scholarly journals Hybrid Spam Filtration Method using Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 1818-1821
Keyword(s):  
Electronic Mail ◽  
Online Communication ◽  
True Positive Rate ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Filtration Method ◽  
Learning Techniques ◽  
Positive Rate ◽  
Bayes Algorithm ◽  
E Mail

Electronic mail (e-mail) is one of the most prevalent approaches for online communication and transferring data through web because of its quick and easy distribution of data, low distribution cost and permanency. Despite these benefits there are certain weaknesses of e-mail. Among these, spam also known as junk e-mail tops. Spam is set of unwanted or inappropriate messages sent over the internet to a massive amount of users for the purpose of marketing, phishing, disseminating malware, etc.With the internet becoming the dominant platform anti-spam solutions are of great use today. This paper illustrates an efficient hybrid spam filtration method using Naïve Bayes algorithm and Markov Random Field technique, which detects and filters spam messages. The proposed method is evaluated based on its accurateness, meticulousness and time consumption. The results confirm that the proposed hybrid method achieves high percentage of true positive rate in finding e-mail spam messages.

scholarly journals Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1467 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Manufacturing Systems ◽  
True Positive Rate ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Control Approach ◽  
Learning Techniques ◽  
Positive Rate ◽  
Smart Manufacturing Systems

Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

A Novel Approach for Computer-Aided Diagnosis for Distinction Between Benign and Malignant of Lung Nodules Based on Machine Learning Techniques

2018 ◽  
pp. 281-290
Author(s):  
Shashidhara Bola
Keyword(s):  
False Positive Rate ◽  
Lung Nodule ◽  
Machine Learning Techniques ◽  
Lung Nodules ◽  
Data Set ◽  
Novel Approach ◽  
Learning Techniques ◽  
Nodule Shape ◽  
Positive Rate ◽  
Aided Diagnosis

A new method is proposed to classify the lung nodules as benign and malignant. The method is based on analysis of lung nodule shape, contour, and texture for better classification. The data set consists of 39 lung nodules of 39 patients which contain 19 benign and 20 malignant nodules. Lung regions are segmented based on morphological operators and lung nodules are detected based on shape and area features. The proposed algorithm was tested on LIDC (lung image database consortium) datasets and the results were found to be satisfactory. The performance of the method for distinction between benign and malignant was evaluated by the use of receiver operating characteristic (ROC) analysis. The method achieved area under the ROC curve was 0.903 which reduces the false positive rate.

Electronic Mail Security

2011 ◽  
pp. 147-160 ◽  
Author(s):  
Edwin I. Achugbue
Keyword(s):  
Electronic Mail ◽  
The Internet ◽  
Security Threat ◽  
The Future ◽  
History Of ◽  
The Individual ◽  
Mail Address ◽  
E Mail

The chapter focuses on the history of the internet system of e-mail; e-mail security; threat to e-mail security, usefulness of e-mail address and country codes, how e-mails can be secured by the individual and electronic mail policy. The future of e-mail security is also described.

Future of Small Business E-Commerce

2011 ◽  
pp. 2159-2163 ◽  
Author(s):  
Simpson Poon
Keyword(s):  
Small Business ◽  
Small Businesses ◽  
Electronic Mail ◽  
The Internet ◽  
File Transfer Protocol ◽  
File Transfer ◽  
Research Findings ◽  
Use Of The Internet ◽  
In The Beginning ◽  
E Mail

The use of the Internet for business purposes among small businesses started quite early in the e-commerce evolution. In the beginning, innovative and entrepreneurial owners of small businesses attempted to use rudimentary Internet tools such as electronic mail (e-mail) and file transfer protocol (FTP) to exchange messages and documents. While primitive, it fulfilled much of the business needs at the time. Even to date, e-mail and document exchange, according to some of the latest research findings, are still the most commonly used tools despite the fact that tools themselves have become more sophisticated.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2509 ◽  
Author(s):  
Kamran Shaukat ◽  
Suhuai Luo ◽  
Vijay Varadharajan ◽  
Ibrahim A. Hameed ◽  
Shan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Modern World ◽  
The Internet ◽  
Cyber Threats ◽  
Risk Detection ◽  
Learning Techniques ◽  
Made In

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Adapting Cooperative Learning Strategies to Facilitate Ethics Discus sions Using E-Mail

1995 ◽  
Vol 58 (4) ◽  
pp. 3-6 ◽  
Author(s):  
George S. Peek ◽  
Maria L. Roxas ◽  
Lucia E. Peek
Keyword(s):  
Cooperative Learning ◽  
Learning Strategies ◽  
Ethical Dilemma ◽  
Structured Learning ◽  
The Internet ◽  
Learning Techniques ◽  
Team Environment ◽  
Intellectual Skills ◽  
E Mail ◽  
Current Business

This paper reports on a project in which students discussed an ethical dilemma using Internet e-mail as the medium of communication. The assignment requires both informal and formal document development in a team environment and uses cooperative learning strategies to assure full participa tion by all students. Students thus have the advantage of discussing an important current business issue, are facili tated in this discussion by the use of structured learning techniques, are required to exercise their thinking and writ ing skills in a variety of ways, and in many cases must acquire new technical and intellectual skills for communi cation across the Internet.

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