Hybrid Spam Filtering for Mobile Communication

Spam messages are an increasing threat to mobile communication. Several mitigation techniques have been proposed, including white and black listing, challenge-response and content-based filtering. However, none are perfect and it makes sense to use a combination rather than just one. We propose an anti-spam framework based on the hybrid of content-based filtering and challenge-response. There is the trade-offs between accuracy of anti-spam classifiers and the communication overhead. Experimental results show how, depending on the proportion of spam messages, different filtering %%@ parameters should be set.Comment: 6 pages, 5 figures, 1 tabl

One Protocol to Rule Them All? On Securing Interoperable Messaging

European lawmakers have ruled that users on different platforms should be able to exchange messages with each other. Yet messaging interoperability opens up a Pandora's box of security and privacy challenges. While championed not just as an anti-trust measure but as a means of providing a better experience for the end user, interoperability runs the risk of making the user experience worse if poorly executed. There are two fundamental questions: how to enable the actual message exchange, and how to handle the numerous residual challenges arising from encrypted messages passing from one service provider to another -- including but certainly not limited to content moderation, user authentication, key management, and metadata sharing between providers. In this work, we identify specific open questions and challenges around interoperable communication in end-to-end encrypted messaging, and present high-level suggestions for tackling these challenges

From Understanding Telephone Scams to Implementing Authenticated Caller ID Transmission

abstract: The telephone network is used by almost every person in the modern world. With the rise of Internet access to the PSTN, the telephone network today is rife with telephone spam and scams. Spam calls are significant annoyances for telephone users, unlike email spam, spam calls demand immediate attention. They are not only significant annoyances but also result in significant financial losses in the economy. According to complaint data from the FTC, complaints on illegal calls have made record numbers in recent years. Americans lose billions to fraud due to malicious telephone communication, despite various efforts to subdue telephone spam, scam, and robocalls. In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Cultural aspects of multi-channel customer management: A UK case study

Channel management is one CRM systems component much influenced by the behaviour of customers in relation to its implementation and use. The consumers’ behaviours, preferences, perceptions and expectations are crucial for the implementation and use of channel management. Customers’ contact with the organization’s multi-channels can occur at several touch points throughout the customer lifecycle. Customers’ behaviours may be differentiated according to the individual or micro level, but it might also differ at an ecological or macro level of analysis (Ramaseshan et al., 2006). In this paper the authors have conducted a case study in the UK to analyze customers’ behaviours at a macro level and customers channel choices, through out the customer lifecycle. The authors have used a Structurational Analysis model (Ali and Brooks, 2008) to identify the cultural factors (Ali, et al. 2008a) that influence multi-channel customer management in the UK

Final Tanzania mixed methods evaluation report

e-PactThis final mixed methods report forms part of the external impact evaluation of mNutrition in Tanzania. The evaluation was conducted by a consortium of researchers from Gamos, the Institute of Development Studies (IDS) and the International Food Policy Research Institute (IFPRI) between 2016 and 2020. Mnutrition was a global initiative supported by DFID, organised by GSMA, and implemented by in-country mobile network operators (MNOs) and other providers to use mobile technology to improve the health and nutritional status of children and adults in low-income countries. In Tanzania, mNutrition was implemented through the ‘Healthy Pregnancy, Healthy Baby’ (HPHB) SMS (text messaging) programme, which is part of the Wazazi Nipendeni mHealth platform. The programme was run by the mHealth Tanzania-PPP initiated by the Ministry of Health and Social Welfare, with financial support from the US Government’s Centers for Disease Control and Prevention (CDC). Wazazi Nipendeni is targeted at pregnant women and mothers of young children, as well as their partners (husbands, etc.). It is available nationally on all phone networks. The HPHB SMS service sends free text messages in Swahili on a range of pregnancy and early childhood issues. Nutrition was a small component of the original HPHB SMS service but was extended substantially with the addition of the mNutrition content. This report summarises the final findings of the mNutrition evaluation in Tanzania drawn from across the three interlinked evaluation components (quantitative, qualitative and business model) and structured around the key overarching evaluation questions. For more details on the technical and methodological aspects of the evaluation please refer to the separate methods-specific technical reports

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

SMS, one of the most popular and fast-growing GSM value-added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F-measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine-tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state-of-the-art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F-measure.publishedVersio