In recent years, Cyber attacks have increased in number, and with them, the intensity of the attacks and their potential to damage the user have also increased significantly. In an ever-advancing world, users find it difficult to keep up with the latest developments in technology, which can leave them vulnerable to attacks. To avoid such situations we need tools to deter such attacks, for this machine learning models are among the best options. This paper presents a Browser Extension that uses machine learning models to enhance online security by integrating three crucial functionalities: Malicious URL detection, Spam Email detection and Network logs analysis. The proposed solution uses LGBM classifier for classification of Phishing websites, the model has been trained on a dataset with 87 features, this model achieved an accuracy of 96.5% with a precision of 96.8% and F1 score of 96.49%. The Model for Spam email detection uses Multinomial NB algorithm which has been trained on a dataset with over 5500 messages, this model achieved an accuracy of 97.09% with a precision of 100%. The results demonstrate the effectiveness of using machine learning models for cyber security.

In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns of collecting SMS data, and the need for adversary-resistant detection models. SpamDam comprises four innovative modules: an SMS spam radar that identifies spam messages from online social networks(OSNs); an SMS spam inspector for statistical analysis; SMS spam detectors(SSDs) that enable both central training and federated learning; and an SSD analyzer that evaluates model resistance against adversaries in realistic scenarios. Leveraging SpamDam, we have compiled over 76K SMS spam messages from Twitter and Weibo between 2018 and 2023, forming the largest dataset of its kind. This dataset has enabled new insights into recent spam campaigns and the training of high-performing binary and multi-label classifiers for spam detection. Furthermore, effectiveness of federated learning has been well demonstrated to enable privacy-preserving SMS spam detection. Additionally, we have rigorously tested the adversarial robustness of SMS spam detection models, introducing the novel reverse backdoor attack, which has shown effectiveness and stealthiness in practical tests.

ML-Pack is a small footprint C++ machine learning library that can be easily integrated into other programs. Learn how to use it for spam classification.

Wikipedia tells us that Machine learning is, "a field of computer science that gives computers the ability to learn without being explicitly programmed." It goes on to say, "machine learning explores the study and construction of algorithms that can learn from and make predictions on data -- such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs." What does it mean to learn from inputs without being explicitly programmed? Let us consider a classical machine learning problem: spam filtering. Imagine that we know nothing about machine learning, but are tasked with determining whether an email consists of spam or not.

Last time we discussed where Artificial Neural Networks (ANN) come from and basic concepts behind Multilayer Perceptrons (MLP). This time we use such a MLP for the spam classification problem of issue no. 3. This was good enough for our baseline model, since we just filtered on specific words, numbers and currency symbols. However, real-world machine learning algorithms only understand numbers and thus we need to transform the words into numbers, a task which falls into an area called natural language processing (NLP) which is a huge research area by itself. A very common approach is bag-of-words, where the vocabulary of the problem domain is represented as a sparse vector, where each element of the vector represents a single word and the value is either the number of occurrences in a certain document or just a binary value indicating that the word occurred one or several times, which is also called one-hot-encoding.