Bayes.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing

2020 ◽  
Vol 17 (8) ◽  
pp. 3765-3769
Author(s):  
N. P. Ponnuviji ◽  
M. Vigilson Prem
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Distributed Denial Of Service ◽  
Detection Techniques ◽  
Network Bandwidth ◽  
Ddos Attack

Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threats and vulnerabilities in the protocols legacy in the form of bugs that pave way for intrusion in different ways by the attackers. The most common among attacks is the Distributed Denial of Service (DDoS) attack. This attack targets the cloud’s performance and cause serious damage to the entire cloud computing environment. In the DDoS attack scenario, the compromised computers are targeted. The attacks are done by transmitting a large number of packets injected with known and unknown bugs to a server. A huge portion of the network bandwidth of the users’ cloud infrastructure is affected by consuming enormous time of their servers. In this paper, we have proposed a DDoS Attack detection scheme based on Random Forest algorithm to mitigate the DDoS threat. This algorithm is used along with the signature detection techniques and generates a decision tree. This helps in the detection of signature attacks for the DDoS flooding attacks. We have also used other machine learning algorithms and analyzed based on the yielded results.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals Ammonoid Taxonomy with Supervised and Unsupervised Machine Learning Algorithms

2021 ◽  
Author(s):  
Floe Foxon
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Clustering Algorithms ◽  
Measurement Data ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Unsupervised Machine Learning

Ammonoid identification is crucial to biostratigraphy, systematic palaeontology, and evolutionary biology, but may prove difficult when shell features and sutures are poorly preserved. This necessitates novel approaches to ammonoid taxonomy. This study aimed to taxonomize ammonoids by their conch geometry using supervised and unsupervised machine learning algorithms. Ammonoid measurement data (conch diameter, whorl height, whorl width, and umbilical width) were taken from the Paleobiology Database (PBDB). 11 species with ≥50 specimens each were identified providing N=781 total unique specimens. Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbours, and Support Vector Machine classifiers were applied to the PBDB data with a 5x5 nested cross-validation approach to obtain unbiased generalization performance estimates across a grid search of algorithm parameters. All supervised classifiers achieved ≥70% accuracy in identifying ammonoid species, with Naive Bayes demonstrating the least over-fitting. The unsupervised clustering algorithms K-Means, DBSCAN, OPTICS, Mean Shift, and Affinity Propagation achieved Normalized Mutual Information scores of ≥0.6, with the centroid-based methods having most success. This presents a reasonably-accurate proof-of-concept approach to ammonoid classification which may assist identification in cases where more traditional methods are not feasible.

scholarly journals Classification of potential blood donors using machine learning algorithms approach

2020 ◽  
Vol 8 (3) ◽  
pp. 217-221
Author(s):  
Merinda Lestandy ◽  
Lailis Syafa'ah ◽  
Amrul Faruq
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Donor ◽  
Blood Donors ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Blood Type ◽  
The Neural Network

Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.

scholarly journals Data Driven Approach for Eye Disease Classification with Machine Learning

2019 ◽  
Vol 9 (14) ◽  
pp. 2789 ◽  
Author(s):  
Sadaf Malik ◽  
Nadia Kanwal ◽  
Mamoona Naveed Asghar ◽  
Mohammad Ali A. Sadiq ◽  
Irfan Karamat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Multiple Features ◽  
Standard Format ◽  
Free Data

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

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