Deep learning to filter SMS spam

Improving the performance of machine learning algorithms has been always the topic of interest in data mining. The ensemble learning is one of the machine learning methods that, according to the subject literature, it yields better performance than the single base learner in the accuracy parameter. In the ensemble learning, all base learners are considered at the same level in terms of power and separation capabilities. However, whether the ensemble is made of homogeneous based learners or it is made of heterogeneous base learners, in either case, weaknesses and strengths of the base learners are ignored. To overcome this challenge, the stronger coefficient of influence should be assigned to stronger base learners and the lower coefficient of influence should be assigned to weaker base learners. However, given that the data is associated with uncertainty in the real-world issues, it is impossible to determine which base learner performs better than the others under these circumstances. Learning Automata is one of the desirable options of reinforcement learning subject literature to dealing with dynamic environments. The learning automata works by receiving feedback from the environment. In this paper, a method named LAbEL has been proposed which allows the assignment of the coefficient of influence to each base learner in the ensemble dynamically. Due to the use of learning automata, the proposed method works adjusted to the problem space conditions. The LAbEL is based on learning automata and according to its ability to dealing with dynamic environments, it is possible to apply it to issues where data has nonlinear and unpredictable behavior.

A new ensemble learning method based on learning automata

Spam and non-spam email identification are one of the most challenging tasks for both email service providers and consumers. The spammers try to spread misleading facts through irritating messages by attracting user's attention. Several spam identification-models have previously been proposed and tested but the recorded accuracy has shown that further work in this direction is needed to achieve improved accuracy, low training time, and less error rate. In this research work, we have proposed a model that classifies the e-mail into spam and ham. DBSCAN and Isolation Forest are used to identify the extreme values outside of the specific range. Heatmap, Recursive Feature Elimination, and Chi-Square feature selection techniques are used to select the effective features. The proposed model is implemented in both machine learning and deep learning to establish a comparative analysis. Multinomial Naive Bayes (MNB), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB) are used to introduce ensemble method in machine learning implementation. Recurrent Neural Network (RNN), Gradient Descent (GD), Artificial Neural Network (ANN) for deep learning implementation. An ensemble method is constructed to combine multiple classifiers' output. The ensemble methods allow producing better prediction accuracy compared to a single classifier. Our proposed model obtained an accuracy of 100%, AUC=100, MSE error = 0 and RMSE error = 0 for machine learning implementation and accuracy of 99%, loss value= 0.0165 for deep learning implementation based on an email spam base dataset collected from the UCI machine learning repository.

Analysis of Optimized Machine Learning and Deep Learning Techniques for Spam Detection

Text mining, a section of the synthetic intelligence, is gaining grounds nowadays in terms of the applications in business and analysis. Varied sectors and domains across industries understand the potential of text mining in gaining information, mining helpful data and in enhancing the choice creating method in terms of speed and potency. In today's world, where the web area is overflowing with data, text mining technology and solutions will persuade be the turning purpose. Most of the recent researches conducted during this space focused principally on advanced or the hybrid of deep neural networks so as to induce economical and higher results. Functioning on such serious models isn't solely time taking however conjointly needs the usage of heap of resources. Therefore, to get the comparable results only basic deep learning models have been used in order to minimize the model's complexity and computational cost. For the same, RNN and LSTM based models have been used and accuracy of proposed models have been enhanced by by varying the hyper parameters and using Glove word embeddings. Moreover, a labeled dataset named Sentiment5k has also been created using semi-supervised learning approach for evaluating the performance of RNN & LSTM based models.

Enhancing Text Mining Using Deep Learning Models

Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

In our daily life, we use email and SMS many times to communicate to each other, but due to the increase of spam email and SMS, it becomes a headache for both the sender and receiver. We need spam detection tool to detect the spam, and there are many spam detection tools available in the market but they are not up to the mark because they only emphasize on individual classifier or only one or two combination of classifier. In our research, we present different combinations of four different classifiers, namely “Gaussian Naive Bayes”, “Multinomial Naive Bayes”, “Bernoulli Naive Bayes”, and “Decision Tree”. We have used voting classifier, a type of ensemble learning to calculate the accuracy of different combinations of classifiers. Results show that use of voting classifier produces more accurate prediction than individual classifier. We had also created an android application to serve the purpose. The mobile application works on client–server principle. Basically, the mobile application acts as a client which sends the data clicked by a user from mobile to server. At the server, there is machine learning script which classifies the received data and sends the prediction back to the client.

Spam Detection Using Ensemble Learning

Introduced as a free and open-source network analysis framework and designed to function as a network security monitor (NSM), Zeek can also be used as a network intrusion detection system (NIDS) when combined with additional live network events analysis. But it lacks some features that should have come by default built-in. So to fill this gap, some features have been added to it, so that Zeek IDS becomes more useful to system administrators. This tool is being used by Fortune 500 companies, universities, and governments, and it helps them detect malicious activities. So they can prevent them from suffering a major loss that could have been caused by malicious activities. In this paper, we have troubleshot and resolved some of the problems with the Zeek network monitoring tool. Zeek's logging capability is enhanced by adding the City and Country name of IPs to the logs, filtering the noise of a particular domain, filtering the noise of a particular port, and separating Local and Remote Connection Logs into separate files.

Refinements In Zeek Intrusion Detection System

Intrusion detection/prevention systems have attracted much interest in recent years due to increased online connectivity. In recent years due to COVID pandemic and due to the increased number of online users, online data has become more and more exposed to different types of attacks. Hence, in order to keep data safe, it has become quite important to detect/prevent such attacks. An IDS is a sensor that is used for the observation of such attacks on the nodes or the network itself, and in this way, it tries to keep the information safe from possible attacks. However, accurately identifying such attacks so that they can be prevented effectively is a concern. This accuracy is measured by the number of false positive & false negative in a dataset. These days ML/DL algorithms are being significantly utilized for improving the accuracy of different systems (e.g., health care, stock market, forecasting etc.). Considering its importance, the work presented here studies the impact of using ML/DL algorithms on the accuracy of IDS/IPS. The impact of these algorithms is studied by using evaluation metrics for classification of network assaults in the intrusion detection system using different datasets. These algorithms are subject to further changes for improving the accuracy parameters based on evaluation metrics.

Utilizing ML and DL Algorithms for Alert Classification in Intrusion Detection and Prevention Systems: A Detailed Review

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

Utkarsh Dixit

Papers

Spam Detection Using Ensemble Learning

Refinements In Zeek Intrusion Detection System

Utilizing ML and DL Algorithms for Alert Classification in Intrusion Detection and Prevention Systems: A Detailed Review

Comparison of Different Machine Learning Algorithms Based on Intrusion Detection System

Analysis of Automatic Text Classification of Legal Documents