SMS enabled fraud is of great concern globally. Building classifiers based on machine learning for SMS fraud requires the use of suitable datasets for model training and validation. Most research has centred on the use of datasets of SMSs in English. This paper introduces a first dataset for SMS fraud detection in Chichewa, a major language in Africa, and reports on experiments with machine learning algorithms for classifying SMSs in Chichewa as fraud or non-fraud. We answer the broader research question of how feasible it is to develop machine learning classification models for Chichewa SMSs. To do that, we created three datasets. A small dataset of SMS in Chichewa was collected through primary research from a segment of the young population. We applied a label-preserving text transformations to increase its size. The enlarged dataset was translated into English using two approaches: human translation and machine translation. The Chichewa and the translated datasets were subjected to machine classification using random forest and logistic regression. Our findings indicate that both models achieved a promising accuracy of over 96% on the Chichewa dataset. There was a drop in performance when moving from the Chichewa to the translated dataset. This highlights the importance of data preprocessing, especially in multilingual or cross-lingual NLP tasks, and shows the challenges of relying on machine-translated text for training machine learning models. Our results underscore the importance of developing language specific models for SMS fraud detection to optimise accuracy and performance. Since most machine learning models require data preprocessing, it is essential to investigate the impact of the reliance on English-specific tools for data preprocessing.

The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstandard text into its standardized form, the proposed model enhances the efficacy of machine learning classifiers, particularly the Naive Bayesian classifier, in distinguishing smishing messages from legitimate ones. Our experimental results, validated on a publicly available dataset, demonstrate a detection accuracy of 96.2%, with a low False Positive Rate of 3.87% and False Negative Rate of 2.85%. This approach significantly outperforms existing methodologies, providing a robust solution to the increasingly sophisticated threat of smishing in the mobile environment.

This paper explores the rising concern of utilizing Large Language Models (LLMs) in spear phishing message generation, and their performance compared to human-authored counterparts. Our pilot study compares the effectiveness of smishing (SMS phishing) messages created by GPT-4 and human authors, which have been personalized to willing targets. The targets assessed the messages in a modified ranked-order experiment using a novel methodology we call TRAPD (Threshold Ranking Approach for Personalized Deception). Specifically, targets provide personal information (job title and location, hobby, item purchased online), spear smishing messages are created using this information by humans and GPT-4, targets are invited back to rank-order 12 messages from most to least convincing (and identify which they would click on), and then asked questions about why they ranked messages the way they did. They also guess which messages are created by an LLM and their reasoning. Results from 25 targets show that LLM-generated messages are most often perceived as more convincing than those authored by humans, with messages related to jobs being the most convincing. We characterize different criteria used when assessing the authenticity of messages including word choice, style, and personal relevance. Results also show that targets were unable to identify whether the messages was AI-generated or human-authored and struggled to identify criteria to use in order to make this distinction. This study aims to highlight the urgent need for further research and improved countermeasures against personalized AI-enabled social engineering attacks.

In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Na\"ive Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate. The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages.

People tend to keep their smartphones on them 24/7. Sending out a blast to an SMS list about new properties in the area gets the word out quickly and may help clients get an edge over others looking for similar homes. Segment audiences to ensure customers get messages only about listings in their price range and based on their wants and needs. Real estate agents have to do a bit of work when initially adding a client to the database. Still, segmentation prevents situations where clients get numerous SMS messages that don't apply to them.

In part 1, I explained the voicemail capabilities of the RingCentral cloud communications system and AI solutions that can be employed to build an effective virtual voicemail assistant for your telephone customer services. I also showed you how to create and set up a dedicated extension for taking only voicemail messages and the overall workflow of a virtual voicemail assistant. In this article, I will walk through the essential steps to develop a web app -- a demo of virtual voicemail assistant for RingCentral Developers support, which can listen for new voicemail messages and perform the following tasks. The associated demo application is built using the Node JS Express Web application framework. Thus, for conveniences, I will use the Node JS SDKs provided by RingCentral, Monkey Learn, and Rev AI to access their services.

I live in a great city. But like in most cities, finding a parking space here is always frustrating. Spots get snapped up quickly and even if you have a dedicated parking space for yourself, it's hard for friends to drop by since they can't find a place to park. This might sound pretty complicated, but building a working version of this with deep learning is actually pretty quick and easy. All the tools are available -- it is just a matter of knowing where to find the tools and how to put them together.

In part 1, I explained the voicemail capabilities of the RingCentral cloud communications system, and AI (Artificial Intelligence) solutions that can be employed to build an effective virtual voicemail assistant for your telephone customer services. I also showed you how to create and setup a dedicated extension for taking only voicemail messages, and the overall workflow of a virtual voicemail assistant. In this article, I will walk through the essential steps to develop a Web app -- a demo of virtual voicemail assistant for RingCentral Developers support, which can listen for new voicemail messages and perform the following tasks. The associated demo application is built using Node JS, Express Web application framework. Thus, for conveniences, I will use the Node JS SDKs provided by RingCentral, Monkey Learn, and Rev AI to access their services.

It's Friday, August 3, and I have hooked a live one. Using StreamingPhish, a tool that identifies potential phishing sites by mining data on newly registered certificates, I've spotted an Apple phishing site before it's even ready for victims. Conveniently, the operator has even left a Web shell wide open for me to watch him at work. As I download the phishing kit, I take a look at the site access logs from within the shell. Evidently, I've caught the site just a few hours after the certificate was registered.

Many marketers double-down on two or three channels that they know will yield a positive ROI. But ignore other channels, and you risk missing wider audiences. Your customer is everywhere--and often all at once. But the multichannel marketing landscape is getting harder to manage. No wonder, according to Adobe, that only 14% of organizations are running coordinated marketing campaigns across all channels.