Installation of Zentyal; LINUX Small Business Server

This project work is about setting up a server for the Electronics and computer Lab of the ECET department. The current server used in this lab runs OpenBSD 3.3. OpenBSD is a Unix-like operating system which offers services such as mail server, web server, ftp server, DNS server, router, firewall, or NFS file server. However this version of openBSD is obsolete and several versions has been developed over the years, the recent version is openBSD 5.6. Furthermore, openBSD has no graphic user interface (GUI). It accepts and processes command through the command line interface. Hence, it can be defined as not being so user friendly. Zentyal is a variant of Linux operating system. It is to be used in this project as a server for the ECET lab. Zentyal offers same functions as OpenBSD and much more. This variant of Linux was chosen because it is free and more user friendly. It has a graphic user interface and also has a well-integrated management tools. It will serve as a gateway between the computers in the lab and the BGSU wireless network and also as a file server for the ECET lab. The Zentyal server hardware also uses a wireless network interface card to connect to the BGSU network, thereby providing more flexibility in connection. The phases of this project involve obtaining the needed hardware and installation of the Zentyal operation system. Putting all the computers of the ECET lab on one network (LAN network) and connecting to the server. The server will serve as a gateway (router) to the internet through the BGSU network. Other functionalities Zentyal operating system offers is going to be explored such as Network address translation (NAT) and Firewall

Spam Detection Using Machine Learning and Deep Learning

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove these spam messages is important. This dissertation explores the process of text classification from data input to embedded representation of the words in vector form and finally the classification process. Therefore, we have applied different embedding methods to capture both the linguistic and semantic meanings of words. Static embedding methods that are used include Word to Vector (Word2Vec) and Global Vectors (GloVe), while for dynamic embedding the transfer learning of the Bidirectional Encoder Representations from Transformers (BERT) was employed. For classification, both machine learning and deep learning techniques were used to build an efficient and sensitive classification model with good accuracy and low false positive rate. Our result established that the combination of BERT for embedding and machine learning for classification produced better classification results than other combinations. With these results, we developed models that combined the self-feature extraction advantage of deep learning and the effective classification of machine learning. These models were tested on four different datasets, namely: SMS Spam dataset, Ling dataset, Spam Assassin dataset and Enron dataset. BERT+SVC (hybrid model) produced the result with highest accuracy and lowest false positive rate

Spam Detection Using Machine Learning

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication. Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate. Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model. Classification methods employed includes SVM (support vector machine), Bayesian, Naïve Bayes, Bayesian with Adaboost, Naïve Bayes with Adaboost. However, for this project, the Bayesian was employed using Python programming language to develop a classification model. Keywords: machine learning (ML), machine learning classifier, Naïve Bayes, SVM, Adaboost, spam classification, ham. DOI: 10.7176/CEIS/11-3-04 Publication date:May 31st 202